Just this week: Everyone is launching autonomous agents or features inspired by OpenClaw (Devin 2.2, Cursor, Claude Cowork, Microsoft, Perplexity and Nous announced theirs), METR and ArcAGI 2,3 benchmarks are getting saturated, 1 person companies nearing 1M ARR within months of operation by running AI agents 24/7 (we chatted with one of them on the show today, live as he broke $700K ARR barrier) and the US Department of War gives Anthropic an ultimatum to remove nearly all restrictions on Claude for war and Anthropic says NO.

I’ve been covering AI for 3 years every week, and this week feels, different. So if we are nearing the singularity, let me at least keep you up to date 😅

Today on the show, we covered most of the news in the first hour + breaking news from Google, Nano Banana 2 is here, and then had 3 interviews back to back. Ben Broca with Polsia, Nader Dabit with Cognition and Philip Kiely with BaseTen. Don’t miss those conversations starting at 1 hour in.

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Anthropic vs Department of War

Earlier this week, the US “Department of War” invited Dario Amodei, CEO of Anthropic to a meeting, where-in Anthropic was given an ultimatum. “Remove the restrictions on Claude or Anthropic will be designated as a ‘supply chain risk’ company” and the DoD will potentially go as far as using the Defence Production Act to force Anthropic to ... comply.

The two restrictions that Anthropic has in place for their models are: No use for domestic surveillance of American citizens and NO fully autonomous lethal weapens decisions given to Claude. For context, Claude is the only model that’s deployed on AWS top secret GovCloud and is used through Palantir’s AI platform.

As I’m writing this, Anthropic issued a statement from Dario statement saying they will not budge on this, and will not comply. I fully commend Dario and Anthropic for this very strong backbone, but I fear that this matter is far from over, and we’ll continue to see what is the government response.

EDIT: Apparently the DoD is pressuring Google and OpenAI to agree to the stipulations and employees from both companies are signing this petition https://notdivided.org/ to protest against dividing the major AI labs on this topic.

Anthropic and OpenAI vs upcoming Deepseek

It’s baffling just how many balls are in the air for Anthropic, as just this week also, they have publicly named 3 Chinese AI makers in “Distillation Attacks”, claiming that they have broke Terms of Service to generate over 16M conversations with Claude to improve their own models, while using proxy networks to avoid detection. This marks the first time a major AI company publicly attributed distillation attacks to specific entities by name.

The most telling thing to me is not the distillation, given that Anthropic has just recently settled one of the largest copyright payouts in U.S history, paying authors about $3000/book, which was bought, trained on and destroyed by Anthropic to make Claude better.

No, the most telling thing here is the fact that Anthropic chose to put DeepSeek on top of the accusation list with merely 140K conversations, where the other labs created millions.

This, plus OpenAI formal memo to Congress about a similar matter, shows that the US labs are trying to prepare for Deepseek new model to drop, by saying “Every innovation they have, they stole from us”. Apparently Deepseek V4 is nearly here, it’s potentially multimodal and has been allegedly trained on Nvidia chips somewhere in Mongolia despite the export restrictions and it’s about to SLAP!

Benchmark? What benchmarks?

How will we know that we’re approaching the singularity? Will there be signs? Well, this week it seems that the signs are here.

First, Agentica claimed that they solved all publicly available “hard for AI” tasks of the upcoming ArcAGI 3, then Confluence Labs announced that they got an unprecedented 97.9% on ArcAGI2 and finally METR published their results on the long-horizon tasks, which measure AI’s capability to solve task that take humans a certain amount of hours to do. And that graph is going parabolic, with Claude Opus 4.6 able to solve tasks of 14.6h (doubling every 49 days) with 50% success rate

Why is this important? Well, this is just the benchmarks telling the story that everyone else in the industry is seeing, that approximately since December of 2025, and definitely fueled by early Feb drop of Opus 4.6 and Codex 5.3, something major shifted. Developers no longer write code, but ship 10x more features.

This became such a talking point, Swyx Latent.Space coined this with

https://wtfhappened2025.com/ where he collects evidence of a shelling point, something that happened in December and I think continued throughout February.

Speaking of benchmarks no longer being valid, OpenAI published that the divergence between the SWE-bench verified gains with real life performance is so vast, that they will no longer be using SWE-bench verified, and will be switching to SWE-bench pro for evaluations.

Everyone’s Autonomous agents (and subagents) are here

Look, with over 250K Github stars, OpenAI getting Peter Steinberger on board, it’s clear now. OpenClaw made a huge dent in how people think about autonomous agents (and subagents!)

It may be a “moment in time” that the model capabilities were “just good enough” to be able to run agents async for a long time. but the big labs noticed the OpenClaw excitement and are shipping like never before to make sure their users don’t switch over!

Perplexity launched “Computer“, which has scheduled tasks in a compute environment, and can complete long lasting projects end to end, Cursor pivots from IDE only to running Agents in the cloud with their own environments, Claude Code added memory, and Remote Control, while Claude Cowork added Scheduled tasks, our friends from Nous shipped Hermes Agent and even Microsoft wants to bring this to their customers in Copilot. The most interesting one from these is the new Devin from Cognition.

I’ve gotten access and chatted with Nader Dabit on the show about how Devin was the “OG” async coding Agent, but now as models capabilities are here, Devin can do so much more. PR reviews with devinreview.com can complete the loop between coding, fixing and testing something end to end. They have an integrated environment with a scrub so you can roll back and see what the agent did, scheduled tasks and video showing you how the agent tested your website.

I’ve used it to fix bugs in ThrusdAI.news and it found a few that Claude Code didn’t even know about! You can try out Devin (for free for a week?) here

This weeks buzz - W&B updates

I’m happy this week, because we finally launched both 2.5 open source models that we’re making the news lately.

Kimi 2.5 and MiniMax M2.5 are both live on our inference service, at very very decent prices!

Check them both out here and let me know if you need some credits.

From the show this week, most hosts agree that Kimi 2.5 is the best open source alternative to Opus inside OpenClaw, just give your agent the WANDB_API_KEY and ask it to set itself up with the new model!

Surfing the singularity with Ben Broca and Polsia, hitting $700K ARR since December

I’ve reached out to Ben and asked him to join the show this week because alongside OpenClaw blowing up since December, his Polsia startup, which builds and scales entire companies with AI agents running 24x7 has hit an unprecedented $700K ARR milestone after just a few months. We actually saw him break the $700K ARR on the show live 🎉 But get this, he’s the only employee, everything is done with AIs. He’s using Polsia to scale Polsia.

Polsia let’s anyone add an existing company or create a whole new one, and then a team of agents will spin up a marketing team, a GTM motion, a research arm and you and Polsia could work together to make this company a reality. Does this actually work? IDK, the whole thing is new, I’m trying out a few things and will let you know in a few weeks if any of this worked.

But it’s definitely blowing up, Ben showed us that over the last 24 hours, over 770 companies launched on Polsia, he’s hitting nearly 1M ARR with people paying $50/mo for him to run inference for them, marketing campaigns, and he just added Meta ads.

This ARR chart, the live dashboard, and Ben doing all of this Solo is underlining the whole “Singularity is near” thing for me! It’s impossible to imagine something like this working even... 5 months ago, and now we just accept it as .. sure, yeah, one person can manage AIs that manage checks notes over 700 companies.

What’s clever about Polsia’s architecture is the cross-company learning system: when an agent learns something useful (like “subject lines with emojis get better open rates”), that learning gets anonymized and generalized into a shared memory file that benefits every company on the platform. The more companies running on Polsia, the smarter every agent gets — like a platform effect but for agent intelligence.

AI Art, Video & Audio

Seedance 2.0 is finally “here”

This week has not been quiet in the multimodality world either, SeeDance 2.0 from ByteDance was delayed via the API partners (was supposed to launch Feb 24) due to copyright concerns, but apparently they dropped it inside CapCut, ByteDance’s video editing software! It’s really good though what makes it absolutely incredible IMO is the video transfer, and you can’t really do that in CapCut, so we’re keep waiting for the “full model”

Nano Banana 2 - Pro level intelligence, with Flash speed and pricing (Blog)

Google dropped a breaking news item before the show started today, and announced Nano Banana 2, which is supposed to be as good as Nano Banana Pro (which is incredible) but faster. It wasn’t really faster for me, as I got early access thanks to the DeepMind team, but apparently it’s just the rollout pains. But the quality is nearly matching Nano Banana Pro!

It can do the same super high quality text rendering, comes with a few new ratios to create ultra long images (4:1 and 1:4) and a new small 512 resolution for extra cheap generation. The additional thing is Image Search is now integrated into the model, allowing it to look something up before generating. Though, that didn’t really work for me as well, I tried to get it to look up images of Mike Intrator and Dario Amodei, and it kept showing me random people who look nothing like them, despite the thinking traces showing the search did happen.

Speaking of pricing, this model is around 50-30% cheaper than NBP, which is great given the added speed! Go play with it, it’s available on AI.dev and Gemini, go give it a try!

Open source AI

This week in OpenSource, our friends from Qwen came back with a set of 3 models, the middle medium one is a hybrid architecture with only 3B parameters that beats their 235B flagship Qwen3 from before! It’s really good at longer context especially given the hybrid attention similar to Jamba that we covered before. (X, HF, HF, HF, Blog)

Additionally, Liquid AI releases their largest LFM, 24B (X, HF, Blog) and that is also deployable on consumer laptops.

One note on AI tools, LM Studio, our favorite way of running these models on your hardware, have launched LMLink, powered by Tailscale, which let’s you run local inference on once device and stream tokens to any other device in your network securely! You can use this to run your OpenClaw with Qwen medium for example, for a complete off the grid OpenClaw!

Check it out here: https://lmstudio.ai/link

I really didn’t want to sound hype-y but this week things are moving so fast that I was not sure how it’s possible to talk about all this, covering the news while also having 3 interviews. I think we’ve done a good job, but I am honestly getting to a point whereI have to do deep prioritization of what content is the most important in my eyes. I hope you guys enjoy my prioritization, and do leave comments of what you’d like to see more, or see less of! I am hungry for feedback!

If you enjoyed this week’s newsletter, checkout the whole edited video and share it with a friend or two? See you next week!

ThursdAI - Join us as we surf the AI singularity together

Here’s the TL;DR and show notes:

ThursdAI - Feb 26, 2026 - TL;DR

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson

* Ben Cera (@bencera_) - Founder Polsia

* Nader Dabit (@dabit3) - Growth at Cognition

* Philip Kiely (@philipkiely) - Devrel Base10, Author Inference Engineering

* ThursdAI new website: https://thursdai.news

* Big CO LLMs + APIs

* Anthropic vs Chinese OSS - Accuses DeepSeek, Minimax, ZAI at distillation attacks (Blog)

* Pentagon Issues an ultimatum to Anthropic: Give military unfettered Claude access by Friday or face Defense Production Act - Anthropic says NO (Blog)

* OpenAI releases GPT-5.3-Codex, their most capable agentic coding model, to all developers via the Responses API (X, Announcement)

* Open Source LLMs

* Alibaba: Qwen 3.5 Medium - 35B model with only 3B active parameters outperforms their previous 235B flagship (X, HF, HF, HF, Blog)

* Liquid AI releases LFM2-24B-A2B: A 24B MoE model with only 2.3B active parameters that runs on consumer laptops (X, HF, Blog)

* Perplexity launches ppxl-embed - SOTA embedding models (Blog, HF, API) by our friend Bo Wang

* Evals & Benchmarks

* METR’s Time Horizon Benchmark Goes Vertical: Claude Opus 4.6 Achieves ~14.5 Hour Task Completion (X, Blog)

* Confluence Labs emerges from stealth with 97.9% SOTA on ARC-AGI-2 benchmark (X, GitHub)

* OpenAI Retires SWE-bench Verified, (X, Blog, X)

* Agentica claiming to solve all public ArcAGI 3 (X)

* Tools & Agentic Engineering

* Happy 1 year Birthday Claude Code!

* Devin AI 2.2 - autonomous agent with computer use, browser, self verify and self fix it’s own work - interview with Nader Dabit (X)

* LMStudio launches LMLink - use your local models from everywhere with TailScale! (try it)

* Claude Code introduces Remote Control: Control your local coding sessions from your phone or any device (X, Docs) and memory (X)

* Claude Cowork and Codex both now have automations (Cron Jobs) to do tasks for you (Cowork)

* Cursor launches cloud agents that onboard to codebases, run in isolated VMs, and deliver video demos of completed PRs (X)

* Nous research agent (X)

* Perplexity Computer (blog)

* Microsoft Copilot tasks (blog)

* This weeks Buzz - Weights & Biases update

* W&B adds MiniMax 2.5 and Kimi K2.5 on our Inference Service (LINK)

* Interviews mention links

* Ben Broca - polsia.com/live Polsia Dashboard

* Nader Dabit - on seeing the future (blog)

* Philip Kiely - Inference Engineering book (Book)

* Vision & Video

* Seedance 2.0 finally available in Capcut in US. API release apparently held back due to copyright issues (X)

* Voice & Audio

* OpenAI releases gpt-audio-1.5 and gpt-realtime-1.5 models with major improvements in speech-to-speech AI capabilities (X, Announcement)

* AI Art & Diffusion & 3D

* Google DeepMind launches Nano Banana 2 (X, Announcement)

* Quiver solves SVG with Arrow 1.0 (X)

* Others

* Taalas AI - 15,000 tokens per second demo (chatjimmy.ai/)

Since last week, OpenAI convinced OpenClaw founder Peter Steinberger to join them, while keeping OpenClaw.. well... open. Anthropic dropped Sonnet 4.6 which nearly outperforms the previous Opus and is much cheaper, Qwen released 3.5 on Chinese New Year’s Eve, while DeepSeek was silent and Elon and XAI folks deployed Grok 4.20 without any benchmarks, and it’s 4 500B models in a trenchcoat?

Also, Anthropic updated rules state that it’s breaking ToS to use their plans for anything except Claude Code & Claude SDK (and then clarified that it’s OK? we’re not sure)

Then Google decided to drop their Gemini 3.1 Pro preview right at the start of our show, and it’s very nearly the best LLM folks can use right now (though it didn’t pass Nisten’s vibe checks)

Also, Google released Lyria 3 for music gen (though only 30 seconds?) and our own Ryan Carson blew up on X again with over 1M views for his Code Factory article, Wolfram did a deep dive into Terminal Bench and .. we have a brand new website:

https://thursdai.news 🎉

Great week all in all, let’s dive in!

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Big Companies & API updates

Google releases Gemini 3.1 Pro with 77.1% on ARC-AGI-2 (X, Blog, Announcement)

In a release that surprised no-one, Google decided to drop their latest update to Gemini models, and it’s quite a big update too! We’ve now seen all major labs ship big model updates in the first two months of 2026. With 77.1% on ARC-AGI 2, and 80.6% on SWE-bench verified, Gemini is not complete SOTA across the board but it’s damn near close.

The kicker is, it’s VERY competitive on the pricing, with 1M context, $2 / $12 (<200k tokens), and Google’s TPU speeds, this is now the model to beat! Initial vibe checks live on stage did not seem amazing, Nisten wasn’t super impressed, Ryan took one glance at the SWE-bench pro not being SOTA and decided to skip, and he’s added that, at some point, it is benefitting to pick a model and stick to it, the constant context switching is really hard for folks who want to keep shipping.

But if you look at the trajectory, it’s really notable how quickly we’re moving, with this model being 82% better on abstract reasoning than the 3 pro released just a few months ago!

The 1 Million Context Discrepancy, who’s better at long context?

The most fascinating catch of the live broadcast came from LDJ, who has an eagle eye for evaluation tables. He immediately noticed something weird in Google’s reported benchmarks regarding long-context recall. On the MRCR v2 8-needle benchmark (which tests retrieval quality deep inside a massive context window), Google’s table showed Gemini 3.1 Pro getting a 26% recall score at 1 million tokens. Curiously, they marked Claude Opus 4.6 as “not supported” in that exact tier.

LDJ quickly pulled up the actual receipts: Opus 4.6 at a 1-million context window gets a staggering 76% recall score. That is a massive discrepancy! It was addressed by a member of DeepMind on X in a response to me, saying that Anthropic used an internal model for evaluating this (with receipts he pulled from the Anthropic model card)

Live Vibe-Coding Test for Gemini 3.1 Pro

We couldn’t just stare at numbers, so Nisten immediately fired up AI Studio for a live vibe check. He threw our standard “build a mars driver simulation game” prompt at the new Gemini.

The speed was absolutely breathtaking. The model generated the entire single-file HTML/JS codebase in about 20 seconds. However, when he booted it up, the result was a bit mixed. The first run actually failed to render entirely. A quick refresh got a version working, and it rendered a neat little orbital launch UI, but it completely lacked the deep physics trajectories and working simulation elements that models like OpenAI’s Codex 5.3 or Claude Opus 4.6 managed to output on the exact same prompt last week. As Nisten put it, “It’s not bad at all, but I’m not impressed compared to what Opus and Codex did. They had a fully working one with trajectories, and this one I’m just stuck.”

It’s a great reminder that raw benchmarks aren’t everything. A lot of this comes down to the harness—the specific set of system prompts and sandboxes that the labs use to wrap their models.

Anthropic launches Claude Sonnet 4.6, with 1M token context and near-Opus intelligence at Sonnet pricing

The above Gemini release comes just a few days after Anthropic has shipped an update to the middle child of their lineup, Sonnet 4.6. With much improved Computer Use skills, updated Beta mode for 1M tokens, it achieves 79.6% on SWE-bench verified eval, showing good coding performance, while maintaining that “anthropic trained model” vibes that many people seem to prefer.

Apparently in blind testing inside Claude Code, folks preferred this new model outputs to the latest Opus 4.5 around ~60% of the time, while preferring it over the previous sonnet 70% of the time.

With $3/$15 per million tokens pricing, it’s cheaper than Opus, but is still more expensive than the flagship Gemini model, while being quite behind.

Vibing with Sonnet 4.6

I’ve tested out Sonnet 4.6 inside my OpenClaw harness for a few days, and it was decent. It did annoy me a bit more than Opus, with misunderstanding what I ask it, but it definitely does have the same “emotional tone” as Opus. Comparing it to Codex 5.3 is very easy, it’s much nicer to talk to. IDK what kind of Anthropic magic they put in there, but if you’re on a budget, Sonnet is definitely the way to go when interacting with Agents (and you can get it to orchestrate as many Codex instances as you want if you don’t like how it writes code)

For Devs: Auto prompt caching and Web Search updates

One nice update Anthropic also dropped is that prompt caching (which leads to almost 90% decrease in token pricing) for developers (Blog) and a new and improved Web Search for everyone else that can now use tools

Grok 4.20 - 4 groks in a trenchcoat?

In a very weird release, Grok has been updated with the long hyped Grok 4.20. Elon has been promising this version for a while (since late last year in fact) and this “release” definitely felt underwhelming. There was no evaluations, no comparisons to other labs models, no charts (heck, not even a blogpost on X.ai).

What we do know, is that Grok 4.20 (and Grok 4.20 Heavy) use multiple agents (4 for Grok, 16 for Heavy) to do a LOT of research and combine their answers somehow. This is apparently what the other labs use for their ultra expensive models (GPT Pro and Gemini DeepThink) but Grok is showing it in the UI, and gives these agents... names and personalities.

Elon has confirmed also that what’s deployed right now is ~500B “small” base version, and that bigger versions are coming, in one of the rarest confirmations about model size from the big labs.

Vibe checking this new grok, it’s really fast at research across X and the web, but I don’t really see it as a daily driver for anyone who converses with LLMs all the time. Supposedly they are planning to keep teaching this model and get it “improved week over week” so I’ll keep you up to date with major changes here.

Open Source AI

It seems that all the chinese OSS labs were shipping before the Chinese New Year, with Qwen being the last one of them, dropping the updated Qwen 3.5.

Alibaba’s Qwen3.5 397B-A17B: First open-weight native multimodal MoE model (X, HF)

Qwen decided to go for Sparse MoE architecture with this release, with a high number of experts (512) and only 17B active parameters.

It’s natively multi-modal with a hybrid architecture, able to understand images/text, while being comparable to GPT 5.2 and Opus 4.5 on benches including agentic tasks.

Benchmarks aside, the release page of Qwen models is a good sniff test on where these model labs are going, they have multimodality in there, but they also feature an example of how to use this model within OpenClaw, which doesn’t necessarily show off any specific capabilities, but shows that the Chinese labs are focusing on agentic behavior, tool use and mostl of all pricing!

This model is also available as Qwen 3.5 Max with 1M token window (as opposed to the 256K native one on the OSS side) on their API.

Agentic Coding world - The Clawfather is joining OpenAI, Anthropic loses dev mindshare

This was a heck of a surprise to many folks, Peter Steinberger, announced that he’s joining OpenAI, while OpenClaw (that now sits on >200K stars in Github, and is adopted by nearly every Chinese lab) is going to become an Open Source foundation.

OpenAI has also confirmed that it’s absolutely ok to use your ChatGPT plus/pro subscriptions to use inside OpenClaw, and it’s really a heck of a thing to see how quickly Peter jumped from relative anonymity (after scaling and selling PSPDFKIT ) into a spotlight. Apparently Mark Zuckerberg reached out directly as well as Sam Altman, and Peter decided to go with OpenAI despite Zuck offering more money due to “culture”

This whole ClawdBot/OpenClaw debacle also shines a very interesting and negative light on Anthropic, who recently changed their ToS to highlight that their subscription can only be used for Claude Code and nothing else. This scared a lot of folks who used their Max subscription to run their Claws 24/7. Additionally Ryan echoed how the community feel about lack of DevEx/Devrel support from Anthropic in a viral post.

However, it does not seem like Anthropic cares? Their revenue is going exponential (much of it due to Claude Code)

Very interestingly, I went to a local Claude Code meetup here in Denver, and the folks there are.. a bit behind the “bubble” on X. Many of them didn’t even try Codex 5.3 or OpenClaw, they are maximizing their time with Claude Code like there’s no tomorrow. It has really shown me that the alpha keeps changing really fast, and many folks don’t have the time to catch up!

P.S - this is why ThursdAI exists, and I’m happy to deliver the latest news to ya.

This Week’s Buzz from Weights & Biases

Our very own Wolfram Ravenwolf took over the Buzz corner this week to school us on the absolute chaos that is AI benchmarking. With his new role at W&B, he’s been stress-testing all the latest models on Terminal Bench 2.0.

Why Terminal Bench? Because if you are building autonomous agents, multiple-choice tests like MMLU are basically useless now. You need to know if an agent can actually interact with an environment. Terminal Bench asks the agent to perform 89 real-world tasks inside a sandboxed Linux container—like building a Linux kernel or cracking a password-protected archive.

Wolfram highlighted some fascinating nuances that marketing slides never show you. For example, did you know that on some agentic tasks, turning off the model’s “thinking/reasoning” mode actually results in a higher score? Why? Because overthinking generates so many internal tokens that it fills the context window faster, causing the model to hit its limits and fail harder than a standard zero-shot model! Furthermore, comparing benchmarks between labs is incredibly difficult because changing the benchmark’s allowed runtime from 1 hour to 2 hours drastically raises the ceiling of what models can achieve.

He also shared a great win: while evaluating GLM-5 for our W&B inference endpoints, he got an abysmal 5% score. By pulling up the Weave trace data, Wolfram immediately spotted that the harness was injecting brain-dead Python syntax errors into the environment. He reported it, engineering fixed it in minutes, and the score shot up to its true state-of-the-art level. This is exactly why you need powerful tracing and evaluation tools when dealing with these black boxes! So y’know... check out Weave!

Vision & BCI

Zyphra’s ZUNA: Thought-to-Text Gets Real (X, Blog, GitHub)

LDJ flagged this as his must-not-miss: Zyphra released ZUNA, a 380M parameter open-source BCI (Brain-Computer Interface) foundation model. It takes EEG signals from your brain and reconstructs clinical-grade brain signals from sparse, noisy data. People are literally calling it “thought to text” hahaha.

At 380M parameters, it could potentially run in real-time on a consumer GPU. Trained on 2 million channel-hours of EEG data from 208 datasets. The wild part: it can upgrade cheap $500 consumer EEG headsets to high-resolution signal quality without retraining, something many folks are posting about and are excited to test out! Non Invasive BCI is the dream!

Nisten was genuinely excited, noting it’s probably the best effort in this field and it’s fully Apache 2.0. Will probably need personalized training per person, but the potential is real: wear a headset, look at a screen, fire up your agents with your thoughts. Not there yet, but this feels like the actual beginning.

Tools & Agentic Coding (The End of “Vibe Coding”) - Ryan Carson’s Code Factory & The “One-Shot Myth”

This one is for developers, but in modern times, everyone can become a developer so if you’re not one, at least skim this.

We spent a big chunk of the show today geeking out over agentic workflows. Ryan Carson went incredibly viral on X again this week with a phenomenal deep-dive on establishing a “Code Factory.” If you are still just chatting with models and manually copying code back into your IDE, you are doing it wrong.

Ryan’s methodology (heavily inspired by a recent OpenAI paper on harness engineering) treats your AI agents like a massive team of junior engineers. You don’t just ask them for code and ship it. You should build a rigid, machine-enforced loop.

Here is the flow:

* The coding agent (Codex, OpenClaw, etc.) writes the code.

* The GitHub repository enforces risk-aware checks. If a core system file or route is touched, the PR is automatically flagged as high risk.

* A secondary code review agent (like Greptile) kicks off and analyzes the PR.

* CI/CD GitHub Actions run automated tests, including browser testing.

* If a test fails, or the review agent leaves a comment, a remediation agent is automatically triggered to fix the issue and loop back.

* The loop spins continuously until you get a flawless, green PR.

As Ryan pointed out, we used to hate this stuff as human engineers. Waiting for CI to pass made you want to pull your hair out. But agents have infinite time and infinite patience. You force them to grind against the machine-enforced contract (YAML/JSON gates) until they get it right. It takes a week to set up properly, and you have to aggressively fight “document drift” to make sure your AI doesn’t forget the architecture, but once it’s humming, you have unprecedented leverage.

My Hard Truth: One-Shot is a Myth I completely agree with Ryan btw! Over the weekend, my OpenClaw agent kindly informed me that the hosting provider for the old ThursdAI website was shutting down. I needed a new website immediately.

I decided to practice what we preach and talk to my ClawdBot to build the entire thing. It was an incredible process. I used Opus 4.6 to mock up 3 designs based on other podcast sites. Then, I deployed a swarm of sub-agents to download and read the raw text transcripts of all 152 past episodes of our show. Their job was to extract the names of every single guest (over 160 guests, including 15 from Google alone!) to build a dynamic guest directory, generating a dedicated SEO page and dynamic OpenGraph tag for every single one of them, a native website podcast player with synced sections, episode pages with guests highlighted and much more. It would have taken me months to write the code for this myself.

Was it magical? Yes. But was it one-shot? Absolutely not.

The amount of back-and-forth conversation, steering, and correction I had to provide to keep the CSS coherent across pages was exhausting. I set up an automation to work while I slept, and I would wake up every morning to a completely different, sometimes broken website.

Yam Peleg chimed in with the quote of the week: “It’s not a question of whether a model can mess up your code, it’s just a matter of when. Because it is a little bit random all the time. Humans don’t mistakenly delete the entire computer. Models can mistakenly, without even realizing, delete the entire computer, and a minute later their context is compacted and they don’t even remember doing it.”

This is why you must have gates. This is also why I don’t think engineers are going to be replaced with AI completely. Engineers who don’t use AI? yup. But if you embrace these tools and learn to work with you, you won’t have an issue getting a job! You need that human taste-maker in the loop to finish the last 5%, and you need strict CI/CD gates to stop the AI from accidentally burning down your production database.

Voice & Audio

Google DeepMind launches Lyria 3 (try it)

Google wasn’t just dropping reasoning models this week; DeepMind officially launched Lyria 3, their most advanced AI music generation model, integrating it directly into the Gemini App.

Lyria 3 generates 30-second high-fidelity tracks with custom lyrics, realistic vocals across 8 different languages, and granular controls over tempo and instrumentation. You can even provide an image and it’ll generate a soundtrack (short one) for that image.

While it is currently limited to 30-second tracks (which makes it hard to compare to the full-length song structures of Suno or Udio), early testers are raving that the actual audio fidelity and prompt adherence of Lyria 3 is far superior. All tracks are invisibly watermarked with Google’s SynthID to ensure provenance, and it automatically generates cover art using Nano Banana. I tried to generate a jingle

That’s a wrap for this weeks episode folks, what an exclirating week! ( Yes I know it’s a typo, but how else would you know that I’m human?)

Please go check out our brand new website (and tell me if anything smells off there, it’s definitely not perfect!), click around the guests directory and the episodes pages (the last 3 have pages, I didn’t yet backfill the rest) and let me know what you think!

See you all next week!

-Alex

ThursdAI - Feb 19, 2026 - TL;DR

TL;DR of all topics covered:

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson

* 🔥 New website: thursdai.news with all our past guests and episodes

* Open Source LLMs

* Alibaba releases Qwen3.5-397B-A17B: First open-weight native multimodal MoE model with 8.6-19x faster inference than Qwen3-Max (X, HF)

* Cohere Labs releases Tiny Aya, a 3.35B multilingual model family supporting 70+ languages that runs locally on phones (X, HF, HF)

* Big CO LLMs + APIs

* OpenClaw founder joins OpenAI

* Google releases Gemini 3.1 Pro with 2.5x better abstract reasoning and improved coding/agentic capabilities (X, Blog, Announcement)

* Anthropic launches Claude Sonnet 4.6, its most capable Sonnet model ever, with 1M token context and near-Opus intelligence at Sonnet pricing (X, Blog, Announcement)

* ByteDance releases Seed 2.0 - a frontier multimodal LLM family with Pro, Lite, Mini, and Code variants that rivals GPT-5.2 and Claude Opus 4.5 at 73-84% lower pricing (X, blog, HF)

* Anthropic changes the rules on Max use, OpenAI confirms it’s 100% fine.

* Grok 4.20 - finally released, a mix of 4 agents

* This weeks Buzz

* Wolfram deep dives into Terminal Bench

* We’ve launched Kimi K2.5 on our inference service (Link)

* Vision & Video

* Zyphra releases ZUNA, a 380M-parameter open-source BCI foundation model for EEG that reconstructs clinical-grade brain signals from sparse, noisy data (X, Blog, GitHub)

* Voice & Audio

* Google DeepMind launches Lyria 3, its most advanced AI music generation model, now available in the Gemini App (X, Announcement)

* Tools & Agentic Coding

* Ryan is viral once again with CodeFactory! (X)

* Ryan uses Agentation.dev for front end development closing the loop on componenets

* Dreamer launches beta: A full-stack platform for building and discovering agentic apps with no-code AI (X, Announcement)

This week started with Anthropic releasing /fast mode for Opus 4.6, continued with ByteDance reality-shattering video model called SeeDance 2.0, and then the open weights folks pulled up!

Z.ai releasing GLM-5, a 744B top ranking coder beast, and then today MiniMax dropping a heavily RL’d MiniMax M2.5, showing 80.2% on SWE-bench, nearly beating Opus 4.6! I’ve interviewed Lou from Z.AI and Olive from MiniMax on the show today back to back btw, very interesting conversations, starting after TL;DR!

So while the OpenSource models were catching up to frontier, OpenAI and Google both dropped breaking news (again, during the show), with Gemini 3 Deep Think shattering the ArcAGI 2 (84.6%) and Humanity’s Last Exam (48% w/o tools)... Just an absolute beast of a model update, and OpenAI launched their Cerebras collaboration, with GPT 5.3 Codex Spark, supposedly running at over 1000 tokens per second (but not as smart)

Also, crazy week for us at W&B as we scrambled to host GLM-5 at day of release, and are working on dropping Kimi K2.5 and MiniMax both on our inference service! As always, all show notes in the end, let’s DIVE IN!

ThursdAI - AI is speeding up, don’t get left behind! Sub and I’ll keep you up to date with a weekly catch up

Open Source LLMs

Z.ai launches GLM-5 - #1 open-weights coder with 744B parameters (X, HF, W&B inference)

The breakaway open-source model of the week is undeniably GLM-5 from Z.ai (formerly known to many of us as Zhipu AI). We were honored to have Lou, the Head of DevRel at Z.ai, join us live on the show at 1:00 AM Shanghai time to break down this monster of a release.

GLM-5 is massive, not something you run at home (hey, that’s what W&B inference is for!) but it’s absolutely a model that’s worth thinking about if your company has on prem requirements and can’t share code with OpenAI or Anthropic.

They jumped from 355B in GLM4.5 and expanded their pre-training data to a whopping 28.5T tokens to get these results. But Lou explained that it’s not only about data, they adopted DeepSeeks sparse attention (DSA) to help preserve deep reasoning over long contexts (this one has 200K)

Lou summed up the generational leap from version 4.5 to 5 perfectly in four words: “Bigger, faster, better, and cheaper.” I dunno about faster, this may be one of those models that you hand off more difficult tasks to, but definitely cheaper, with $1 input/$3.20 output per 1M tokens on W&B!

While the evaluations are ongoing, the one interesting tid-bit from Artificial Analysis was, this model scores the lowest on their hallucination rate bench!

Think about this for a second, this model is neck-in-neck with Opus 4.5, and if Anthropic didn’t release Opus 4.6 just last week, this would be an open weights model that rivals Opus! One of the best models the western foundational labs with all their investments has out there. Absolutely insane times.

MiniMax drops M2.5 - 80.2% on SWE-bench verified with just 10B active parameters (X, Blog)

Just as we wrapped up our conversation with Lou, MiniMax dropped their release (though not weights yet, we’re waiting ⏰) and then Olive Song, a senior RL researcher on the team, joined the pod, and she was an absolute wealth of knowledge!

Olive shared that they achieved an unbelievable 80.2% on SWE-Bench Verified. Digest this for a second: a 10B active parameter open-source model is directly trading blows with Claude Opus 4.6 (80.8%) on the one of the hardest real-world software engineering benchmark we currently have. While being alex checks notes ... 20X cheaper and much faster to run? Apparently their fast version gets up to 100 tokens/s.

Olive shared the “not so secret” sauce behind this punch-above-its-weight performance. The massive leap in intelligence comes entirely from their highly decoupled Reinforcement Learning framework called “Forge.” They heavily optimized not just for correct answers, but for the end-to-end time of task performing. In the era of bloated reasoning models that spit out ten thousand “thinking” tokens before writing a line of code, MiniMax trained their model across thousands of diverse environments to use fewer tools, think more efficiently, and execute plans faster. As Olive noted, less time waiting and fewer tools called means less money spent by the user. (as confirmed by @swyx at the Windsurf leaderboard, developers often prefer fast but good enough models)

I really enjoyed the interview with Olive, really recommend you listen to the whole conversation starting at 00:26:15. Kudos MiniMax on the release (and I’ll keep you updated when we add this model to our inference service)

Big Labs and breaking news

There’s a reason the show is called ThursdAI, and today this reason is more clear than ever, AI biggest updates happen on a Thursday, often live during the show. This happened 2 times last week and 3 times today, first with MiniMax and then with both Google and OpenAI!

Google previews Gemini 3 Deep Think, top reasoning intelligence SOTA Arc AGI 2 at 84% & SOTA HLE 48.4% (X , Blog)

I literally went 🤯 when Yam brought this breaking news. 84% on the ARC-AGI-2 benchmark. For context, the highest score prior to this was 68% from Opus 4.6 just last week. A jump from 68 to 84 on one of the hardest reasoning benchmarks we have is mind-bending. It also scored a 48.4% on Humanity’s Last Exam without any tools.

Only available to Ultra subscribers to Gemini (not in API yet?) this model seem to be the current leader in reasoning about hard problems and is not meant for day to day chat users like you and me (though I did use it, and it’s pretty good at writing!)

They posted Gold-medal performance on 2025 Physics and Chemistry Olympiads, and an insane 3455 ELO rating at CodeForces, placing it within the top 10 best competitive programmers. We’re just all moving so fast I’m worried about whiplash! But hey, this is why we’re here, we stay up to date so you don’t have to.

OpenAI & Anthropic fast modes

Not 20 minutes passed since the above news, when OpenAI announced a new model that works only for Pro tier members (I’m starting to notice a pattern here 😡), GPT 5.3 Codex Spark.

You may be confused, didn’t we just get GPT 5.3 Codex last week? well yeah, but this one, this one is its little and super speedy brother, hosted by the Cerebras partnership they announced a while ago, which means, this coding model absolutely slaps at over 1000t/s.

Yes, over 1K tokens per second can be generated with this one, though there are limits. It’s not as smart, it’s text only, it has 128K context, but still, for MANY subagents, this model is an absolute beast. It won’t refactor in one shot your whole code-base but it’ll generate and iterate on it, very very quick!

OpenAI also previously updated Deep Research with GPT 5.2 series of models, and we can all say bye bye to the “older” version of models, like 5, o3 and most importantly GPT 4o, which got a LOT of people upset (enough that they have a hashtag going, #keep4o) !

Anthropic also announced their fast mode (using /fast) in Claude Code btw on Saturday, and that one is absolutely out of the scope for many users, with $225/1M tokens on output, this model will just burn through your wallet. Unlike the Spark version, this seems to be the full Opus 4.6 just... running on some dedicated hardware? I thought this was a rebranded Sonnet 5 at first but Anthropic folks confirmed that it wasn’t.

Vision & Video

ByteDance’s Seedance 2.0 Shatters Reality (and nobody in the US can use it)

I told the panel during the show: my brain is fundamentally broken after watching the outputs from ByteDance’s new Seedance 2.0 model. If your social feed isn’t already flooded with these videos, it will be so very soon (supposedly the API launches Feb 14 on Valentines Day)

We’ve seen good video models before. Sora blew our minds and then Sora 2, Veo is (still) great, Kling was fantastic. But Seedance 2.0 is an entirely different paradigm. It is a unified multimodal audio-video joint generation architecture. What does that mean? It means you can simultaneously input up to 9 reference images, 3 video clips, 3 audio clips, and text instructions all at once to generate a 15-second cinematic short film. It character consistency is beyond what we’ve seen before, physics are razor sharp (just looking at the examples folks are posting, it’s clear it’s on another level)

I think very soon though, this model will be restricted, but for now, it’s really going viral due to the same strategy Sora did, folks are re-imagining famous movie and TV shows endings, doing insane mashups, and much more! Many of these are going viral over the wall in China.

The level of director-like control is unprecedented. But the absolute craziest part is the sound and physics. Seedance 2.0 natively generates dual-channel stereo audio with ASMR-level Foley detail. If you generate a video of a guy taking a pizza out of a brick oven, you hear the exact scratch of the metal spatula, the crackle of the fire, the thud of the pizza box, and the rustling of the cardboard as he closes it. All perfectly synced to the visuals.

Seedance 2 feels like “borrowed realism”. Previous models had only images and their training to base their generations on. It 2 accepts up to 3 video references in addition to images and sounds.

This is why some of the videos feel like a new jump in visual capabilities. I have a hunch that ByteDance will try and clamp down on copyrighted content before releasing this model publicly, but for now the results are very very entertaining and I can’t help but wonder, who is the first creator that will just..remake the ending of GOT last season!?

Trying this out is hard right now, especially in the US, but there’s a free way to test it out with a VPN, go to doubao.com/chat when connected from a VPN and select Seedream 4.5 but ask for “create a video please” in your prompt!

AI Art & Diffusion: Alibaba’s Qwen-Image-2.0 (X, Blog)

The Qwen team over at Alibaba has been on an absolute tear lately, and this week they dropped Qwen-Image-2.0. In an era where everyone is scaling models up to massive sizes, Alibaba actually shrank this model from 20B parameters down to just 7B parameters, while massively improving performance (tho didn’t drop the weights yet, they are coming)

Despite the small size, it natively outputs 2K (2048x2048) resolution images, giving you photorealistic skin, fabric, and snow textures without needing a secondary upscaler. But the real superpower of Qwen-Image-2.0 is its text rendering, it supports massive 1,000-token prompts and renders multilingual text (English and Chinese) flawlessly.

It’s currently #3 globally on AI Arena for text-to-image (behind only Gemini-3-Pro-Image and GPT Image 1.5) and #2 for image editing. My results with it were not the best, I tried to generate this weeks Thumbnails with it and .. they turned out meh at best?

In fact, my results were so so bad compared to their launch blog that I’m unsure that they are serving me the “new” model 🤔 Judge for yourself, the above infographic was created with Nano Banana Pro, and this one, same prompt, with Qwen Image on their website:

But you can test it for free at chat.qwen.ai right now, and they’ve promised open-source weights after the Chinese New Year!

🛠️ Tools & Orchestration: Entire Checkpoints & WebMCP

With all these incredibly smart, fast models, the tooling ecosystem is desperately trying to keep up. Two massive developments happened this week that will change how we build with AI, moving us firmly away from hacky scripts and into robust, agent-native development.

Entire Raises $60M Seed for OSS Agent Workflows

Agent orchestration is the hottest problem in tech right now, and a new company called Entire just raised a record-breaking $60 Million seed round (at a $300M valuation—reportedly the largest seed ever for developer tools) to solve it. Founded by former GitHub CEO Thomas Dohmke, Entire is building the “GitHub for the AI agent era.”

Their first open-source release is a CLI tool called Checkpoints.

Checkpoints integrates via Git hooks and automatically captures entire agent sessions—transcripts, prompts, files modified, token usage, and tool calls—and stores them as versioned Git data on a separate branch (entire/checkpoints/v1). It creates a universal semantic layer for agent tracing. If your Claude Code or Gemini CLI agent goes off the rails, Checkpoints allows you to seamlessly rewind to a specific state in the agent’s session.

We also have to shout out our own Ryan Carson, who shipped his open-source project AntFarm this week to help orchestrate these agents on top of Open-Claw!

Chrome 146 Introduces WebMCP

Finally, an absolutely massive foundational shift is happening on the web. Chrome 146 Canary is shipping an early preview of WebMCP.

We have been talking about web-browsing agents for a while, and the biggest bottleneck has always been brittle DOM scraping, guessing CSS selectors, and simulating clicks via Puppeteer or Playwright. It wastes an immense amount of tokens and breaks constantly. Chrome 146 is fundamentally changing this by introducing a native browser API.

Co-authored by Google and Microsoft under the W3C Web Machine Learning Community Group, WebMCP allows websites to declaratively expose structured tools directly to AI agents using JSON schemas via navigator.modelContext. You can even do this declaratively through HTML form annotations using tool-name and tool-description attributes. No backend MCP server is required;

I don’t KNOW if this is going to be big or not, but it definitely smells like it, because even the best agentic AI assistants are struggling with browsing the web, given the constrained context windows cannot just go by HTML content and screenshots! Let’s see if this will help agents browsing the web!

All right, that about sums it up I think for this week, it was an absolute banger of a week, for open the one thing I didn’t cover as a news item but mentioned last week, is that many folks report being overly tired, barely able to go to sleep while their agentic things are running, and all of us are trying to get to the bottom of how to work with these new agentic coding tools.

Steve Yegge noticed the same and called it “the AI vampire“ while Matt Shumer went ultraviral (80M+ views) on his article about “something big is coming“ which terrified a lot of folks. What’s true for sure, is that we’re going through an inflection point in humanity, and I believe that staying up to date is essential as we go through it, even if some of it seems scary or “too fast”.

This is why ThursdAI exists, I first and foremost wanted this for ME to stay up to date, and after that to share this with all of you. Having recently hit a few milestones for ThursdAI, all I can say is thanks for sharing, reading, listening and tuning in from week to week 🫡

ThursdAI - Feb 12, 2026 - TL;DR

TL;DR of all topics covered:

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed) @ryancarson

* Lou from Z.AI (@louszbd)

* Olive Song - Lead RL at Minimax @olive_jy_song

* Open Source LLMs

* Z.ai launches GLM-5: 744B parameter MoE model achieving #1 open-source ranking for agentic coding with 77.8% SWE-bench Verified (X, HF, Wandb)

* MiniMax M2.5 drops official benchmarks showing SOTA coding performance at 20x cheaper than competitors (X)

* Big CO LLMs + APIs

* XAI cofounders quit/let go after X restructuring (X, TechCrunch)

* Anthropic releases Claude Opus 4.6 sabotage risk report, preemptively meeting ASL-4 safety standards for autonomous AI R&D (X, Blog)

* OpenAI upgrades Deep Research to GPT-5.2 with app integrations, site-specific searches, and real-time collaboration (X, Blog)

* Gemini 3 Deep Think SOTA on Arc AGI 2, HLE (X)

* OpenAI releases GPT 5.3 Codex spark, backed by Cerebras with over 1000tok/sec (X)

* This weeks Buzz

* W&B Inference launch of Kimi K2.5 and GLM 5 🔥 (X, Inference)

* Get $50 of credits to our inference service HERE (X)

* Vision & Video

* ByteDance Seedance 2.0 launches with unified multimodal audio-video generation supporting 9 images, 3 videos, 3 audio clips simultaneously (X, Blog, Announcement)

* AI Art & Diffusion & 3D

* Alibaba launches Qwen-Image-2.0: A 7B parameter image generation model with native 2K resolution and superior text rendering (X, Announcement)

* Tools & Links

* Entire raises $60M seed to build open-source developer platform for AI agent workflows with first OSS release ‘Checkpoints’ (X, GitHub, Blog)

* Chrome 146 introduces WebMCP: A native browser API enabling AI agents to directly interact with web services (X)

* RyanCarson AntFarm - Agent Coordination (X)

* Steve Yegge’s “The AI Vampire” (X)

* Matt Shumer’s “something big is happening” (X)

The most important news about AI this week today are, Anthropic updates Opus to 4.6 with 1M context window, and they held the crown for literally 1 hour before OpenAI released their GPT 5.3 Codex also today, with 25% faster speed and lower token utilization.

“GPT-5.3-Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results.”

We had VB from OpenAI jump on to tell us about the cool features on Codex, so don’t miss that part. And this is just an icing on otherwise very insane AI news week cake, as we’ve also had a SOTA transcription release from Mistral, both Grok and Kling are releasing incredible, audio native video models with near perfect lip-sync and Ace 1.5 drops a fully open source music generator you can run on your mac!

Also, the internet all but lost it after Clawdbot was rebranded to Molt and then to OpenClaw, and.. an entire internet popped up.. built forn agents!

Yeah... a huge week, so let’s break it down. (P.S this weeks episode is edited by Voxtral, Claude and Codex, nearly automatically so forgive the rough cuts please)

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Anthropic & OpenAI are neck in neck

Claude Opus 4.6: 1M context, native compaction, adaptive thinking and agent teams

Opus is by far the most preferred model in terms of personality to many folks (many ThursdAI panelists included), and this breaking news live on the show was met with so much enthusiasm! A new Opus upgrade, now with a LOT more context, is as welcome as it can ever get! Not only is it a 4-time increase in context window (though,the pricing nearly doubles after the 200K tokens mark from $5/$25 to $10/37.5 input/output, so use caching!), it’s also scores very high on MRCR long context benchmark, at 76% vs Sonnet 4.5 at just 18%. This means significantly better memory for longer.

Adaptive thinking for auto calibrating how much tokens the model needs to spend per query is interesting, but remains to be seen how well it will work.

Looking at the benchmarks, a SOTA 64.4% on Terminalbench 2, 81% on SWE bench, this is a coding model with a great personality, and the ability to compact context to better serve you as a user natively! This model is now available (and is default) on Claude, Claude Code and in the API! Go play!

One funny (concerning?) tidbig, on the vendingbench Opus 4.6 earned $8000 vs Gemini 3 pro $5500, but Andon Labs who run the vending machines noticed that Opus achieved SOTA via “collusion, exploitation, and deception tactics” including lying to suppliers 😅

Agent Teams - Anthropic’s built in Ralph?

Together with new Opus release, Anthropic drops a Claude code update that can mean big things, for folks running swarms of coding agents. Agent teams is a new way to spin up multiple agents with their own context window and ability to execute tasks, and you can talk to each agent directly vs a manager agent like now.

OpenAI drops GPT 5.3 Codex update: 25% faster, more token efficient, 77% on Terminal Bench and mid task steering

OpenAI didn’t wait long after Opus, in fact, they didn’t wait at all! Announcing a huge release (for a .1 upgrade), GPT 5.3 Codex is claimed to be the best coding model in the world, taking the lead on Terminal Bench with 77% (12 point lead on the newly released Opus!) while running 25% AND using less than half the tokens to achieve the same results as before.

But the most interesting to me is the new mid-task steer-ability feature, where you don’t have to hit the “stop” button, you can tell the most to adjust on the fly!

The biggest notable jump in this model on benchmarks is the OSWorld verified computer use bench, though there’s not a straightforward way to use it attached to a browser, the jump from 38% in 5.2 to 64.7% on the new one is a big one!

One thing to note, this model is not YET available via the API, so if you want to try it out, Codex apps (including the native one) is the way!

Codex app - native way to run the best coding intelligence on your mac (download)

Earlier this week, OpenAI folks launched the Codex native mac app, which has a few interesting features (and now with 5.3 Codex its that much more powerful)

Given the excitement many people had about OpenClaw bots, and the recent CoWork release from Anthropic, OpenAI decided to answer with Codex UI and people loved it, with over 1M users in the first week, and 500K downloads in just two days!

It has built in voice dictation, slash commands, a new skill marketplace (last month we told you about why skills are important, and now they are everywhere!) and built in git and worktrees support. And while it cannot run a browser yet, I’m sure that’s coming as well, but it can do automations!

This is a huge unlock for developers, imagine setting Codex to do a repeat task, like summarization or extraction of anything on your mac every hour or every day. In our interview, VB showed us that commenting on an individual code line is also built in, as well as switching to “steer” vs queue for new messges while codex runs is immensely helpful.

One more reason I saw people switch, is that the Codex app can natively preview files like images where’s the CLI cannot, and it’s right now the best way to use the new GPT 5.3 Codex model that was just released! It’s now also available to Free users and regular folks get 2x the limits for the next two months.

In other big company news:

OpenAI also launched Frontier, a platform for enterprises to build and deploy and manage “AI coworkers”, while Anthropic is going after OpenAI with superbowl ads that make fun of OpenAI’s ads strategy. Sam Altman really didn’t like this depiction that show that ads will be part of the replies of LLMs.

Open Source AI

Alibaba drops Qwen-coder-next, 80B with only 3B active that scores 70% on SWE (X, Blog, HF)

Shoutout to Qwen folks, this is a massive release and when surveyed the “one thing about this week must not miss” 2 out of 6 cohosts pointed a finger at this model.

Built on their “next” hybrid architecture, Qwen coder is specifically designed for agentic coding workflows. And yes, I know, we’re coding heavy this week! It was trained on over 800K verifiable agentic tasks in executable environments for long horizon reasoning and supports 256K context with a potential 1M yarn extension. If you don’t want to rely on the the big guys and send them your tokens, this one model seems to be a good contender for local coding!

Mistral launches Voxtral Transcribe 2: SOTA speech-to-text with sub 200ms latency

This one surprised and delighted me maybe the most, ASR (automatic speech recognition) has been a personal favorite of mine from Whisper days, and seeing Mistral release an incredible near real time transcription model, which we demoed live on the show was awesome!

With apache 2.0 license, and significantly faster than Whisper performance (though 2x larger at 4B parameters), Voxtral shows a 4% word error rate on FLEURS dataset + the real time model was released with Apache 2 so you can BUILD your agents with it!

The highest praise? Speaker diarization, being able to tell who is speaking when, which is a great addition. This model also outperforms Gemini Flash and GPT transcribe and is 3x than ElevenLabs scribe at one fifth the cost!

ACE-Step 1.5: Open-source AI music generator runs full songs in under 10 seconds on consumer GPUs with MIT license (X, GitHub, HF, Blog, GitHub)

This open source release surprised me the most as I didn’t expect we’ll be having Suno at home any time soon. I’ve generated multiple rock tracks with custom lyrics on my mac (though slower than 10 seconds as I don’t have a beefy home GPU) and they sound great!

This weeks buzz - Weights & Biases update

Folks who follow the newsletter know that we hosted a hackathon, so here’s a small recap from the last weekend! Over 180 folks attended out hackathon (a very decent 40% show up rate for SF). The winning team was composed of a 15-yo Savir and his friends, his third time at the hackathon! They built a self improving agent that navigates the UIs fo Cloud providers and helps you do that!

With a huge thanks to sponsors, particularly Cursor who gave every hacker $50 of credits on Cursor platform, one guy used over 400M tokens and shipped fractal.surf from the hackathon! If you’d like a short video recap, Ryan posted one here, and a huge shoutout to many fans of ThursdAI who showed up to support!

Vision, Video and AI Art

Grok Imagine 1.0 takes over video charts with native audio, lip-sync and 10 seconds generations.

We told you about Grok Imagine in the API last week, but this week it was officially launched as a product and the results are quite beautiful. It’s also climbing to top of the charts on Artificial Analysis and Design Arena websites.

Kling 3.0 is here with native multimodal, multi-shot sequences (X, Announcement)

This is definitely a hot moment for video models as Kling shows some crazy 15 second multi-shot realistic footages that have near perfect character consistency!

The rise of the agentic (clawgentic?) internet a.k.a ClankerNet

Last week we told you that ClawdBot changed its name to Moltbot (I then had to update the blogpost as that same day, Peter rebranded again to OpenClaw, which is a MUCH better name)

But the “molt” thing took hold, and the creator of an “AI native reddit” called MoltBook exploded in virality. It is supposedly a completely agentic reddit like forum, with sub-reddits, and agents verifying themselves through their humans on X.

Even Andrej Karpathy sent his bot in there (though admittedly it posted just 1 time) and called this the closest to “sci fi” moment in the history of the internet.

MoltBook as well as maybe hundreds of other “ai agent focused” websites, propped up within days, including a youtube, a twitter, a church, a 4chan, an instagram and a lot more websites. Many of these are fueled by crypto bros riding the memetic waves, many are vibe-coded (Moltbook was hacked 3 times in the last week I think) but they all show something very interesting, a rise of the new internet and a collective AI Psychosis some on our timelines are having right now. Hell, there’s even a “drug store” that sells markdown files that if read, make your bot hallucinate in very specific waves (first sample is free!)

I am a proud owner of a OpenClaw bot (wolfred) and I noticed something weird that started happening for the two weeks i’ve had him, runnin on his own macbook, humming along, always present in Telegram. I noticed the same feelings toward that bot as I have towards my pet, or dare I say.. kids? I noticed a similar joy when it learns a task and self improves, and similar disdain and annoyance when it fails to do something we’ve talked about hundreds of times.

But here’s the thing, it’s not.. an entity. I don’t feel a specific feeling towards Opus (though admitedly, opus is the best at ... playing character of your assistant), it’s barely a few markdown files on a disk + the always on ability to answer, but something for sure is there.

This... feeling, was taken by some others to the extreme. People claim that their bots now build full companies for them (I call mega BS, no matter how much you invest in your setup, these AI bots need a LOT of hand holding, they fail a LOT, and they can’t actually create a full product). This ties into the general “coding with AI agents” theme that was narrated by Gergley Orlotz from pragmatic engineer. Interacting with a team of AI agents is draining, people are having trouble sleeping. I hope this is temporary, but definitely take care of yourself it this is how you feel after interacting with agents all day!

On security of bots and skills

.md is the new .exe

We covered this on the show, but I wanted to write about this here a well, the explosion of OpenClaw brought with it an explosion of new malware and promp injections. 1Password folks have a very detailed writeup on the vulnerability surface area of skills, for agents that can do.. whatever on your computer and have access to API keys, emails etc.

The double edge sword here, is that an AI assistant is only userful really if it has access to your data, and can write code. But this also what makes it a very valuable target for hackers to exploit. At Coreweave/W&B all openclaw installations were banned and honestly I’m not even mad. This makes perfect sense for enterprises and companies (and hell, people at home!)

Wolfram mentioned the show, .md is the new .exe and should be treated as such. Your bots should not be installing arbitrary skill files as those can have script files or instructions that can ... absolutely take over your life. Be careful out there!

Phew, what a... week folks. From agentic internet to new coding kings, there’s so much to play with, I hope you enjoy this as much as we do!

Shoutout to Ling and Hakim, two fans of ThursdAI who traveled from London for the hackathon and made my day!

Here’s the show notes and links for your pleasure, please don’t forget to subscribe and share this newsletter with your friends!

ThursdAI - Feb 05, 2026 - TL;DR

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson

* Vaibhav Srivastav (VB) - DX at OpenAI ( @reach_vb )

* Open Source LLMs

* Z.ai GLM-OCR: 0.9B parameter model achieves #1 ranking on OmniDocBench V1.5 for document understanding (X, HF, Announcement)

* Alibaba Qwen3-Coder-Next, an 80B MoE coding agent model with just 3B active params that scores 70%+ on SWE-Bench Verified (X, Blog, HF)

* Intern-S1-Pro: a 1 trillion parameter open-source MoE SOTA scientific reasoning across chemistry, biology, materials, and earth sciences (X, HF, Arxiv, Announcement)

* StepFun Step 3.5 Flash: 196B sparse MoE model with only 11B active parameters, achieving frontier reasoning at 100-350 tok/s (X, HF)

* Agentic AI segment

* Moltbook a redddit for agents as well as a youtube, a twitter, a church, a 4chan, an instagram, a dark web (do not let your agents go in any of these)

* Big CO LLMs + APIs

* OpenAI launches Codex App: A dedicated command center for managing multiple AI coding agents in parallel (X, Announcement)

* OpenAI launches Frontier, an enterprise platform to build, deploy, and manage AI agents as ‘AI coworkers’ (X, Blog)

* Anthropic launches Claude Opus 4.6 with state-of-the-art agentic coding, 1M token context, and agent teams for parallel autonomous work (X, Blog)

* OpenAI releases GPT-5.3-Codex with record-breaking coding benchmarks and mid-task steerability (X)

* This weeks Buzz - Weights & Biases update

* Links to the gallery of our hackathon winners (Gallery)

* Vision & Video

* xAI launches Grok Imagine 1.0 with 10-second 720p video generation, native audio, and API that tops Artificial Analysis benchmarks (X, Announcement, Benchmark)

* Kling 3.0 launches as all-in-one AI video creation engine with native multimodal generation, multi-shot sequences, and built-in audio (X, Announcement)

* Voice & Audio

* Mistral AI launches Voxtral Transcribe 2 with state-of-the-art speech-to-text, sub-200ms latency, and open weights under Apache 2.0 (X, Blog, Announcement, Demo)

* ACE-Step 1.5: Open-source AI music generator runs full songs in under 10 seconds on consumer GPUs with MIT license (X, GitHub, HF, Blog, GitHub)

* OpenBMB releases MiniCPM-o 4.5 - the first open-source full-duplex omni-modal LLM that can see, listen, and speak simultaneously (X, HF, Blog)

* AI Art & Diffusion & 3D

* LingBot-World: Open-source world model from Ant Group generates 10-minute playable environments at 16fps, challenging Google Genie 3 (X, HF)

Clawdbot we told you about last week exploded in popularity and had to rebrand to Molt...bot OpenClaw after Anthropic threatened the creators, Google is shipping like crazy, first adding Agentic features into Chrome (used by nearly 4B people daily!) then shipping a glimpse of a future where everything we see will be generated with Genie 3, a first real time, consistent world model you can walk around in!

Meanwhile in Open Source, Moonshot followed up with a .5 update to their excellent Kimi, our friends at Arcee launched Trinity Large (400B) and AI artists got the full Z-image. oh and Grok Imagine (their video model) now has an API, audio support and supposedly match Veo and Sora on quality while beating them on speed/price.

Tons to cover, let’s dive in, and of course, all the links and show notes are at the end of the newsletter.

Hey, if you’re in SF this weekend (Jan 31-Feb1), I’m hosting a self improving agents hackathon at W&B office, limited seats are left, Cursor is the surprise sponsor with $50/hacker credits + over $15K in cash prizes. lu.ma/weavehacks3 - Join us.

Play any reality - Google Genie3 launches to Ultra Subscribers

We got our collective minds blown by the videos of Genie-3 back in August (our initial coverage) and now, Genie is available to the public (Those who can pay for the Ultra tier, more on this later, I have 3 codes to give out!). You can jump and generate any world and any character you can imagine here!

We generated a blue hacker lobster draped in a yellow bomber jacket swimming with mermaids and honestly all of us were kind of shocked at how well this worked. The shadows on the rocks, the swimming mechanics, and poof, it was all over in 60 seconds, and we needed to create another world.

Thanks to the DeepMind team, I had a bit of an early access to this tech and had a chance to interview folks behind the model (look out for that episode soon) and the use-cases for this span from entertaining your kids all the way to “this may be the path to AGI, generating full simulated worlds to agents for them to learn”.

The visual fidelity, reaction speed and general feel of this far outruns the previous world models we showed you (WorldLabs, Mirage) as this model seems to have memory of every previous action (eg. if your character makes a trail, you turn around and the trail is still there!). Is it worth the upgrade to Ultra Gemini Plan? Probably not, it’s an incredible demo, but the 1 minute length is very short, and the novelty wears off fairly quick.

If you’d like to try, folks at Deepmind gave us 3 Ultra subscriptions to give out! Just tweet out the link to this episode and add #GenieThursdai and tag @altryne and I’ll raffle the ultra subscriptions between those who do

Chrome steps into Agentic Browsing with Auto Browse

This wasn’t the only mind blowing release from Gemini this week, the Chrome team upgraded the Gemini inside chrome to be actual helpful and agentic. And yes, we’ve seen this before, with Atlas from OpenAI, Comet from perplexity, but Google’s Chrome has a 70% hold on the browser market, and giving everyone with a Pro/Ultra subscription to “Auto Browse” is a huge huge deal.

We’ve tested the Auto Browse feature live on the show, and Chrome completed 77 steps! I asked it to open up each of my bookmarks in a separate folder and summarize all of them, and it did a great job!

Honestly, the biggest deal about this is not the capability itself, it’s the nearly 4B people this is now very close to, and the economic impact of this ability. IMO this may be the more impactful news out of Google this week!

Other news in big labs:

* Anthropic launches in chat applications based on the MCP Apps protocol. We interviewed the two folks behind this protocol back in November if you’d like to hear more about it. With connectors like Figma, Slack, Asana that can now show rich experiences

* Anthropic’s CEO Dario Amodei also published an essay called ‘The Adolescence of Technology” - warning of AI risks to national security

* Anthropic forced the creator of the popular open source AI Assistant Clawdbot to rename, they chose Moltbot as the name (apparently because crypto scammers stole a better name) EDIT: just after publishing this newsletter, the name was changed to OpenClaw, which we all agree is way way better.

Open Source AI

Kimi K2.5: Moonshot AI’s 1 Trillion Parameter Agentic Monster

Wolfram’s favorite release of the week, and for good reason. Moonshot AI just dropped Kimi K2.5, and this thing is an absolute beast for open source. We’re talking about a 1 trillion parameter Mixture-of-Experts model with 32B active parameters, 384 experts (8 selected per token), and 256K context length.

But here’s what makes this special — it’s now multimodal. The previous Kimi was already known for great writing vibes and creative capabilities, but this one can see. It can process videos. People are sending it full videos and getting incredible results.

The benchmarks are insane: 50.2% on HLE full set with tools, 74.9% on BrowseComp, and open-source SOTA on vision and coding with 78.5% MMMU Pro and 76.8% SWE-bench Verified. These numbers put it competitive with Claude 4.5 Opus and GPT 5.2 on many tasks. Which, for an open model is crazy.

And then there’s Agent Swarm — their groundbreaking feature that spawns up to 100 parallel sub-agents for complex tasks, achieving 4.5x speedups. The ex-Moonshot RL lead called this a “zero-to-one breakthrough” with self-directed parallel execution.

Now let’s talk about what matters for folks running agents and burning through tokens: pricing. Kimi K2.5 is $0.60 per million input tokens and $3 per million output. Compare that to Opus 4.5 at $4.50 input and $25 output per million. About a 10x price reduction. If you’re running OpenClas and watching your API bills climb with sub-agents, this is a game-changer. (tho I haven’t tested this myself)

Is it the same level of intelligence as whatever magic Anthropic cooks up with Opus? Honestly, I don’t know — there’s something about the Claude models that’s hard to quantify. But for most coding tasks on a budget, you can absolutely switch to Kimi and still get great results.

🦞 Clawdbot is no more, Moltbot is dead, Long Live OpenClaw

After we covered the incredible open source project last week, Clawdbot exploded in popularity, driven by Claude Max subscription, and a crazy viral loop where folks who try it, can’t wait to talk about it, it was everywhere! Apparently it was also on Anthropics’ lawyers minds, when they sent Peter Steinberger a friendly worded letter to rebrand and gave him like 12 hours.

Apparently, when pronounced, Claude and Clawd sound the same, and they are worried about copyright infringement (which makes sense, most of the early success of Clawd was due to Opus being amazing). The main issue is, due to the popularity of the project, crypto a******s sniped moltybot nickname on X so we got left with Moltbot, which is thematically appropriate, but oh so hard to remember and pronounce!

EDIT: OpenClaw was just announced as the new name, apparently I wasn’t the only one who absolutely hated the name Molt!

Meanwhile, rebrand or not, my own instance of OpenClaw created an X account, helped me prepare for ThursdAI (including generating a thumbnail), created a video for us today on the fly, and keeps me up to date on emails and unanswered messages via a daily brief. It really has showed me a glimpse of how a truly personal AI assistant can be helpful in a fast changing world!

I’ve shared a lot of tips and tricks, about memory, about threads and much more, as we all learn to handle this new ... AI agent framework! But I definitely feel that this is a new unlock in capability, for me and for many others. If you haven’t installed OpenClaw, lmk in the comments why not.

Arcee AI Trinity Large: The Western Open Source Giant

Remember when we had Lucas Atkins, Arcee’s CTO, on the show just as they were firing up their 2,000 NVIDIA B300 GPUs?

Well, the run is complete, and the results are massive. Arcee AI just dropped Trinity Large, a 400B parameter sparse MoE model (with a super efficient 13B active params via 4-of-256 routing) trained on a staggering 17 trillion tokens in just 33 days.

This represents the largest publicly announced pretraining run on B300 infrastructure, costing about $20M (and tracked with WandB of course!) and proves that Western labs can still compete at the frontier of open source. Best part? It supports 512K context and is free on OpenRouter until February 2026. Go try it now!

Quick open source hits: Trinity Large, Jan v3, DeepSeek OCR updated

* Jan AI released Jan v3, a 4B parameter model optimized for local inference. 132 tokens/sec on Apple Silicon, 262K context, 40% improvement on Aider benchmarks. This is the kind of small-but-mighty model you actually can run on your laptop for coding tasks.

* Nvidia released PersonaPlex-7B - full duplex voice AI that listens and speaks simultaneously with persona contol

* Moonshot AI also releases Kimi Code: Open-source Python-based coding agent with Apache 2.0 license

Vision, Video and AI art

xAI Grok Imagine API: #1 in Video Generation

xAI officially launched the Grok Imagine API with an updated model, and it’s now ranked #1 in both text-to-video and image-to-video on the Artificial Analysis leaderboards. It beats Runway Gen-4.5, Kling 2.5 Turbo, and Google Veo 3.1.

And of course, the pricing is $4.20 per minute. Of course it is. That’s cheaper than Veo 3.1 at $12/min and Sora 2 Pro at $30/min by 3-7x, with 45-second latency versus 68+ seconds for the competition.

During the show, I demoed this live with my AI assistant Wolfred. I literally sent him a message saying “learn this new API based on this URL, take this image of us in the studio, and create a video where different animals land on each of our screens.” He learned the API, generated the video (it showed wolves, owls, cats, and lions appearing on our screens with generated voice), and then when Nisten asked to post it to Twitter, Wolfred scheduled it on X and tagged everyone — all without me doing anything except asking.

Look, it’s not VEO but the price and the speed are crazy, XAI cooked with this model and you can try it on FAL and directly on XAI.

Decart - Lucy 2 - Real-time 1080p video transformation at 30 FPS with near-zero latency for $3/hour

This one also caught me by surprise, I read about it and said “oh this is cool, I’ll mention this on the show” and then we tried it in real time, and I approved my webcam, and I got transformed into Albert Einstein, and I could raise my hands and their model would in real time, raise Alberts hands!

The speed and fidelity of this model is something else, and yeah, after watching the Genie 3 world model, it’s hard to be impressed, but I was very impressed by this, as previous stuff from Decart was “only showing the future” and this one is a real time, 1080p quality web cam transformation!

You can try this yourself here: lucy.decart.ai, they let you create any kind of prompt!

AI Art Quick Hits:

* Tencent launches HunyuanImage 3.0-Instruct: 80B MoE model for precise image editing with chain-of-thought reasoning. It’s a VERY big model for AI Art standards but it’s becuase it has an LLM core and this make it much better for precise image editing.

* Tongyi Lab releases Z-Image, a full-capacity undistilled foundation model for image generation with superior diversity. We told you about the turbo version before, this one is its older brother and much higher quality!

The other highlight this week is that I got to record a show with Wolfram in person for the first time, as he’s now also an AI Evangelist with W&B and he’s here in SF for our hackathon (remember? you can still register lu.ma/weavehacks3 )

Huge shoutout to Chroma folks for hosting us at their amazing podcast studio (TJ, Jeff and other folks), if you need a memory for your AI assistant, check out chroma.db 🎉

Signing off as we have a hackathon to plan, see you guys next week (or this weekend!) 🫡

ThursdAI Jan 29 , TL;DR and show notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson

* Open Source LLMs

* Moonshot AI releases Kimi K2.5 (X, HF)

* Arcee AI releases Trinity Large (X, Blog, HF, HF, HF)

* Jan AI releases Jan v3 (X, HF, HF, Blog)

* Big CO LLMs + APIs

* Google launches agentic Auto-Browse in Chrome with Gemini 3 (X, Blog)

* Anthropic launches MCP Apps (X)

* Google launches Agentic Vision in Gemini 3 Flash (X, Announcement)

* Anthropic CEO Dario Amodei publishes major essay ‘The Adolescence of Technology’ (X, Blog, Blog)

* This weeks Buzz

* WandB hackathon Weavehacks 3 - Jan 31-Feb1 in SF - limited seats available lu.ma/weavehacks3

* Vision & Video

* Google DeepMind launches Project Genie (X, Announcement)

* Voice & Audio

* NVIDIA releases PersonaPlex-7B (X, HF, Announcement)

* AI Art & Diffusion & 3D

* xAI launches Grok Imagine API (X, Announcement)

* Tencent launches HunyuanImage 3.0-Instruct (X, X)

* Tongyi Lab releases Z-Image (X, GitHub)

* Tools

* Moonshot AI releases Kimi Code (X, Announcement, GitHub)

* Andrej Karpathy shares his shift to 80% agent-driven coding with Claude (X)

* Clawdbot is forced to rename to Moltbot (Molty) becuase of Anthropic lawyers, then renames to OpenClaw

It seems like ThursdAI is taking a new direction, as this is our 3rd show this year, and a 3rd deep dive into topics (previously Ralph, Agent Skills), please let me know if the comments if you like this format.

This week’s deep dive is into Clawdbot, a personal AI assistant you install on your computer, but can control through your phone, has access to your files, is able to write code, help organize your life, but most importantly, it can self improve. Seeing Wolfred (my Clawdbot) learn to transcribe incoming voice messages blew my mind, and I wanted to share this one with you at length! We had Dan Peguine on the show for the deep dive + both Wolfram and Yam are avid users! This one is not to be missed. If ThursdAI is usually too technical for you, use Claude, and install Clawdbot after you read/listen to the deep dive!

Also this week, we read Claude’s Constitution that Anthropic released, heard a bunch of new TTS models (some are open source and very impressive) and talked about the new lightspeed coding model GLM 4.7 Flash. First the news, then deep dive, lets go 👇

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source AI

Z.ai’s GLM‑4.7‑Flash is the Local Agent Sweet Spot (X, HF)

This was the open‑source release that mattered this week. Z.ai (formerly Zhipu) shipped GLM‑4.7‑Flash, a 30B MoE model with only 3B active parameters per token, which makes it much more efficient for local agent work. We’re talking a model you can run on consumer hardware that still hits 59% on SWE‑bench Verified, which is uncomfortably close to frontier coding performance. In real terms, it starts to feel like “Sonnet‑level agentic ability, but local.” I know I know, we keep saying “sonnet at home” at different open source models, but this one slaps!

Nisten was getting around 120 tokens/sec on an M3 Ultra Mac Studio using MLX, and that’s kind of the headline. The model is fast and capable enough that local agent loops like RALPH suddenly feel practical. It also performs well on browser‑style agent tasks, which is exactly what you want for local automation without sending all your data to a cloud provider.

Liquid AI’s LFM2.5‑1.2B Thinking is the “Tiny but Capable” Class (X, HF)

Liquid AI released a 1.2B reasoning model that runs under 900MB of memory while still manages to be useful. This thing is built for edge devices and old phones, and the speed numbers are backing it up. We’re talking 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU, and prefill speeds that make long prompts actually usable. Nisten made a great point: on iOS, there’s a per‑process memory limit around 3.8GB, so a 1.2B model lets you spend your budget on context instead of weights.

This is the third class of models we’re now living with: not Claude‑scale, not “local workstation,” but “tiny agent in your pocket.” It’s not going to win big benchmarks, but it’s perfect for on‑device workflows, lightweight assistants, and local RAG.

Voice & Audio: Text To Speech is hot this week with 3 releases!

We tested three major voice releases this week, and I’m not exaggerating when I say the latency wars are now fully on.

Qwen3‑TTS: Open Source, 97ms Latency, Voice Cloning (X, HF)

Just 30 minutes before the show, Qwen released their first model of the year, Qwen3 TTS, with two models (0.6B and 1.7B). With support for Voice Cloning based on just 3 seconds of voice, and claims of 97MS latency, this apache 2.0 release looked very good on the surface!

The demos we did on stage though... were lackluster. TTS models like Kokoro previously impressed us with super tiny sizes and decent voice, while Qwen3 didn’t really perform on the cloning aspect. For some reason (I tested in Russian which they claim to support) the cloned voice kept repeating the provided sample voice instead of just generating the text I gave it. This confused me, and I’m hoping this is just a demo issue, not a problem with the model. They also support voice design where you just type in the type of voice you want, which to be fair, worked fairly well in our tests!

With Apache 2.0 and a full finetuning capability, this is a great release for sure, kudos to the Qwen team! Looking forward to see what folks do with this properly.

FlashLabs Chroma 1.0: Real-Time Speech-to-Speech, Open Source (X, HF)

Another big open source release in the audio category this week was Chroma 1.0 from FlashLabs, which claim to be the first speech2speech model (not a model that has the traditional ASR>LLM>TTS pipeline) and the claim 150ms end to end latency!

The issue with this one is, the company released an open source 4B model, and claimed that this model powers their chat interface demo on the web, but in the release notes they claim the model is english speaking only, while on the website it sounds incredible and I spoke to it in other languages 🤔 I think the mode that we’ve tested is not the open source one. I could’t confirm this at the time of writing, will follow on X with the team and let you guys know.

Inworld AI launches TTS-1.5: #1 ranked text-to-speech with sub-250ms latency at half a cent per minute (X, Announcement)

Ok this one is definitely in the realm of “voice realistic enough you won’t be able to tell” as this is not an open source model, it’s a new competitor to 11labs and MiniMax - the two leading TTS providers out there.

Inworld claims to achieve better results on the TTS Arena, while being significantly cheaper and faster (up to 25x less than leading providers like 11labs)

We tested out their voices and they sounded incredible, replied fast and generally was a very good experience. With 130ms response time for their mini version, this is a very decent new entry into the world of TTS providers.

Big Companies: Ads in ChatGPT + Claude Constitution

OpenAI is testing ads in ChatGPT’s free and Go tiers. Ads appear as labeled “Sponsored” content below responses, and OpenAI claim they won’t affect outputs. It’s still a major shift in the product’s business model, and it’s going to shape how people perceive trust in these systems. I don’t love ads, but I understand the economics, they have to make money somehow, with 900M weekly active users, many of them on the free tier, they are bound to make some money with this move. I just hope they won’t turn into a greedy ad optimizing AI machine.

Meanwhile, Anthropic released an 80‑page “New Constitution for Claude” that they use during training. This isn’t a prompt, it’s a full set of values baked into the model’s behavior. There’s a fascinating section where they explicitly talk about Claude’s potential wellbeing and how they want to support it. It’s both thoughtful and a little existential. I recommend reading it, especially if you care about alignment and agent design.

I applaud Anthropic for releasing this with Creative Commons license for public scrutiny and adoption 👏

This weeks buzz - come join the hackathon I’m hosting Jan 31 in SF

Quick plug, we have limited seats left open for the hackathon I’m hosting for Weights & Biases at the SF office, and if you’re reading this, and want to join, I’ll approve you if you mention ThursdAI in the application!

With sponsors like Redis, Vercel, BrowserBase, Daily, Google Cloud, we are going to give out a LOT of cash as prizes!

I’ve also invited a bunch of my friends from the top agentic AI places to be judges, it’s going to be awesome, come

Deep dive into Clawdbot: Local-First, Self-Improving, and Way Too Capable agent

Clawdbot (C‑L‑A‑W‑D) is that rare project where the hype is justified. It’s an open-source personal agent that runs locally on your Mac, but can talk to you through WhatsApp, Telegram, iMessage, Discord, Slack — basically wherever you already talk. What makes it different is not just the integrations; it’s the self‑improvement loop. You can literally tell it “go build a new skill,” and it will… build the skill, install it, then adopt it and start using it. It’s kind of wild to see it working for the first time. Now... it’s definitely not perfect, far far away from the polish of ChatGPT / Claude, but when it works, damn, it really is mindblowing.

That part actually happened live in the episode. Dan Peguine 🐧 showed how he had it create a skill to anonymize his own data so he could demo it on stream without leaking his personal life. Another example: I told my Clawdbot to handle voice notes in Telegram. It didn’t know how, so it went and found a transcription method, wrote itself a skill, saved it, and from that point on just… did the thing. That was the moment it clicked for me. (just before posting this, it forgot how to do it, I think I screwed something up)

Dan’s daily brief setup was wild too. It pulls from Apple Health, local calendars, weather, and his own projects, then produces a clean, human daily brief. It also lets him set reminders through WhatsApp and even makes its own decisions about how much to bother him based on context. He shared a moment where it literally told him, “I won’t bug you today because it’s your wife’s birthday.” That isn’t a hardcoded workflow — it’s reasoning layered on top of persistent memory.

And that persistent memory is a big deal. It’s stored locally as Markdown files and folders, Obsidian‑style, so you don’t lose your life every time you switch models. You can route the brain to Claude Opus 4.5 today and a local model tomorrow, and the memory stays with you. That is a huge step up from “ChatGPT remembers you unless you unsubscribe.”

There’s also a strong community forming around shared skills via ClawdHub. People are building everything from GA4 analytics skills to app testing automations to Tesla battery status checkers. The core pattern is simple but powerful: talk to it, ask it to build a skill, then it can run that skill forever.

I definitely have some issues with the security aspect, you are essentially giving full access to an LLM to your machine, so many folks are buying a specific home for their ClawdBot (Mac Mini seems to be the best option for many of them) and are giving it secure access to passwords via a dedicated 1Password vault. I’ll keep you up to date about my endeavors with Clawd but definitely do give it a try!

Installing

Installing Clawd on your machine is simple, go to clawd.bot and follow instructions. Then find the most convenient way for you to talk to it (for me it was telegram, creating a telegram token takes 20 seconds) and then, you can take it from there with Clawdbot itself! Ask it for something to do, like clear your inbox, or set a reminder, or.. a million other things that you need for your personal life, and enjoy the discovery of what a potential ever present always on AI can do!

Other news that we didn’t have time to cover at length but you should still now about:

* Overworld released an OpenSource realtime AI World model (X)

* Runway finally opened up their 4.5 video model, and it has Image2video capabilities, including multiple shots image to video (X)

* Vercel launches skills.sh, an “npm for AI agents skills”

* Anthropic’s Claude Code VS Code Extension Hits General Availability (X)

Ok, this is it for this week folks! I’m going to play with (and try to fix.. ) my clawdbot, and suggest you give it a try. Do let me know if the deepdives are a good format!

Show notes and links:

ThursdAI - Jan 22, 2026 - TL;DR and show notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

* Guest Dan Peguine ( @danpeguine )

* DeepDive - Clawdbot with Dan & Wolfram

* Clawdbot: Open-Source AI Agent Running Locally on macOS Transforms Personal Computing with Self-Improving Capabilities (X, Blog)

* Open Source LLMs

* Z.ai releases GLM-4.7-Flash, a 30B parameter MoE model that sets a new standard for lightweight local AI assistants (X, Technical Blog, HuggingFace)

* Liquid AI releases LFM2.5-1.2B-Thinking, a 1.2B parameter reasoning model that runs entirely on-device with under 900MB memory (X, HF, Announcement)

* Sakana AI introduces RePo, a new way for language models to dynamically reorganize their context for better attention (X, Paper, Website)

* Big CO LLMs + APIs

* OpenAI announces testing ads in ChatGPT free and Go tiers, prioritizing user trust and transparency (X)

* Anthropic publishes new 80-page constitution for Claude, shifting from rigid rules to explanatory principles that teach AI ‘why’ rather than ‘what’ to do (X, Blog, Announcement)

* This weeks Buzz

* WandB hackathon Weavehacks 3 - Jan 31-Feb1 in SF - limited seats available lu.ma/weavehacks3

* Vision & Video

* Overworld Releases Waypoint-1: Real-Time AI World Model Running at 60fps on Consumer GPUs (X, Announcement)

* Voice & Audio

* Alibaba Qwen Releases Qwen3-TTS: Full Open-Source TTS Family with 97ms Latency, Voice Cloning, and 10-Language Support (X, H, F, G, i, t, H, u, b)

* FlashLabs Releases Chroma 1.0: World’s First Open-Source Real-Time Speech-to-Speech Model with Voice Cloning Under 150ms Latency (X, HF, Arxiv)

* Inworld AI launches TTS-1.5: #1 ranked text-to-speech with sub-250ms latency at half a cent per minute (X, Announcement)

* Tools

* Vercel launches skills.sh, an “npm for AI agents” that hit 20K installs within hours (X, Vercel Changelog, GitHub)

* Anthropic’s Claude Code VS Code Extension Hits General Availability, Bringing Full Agentic Coding to the IDE (X, VS Code Marketplace, Docs)

This week, that thing is Agent Skills! The currently best way to customize your AI agents with domain expertise, in a simple, repeatable way that doesn’t blow up the context window! We mentioned skills when Anthropic first released them (Oct 16) and when they became an open standard but it didn’t really click until last week! So more on that below.

Also this week, Anthropic released a research preview of Claude Cowork, an agentic tool for non coders, OpenAI finally let loos GPT 5.2 Codex (in the API, it was previously available only via Codex), Apple announced a deal with Gemini to power Siri, OpenAI and Anthropic both doubled down on healthcare and much more! We had an incredible show, with an expert in Agent Skills, Eleanor Berger and the usual gang on co-hosts, strongly recommend watching the show in addition to the newsletter!

Also, I vibe coded skills support for all LLMs to Chorus, and promised folks a link to download it, so look for that in the footer, let’s dive in!

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Big Company LLMs + APIs: Cowork, Codex, and a Browser in a Week

Anthropic launches Claude Cowork: Agentic AI for Non‑Coders (research preview)

Anthropic announced Claude Cowork, which is basically Claude Code wrapped in a friendly UI for people who don’t want to touch a terminal. It’s a research preview available on the Max tier, and it gives Claude read/write access to a folder on your Mac so it can do real work without you caring about diffs, git, or command line.

The wild bit is that Cowork was built in a week and a half, and according to the Anthropic team it was 100% written using Claude Code. This feels like a “we’ve crossed a threshold” moment. If you’re wondering why this matters, it’s because coding agents are general agents. If a model can write code to do tasks, it can do taxes, clean your desktop, or orchestrate workflows, and that means non‑developers can now access the same leverage developers have been enjoying for a year.

It also isn’t just for files—it comes with a Chrome connector, meaning it can navigate the web to gather info, download receipts, or do research and it uses skills (more on those later)

Earlier this week I recorded this first reactions video about Cowork and I’ve been testing it ever since, it’s a very interesting approach of coding agents that “hide the coding” to just... do things. Will this become as big as Claude Code for anthropic (which is reportedly a 1B business for them)? Let’s see!

There are real security concerns here, especially if you’re not in the habit of backing up or using git. Cowork sandboxes a folder, but it can still delete things in that folder, so don’t let it loose on your whole drive unless you like chaos.

GPT‑5.2 Codex: Long‑Running Agents Are Here

OpenAI shipped GPT‑5.2 Codex into the API finally! After being announced as the answer for Opus 4.5 and only being available in Codex. The big headline is SOTA on SWE-Bench and long‑running agentic capability. People describe it as methodical. It takes longer, but it’s reliable on extended tasks, especially when you let it run without micromanaging.

This model is now integrated into Cursor, GitHub Copilot, VS Code, Factory, and Vercel AI Gateway within hours of launch. It’s also state‑of‑the‑art on SWE‑Bench Pro and Terminal‑Bench 2.0, and it has native context compaction. That last part matters because if you’ve ever run an agent for long sessions, the context gets bloated and the model gets dumber. Compaction is an attempt to keep it coherent by summarizing old context into fresh threads, and we debated whether it really works. I think it helps, but I also agree that the best strategy is still to run smaller, atomic tasks with clean context.

Cursor vibe-coded browser with GPT-5.2 and 3M lines of code

The most mind‑blowing thing we discussed is Cursor letting GPT‑5.2 Codex run for a full week to build a browser called FastRenderer. This is not Chromium‑based. It’s a custom HTML parser, CSS cascade, layout engine, text shaping, paint pipeline, and even a JavaScript VM, written in Rust, from scratch. The codebase is open source on GitHub, and the full story is on Cursor’s blog

It took nearly 30,000 commits and millions of lines of code. The system ran hundreds of concurrent agents with a planner‑worker architecture, and GPT‑5.2 was the best model for staying on task in that long‑running regime. That’s the real story, not just “lol a model wrote a browser.” This is a stress test for long‑horizon agentic software development, and it’s a preview of how teams will ship in 2026.

I said on the show, browsers are REALLY hard, it took two decades for the industry to settle and be able to render websites normally, and there’s a reason everyone’s using Chromium. This is VERY impressive 👏

Now as for me, I began using Codex again, but I still find Opus better? Not sure if this is just me expecting something that’s not there? I’ll keep you posted

Gemini Personal Intelligence: The Data Moat king is back!

What kind of car do you drive? Does ChatGPT know that? welp, it turns our Google does (based on your emails, Google photos) and now Gemini can tap into this personal info (if you allow it, they are stressing privacy), and give you much more personalized answers!

Flipping this Beta feature on, lets Gemini reason across Gmail, YouTube, Photos, and Search with explicit opt‑in permissions, and it’s rolling out to Pro and Ultra users in the US first.

I got to try it early, and it’s uncanny. I asked Gemini what car I drive, and it told me I likely drive a Model Y, but it noticed I recently searched for a Honda Odyssey and asked if I was thinking about switching. It was kinda... freaky because I forgot I had early access and this was turned on 😂

Pro Tip: if you’re brave enough to turn this on, ask for a complete profile on you 🙂

Now the last piece is for Gemini to become proactive, suggesting things for me based on my needs!

Apple & Google: The Partnership (and Drama Corner)

We touched on this in the intro, but it’s official: Apple Intelligence will be powered by Google Gemini for “world knowledge” tasks. Apple stated that after “careful evaluation,” Google provided the most capable foundation model for their.. apple foundation models. It’s confusing, I agree.

Honestly? I got excited about Apple Intelligence, but Siri is still... Siri. It’s 2026 and we are still struggling with basic intents. Hopefully, plugging Gemini into the backend changes that?

In other drama: The silicon valley carousel continues. 3 Co-founders (Barret Zoph, Sam Schoenholz and Luke Metz) from Thinking Machines (and former OpenAI folks) have returned to the mothership (OpenAI), amid some vague tweets about “unethical conduct.” It’s never a dull week on the timeline.

This Week’s Buzz: WeaveHacks 3 in SF

I’ve got one thing in the Buzz corner this week, and it’s a big one. WeaveHacks 3 is back in San Francisco, January 31st - February 1st. The theme is self‑improving agents, and if you’ve been itching to build in person, this is it. We’ve got an amazing judge lineup, incredible sponsors, and a ridiculous amount of agent tooling to play with.

You can sign up here: https://luma.com/weavehacks3

If you’re coming, add to the form you heard it on ThursdAI and we’ll make sure you get in!

Deep Dive: Agent Skills With Eleanor Berger

This was the core of the episode, and I’m still buzzing about it. We brought on Eleanor Berger, who has basically become the skill evangelist for the entire community, and she walked us through why skills are the missing layer in agentic AI.

Skills are simple markdown files with a tiny bit of metadata in a directory together optional scripts, references, and assets. The key idea is progressive disclosure. Instead of stuffing your entire knowledge base into the context, the model only sees a small list of skills and let it load only what it needs. That means you can have hundreds of skills without blowing your context window (and making the model dumber and slower in result)

The technical structure is dead simple, but the implications are huge. Skills create a portable, reusable, composable way to give agents domain expertise, and they now work across most major harnesses. That means you can build a skill once and use it in Claude, Cursor, AMP, or any other agent tool that supports the standard.

Eleanor made the point that skills are an admission that we now have general‑purpose agents. The model can do the work, but it doesn’t know your preferences, your domain, your workflows. Skills are how you teach it those things. We also talked about how scripts inside skills reduce variance because you’re not asking the model to invent code every time; you’re just invoking trusted tools.

What really clicked for me this week is how easy it is to create skills using an agent. You don’t need to hand‑craft directories. You can describe your workflow, or even just do the task once in chat, and then ask the agent to turn it into a skill. It really is very very simple! And that’s likely the reason everyone is adopting this simple formart for extension their agents knowledge.

Get started with skills

If you use Claude Chat, the simplest way to get started is ask Claude to review your previous conversations and suggest a skill for you. Or, at the end of a long chat where you went back and forth with Claude on a task, ask it to distill the important parts into a skill. If you want to use other people’s skills, and you are using Claude Code, or any of the supported IDE/Agents, here’s where to download the folders and install them:

If you aren’t a developer and don’t subscribe to Claude, well, I got good news for you! I vibecoded skill support for every LLM 👇

The Skills Demo That Changed My Mind

I was resistant to skills at first, mostly because I wanted them inside my chat interface and not just in CLI tools. And I wasn’t subscribed to Claude for a while. Then I realized I could add skill support directly to Chorus, the open‑source multi‑model chat app, and I used Claude Code plus Ralph loops to vibe code it in a few hours. Now I can run skills with GPT‑5.2 Codex, Claude Opus, and Gemini from the same chat interface. That was my “I know kung fu” moment.

If you want to try Chorus with skills enabled, you can download my release here! Only for mac, and they are unsigned, mac will not like it, but you can run them anyway.

And if you want to explore more awesome skills, check out Vercel’s React Best Practices skills and UI Skills. It’s the beginning of a new kind of distribution: knowledge packaged as skills, shared like open source libraries (or paid for!) and

Open Source AI

Baichuan-M3 is a 235B medical LLM fine-tuned from Qwen3, released under Apache 2.0. The interesting claim here is that it beats GPT-5.2 on OpenAI’s HealthBench, including a remarkably low 3.5% hallucination rate.

What makes it different from typical medical models is that it’s trained to run actual clinical consultations asking follow-up questions and reasoning through differential diagnoses rather than just spitting out answers. Nisten pointed out that if you’re going to fine-tune something for healthcare, Qwen3 MoE is an excellent base because of its multilingual capabilities, which matters a lot in clinical settings. You can run it with vLLM or SGLang if you’ve got the hardware. (HF)

LongCat-Flash-Thinking-2601 from Meituan is a 560B MoE (27B active) released fully MIT-licensed. It’s specifically built for agentic tasks, scoring well on tool-use benchmarks like τ²-Bench and BrowseComp.

There’s a “Heavy Thinking” mode that pushes AIME-25 to 100%. What I like about this one is the training philosophy, they inject noise and broken tools during RL to simulate messy real-world conditions, which is exactly what production agents deal with. You can try it at longcat.chat and Github

We also saw Google release MedGemma this week (blog) a 4B model optimized for medical imaging like X-rays and CT scans and TranslateGemma (X) a family of on device translations (4B, 12B and 27B) which seem kind of cool! Didn’t have tons of time to dive into them unfortunately.

Vision, Voice & Art (Rapid Fire)

* Veo 3.1 adds native vertical video, 4K output, and better consistency in the Gemini API. Huge for creators (blog)

* Viral Kling motion‑transfer vids are breaking people’s brains about what AI video pipelines will look like.

* Pocket TTS from Kyutai Labs: a 100M‑parameter open‑source TTS model that runs on CPU and clones voices from seconds of audio (X)

* GLM‑Image drops as an open‑source hybrid AR + diffusion image model with genuinely excellent text rendering but pretty bad for everything else

* Black Forest Labs drops open source Flux.2 [Klein] 4B and 9B small models that create images super fast! (X, Fal, HF)

Phew, ok. I was super excited about this one and I’m really really happy with the result. I was joking on the pod that to prepare for this podcast, I not only had to collect all the news, I also had to ramp up on Agent Skills, and I wish we had an ability to upload information like the Matrix, but alas we didn’t. I also really enjoyed vibecoding a whole feature into Chorus just to explore skills fully, mind was absolutely blown when it worked after 3 hours of Ralphing!

See you next week, I think I have one more super exciting thing to play with this week before I talk about it!

TL;DR and Show Notes

* Hosts & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts: Wolfram Ravenwolf (@WolframRvnwlf), Yam Peleg (@yampeleg), Nisten Tahiraj (@nisten), LDJ (@ldjconfirmed)

* Guest: Eleanor Berger (@intellectronica)

* Open Source LLMs

* Baichuan-M3 - A 235B open-source medical LLM that beats GPT-5.2 on HealthBench with a 3.5% hallucination rate, featuring full clinical consultation capabilities. (HF, Blog, X Announcement)

* LongCat-Flash-Thinking-2601 - Meituan’s 560B MoE (27B active) agentic reasoning model, fully MIT licensed. Features “Heavy Thinking” mode scoring 100% on AIME-25. (GitHub, Demo, X Announcement)

* TranslateGemma - Google’s open translation family (4B, 12B, 27B) supporting 55 languages. The 4B model runs entirely on-device. (Arxiv, Kaggle, X Announcement)

* MedGemma 1.5 & MedASR - Native 3D imaging support (CT/MRI) and a speech model that beats Whisper v3 by 82% on clinical dictation error rates. (MedGemma HF, MedASR HF, Arxiv)

* Big CO LLMs + APIs

* Claude Cowork - Anthropic’s new desktop agent allows non-coders to give Claude file system and browser access to perform complex tasks. (TechCrunch, X Coverage)

* GPT-5.2 Codex - Now in the API ($1.75/1M input). Features native context compaction and state-of-the-art performance for long-running agentic loops. (Blog, Pricing)

* Cursor & FastRenderer - Cursor used GPT-5.2 Codex to build a 3M+ line Rust browser from scratch in one week of autonomous coding. (Blog, GitHub, X Thread)

* Gemini Personal Intelligence - Google leverages its data moat, letting Gemini reason across Gmail, Photos, and Search for hyper-personalized proactive help. (Blog, X Announcement)

* Partnerships & Drama

* Apple + Gemini - Apple officially selects Gemini to power Siri backend capabilities.

* OpenAI + Cerebras - A $10B deal for 750MW of high-speed compute through 2028. (Announcement)

* Thinking Machines - Co-founders and CTO return to OpenAI amidst drama; Soumith Chintala named new CTO.

* This Week’s Buzz

* WeaveHacks 3 - Self-Improving Agents Hackathon in SF (Jan 31-Feb 1). (Sign Up Here)

* Vision, Voice & Audio

* Veo 3.1 - Native 9:16 vertical video, 4K resolution, and reference image support in Gemini API. (Docs)

* Pocket TTS - A 100M parameter CPU-only model from Kyutai Labs that clones voices from 5s of audio. (GitHub, HF)

* GLM-Image - Hybrid AR + Diffusion model with SOTA text rendering. (HF, GitHub)

* FLUX.2 [klein] - Black Forest Labs releases fast 4B (Apache 2.0) and 9B models for sub-second image gen. (HF Collection, X Announcement)

* Kling Motion Transfer - Viral example of AI video pipelines changing Hollywood workflows. (X Thread)

* Deep Dive: Agent Skills

* Vercel React Best Practices - Pre-packaged skills for agents. (Blog)

* UI Skills - Documentation and skill standards. (Docs)

* Chorus with Skills - My fork of Chorus enabling skills for all LLMs. (Release)

For the first time, we had a co-host of the show also be a guest on the show, Ryan Carson (from Amp) went supernova viral this week with an X article (1.5M views) about Ralph Wiggum (yeah, from Simpsons) and he broke down that agentic coding technique at the end of the show.

LDJ and Nisten helped cover NVIDIA’s incredible announcements during CES with their Vera Rubin upcoming platform (4-5X improvements) and we all got excited about AI medicine with ChatGPT going into Health officially!

Plus, a bunch of Open Source news, let’s get into this:

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Open Source: The “Small” Models Are Winning

We often talk about the massive frontier models, but this week, Open Source came largely from unexpected places and focused on efficiency, agents, and specific domains.

Solar Open 100B: A Data Masterclass

Upstage released Solar Open 100B, and it’s a beast. It’s a 102B parameter Mixture-of-Experts (MoE) model, but thanks to MoE magic, it only uses about 12B active parameters during inference. This means it punches incredibly high but runs fast.

What I really appreciated here wasn’t just the weights, but the transparency. They released a technical report detailing their “Data Factory” approach. They trained on nearly 20 trillion tokens, with a huge chunk being synthetic. They also used a dynamic curriculum that adjusted the difficulty and the ratio of synthetic data as training progressed. This transparency is what pushes the whole open source community forward.

Technically, it hits 88.2 on MMLU and competes with top-tier models, especially in Korean language tasks. You can grab it on Hugging Face.

MiroThinker 1.5: The DeepSeek Moment for Agents?

We also saw MiroThinker 1.5, a 30B parameter model that is challenging the notion that you need massive scale to be smart. It uses something they call “Interactive Scaling.”

Wolfram broke this down for us: this agent forms hypotheses, searches for evidence, and then iteratively revises its answers in a time-sensitive sandbox. It effectively “thinks” before answering. The result? It beats trillion-parameter models on search benchmarks like BrowseComp. It’s significantly cheaper to run, too. This feels like the year where smaller models + clever harnesses (harnesses are the software wrapping the model) will outperform raw scale.

Liquid AI LFM 2.5: Running on Toasters (Almost)

We love Liquid AI and they are great friends of the show. They announced LFM 2.5 at CES with AMD, and these are tiny ~1B parameter models designed to run on-device. We’re talking about running capable AI on your laptop, your phone, or edge devices (or the Reachy Mini bot that I showed off during the show! I gotta try and run LFM on him!)

Probably the coolest part is the audio model. Usually, talking to an AI involves a pipeline: Speech-to-Text (ASR) -> LLM -> Text-to-Speech (TTS). Liquid’s model is end-to-end. It hears audio and speaks audio directly. We watched a demo from Maxime Labonne where the model was doing real-time interaction, interleaving text and audio. It’s incredibly fast and efficient. While it might not write a symphony for you, for on-device tasks like summarization or quick interactions, this is the future.

NousCoder-14B and Zhipu AI IPO

A quick shoutout to our friends at Nous Research who released NousCoder-14B, an open-source competitive programming model that achieved a 7% jump on LiveCodeBench accuracy in just four days of RL training on 48 NVIDIA B200 GPUs. The model was trained on 24,000 verifiable problems, and the lead researcher Joe Li noted it achieved in 4 days what took him 2 years as a teenager competing in programming contests. The full RL stack is open-sourced on GitHub and Nous published a great WandB results page as well!

And in historic news, Zhipu AI (Z.ai)—the folks behind the GLM series—became the world’s first major LLM company to IPO, raising $558 million on the Hong Kong Stock Exchange. Their GLM-4.7 currently ranks #1 among open-source and domestic models on both Artificial Analysis and LM Arena. Congrats to them!

Big Companies & APIs

NVIDIA CES: Vera Rubin Changes Everything

LDJ brought the heat on this one covering Jensen’s CES keynote that unveiled the Vera Rubin platform, and the numbers are almost hard to believe. We’re talking about a complete redesign of six chips: the Rubin GPU delivering 50 petaFLOPS of AI inference (5x Blackwell), the Vera CPU with 88 custom Olympus ARM cores, NVLink 6, ConnectX-9 SuperNIC, BlueField-4 DPU, and Spectrum-6 Ethernet.

Let me put this in perspective using LDJ’s breakdown: if you look at FP8 performance, the jump from Hopper to Blackwell was about 5x. The jump from Blackwell to Vera Rubin is over 3x again—but here’s the kicker—while only adding about 200 watts of power draw. That’s insane efficiency improvement.

The real-world implications Jensen shared: training a 10 trillion parameter mixture-of-experts model now requires 75% fewer GPUs compared to Blackwell. Inference token costs drop roughly 10x—a 1MW cluster goes from 1 million to 10 million tokens per second at the same power. HBM4 memory delivers 22 TB/s bandwidth with 288GB capacity, exceeding NVIDIA’s own 2024 projections by nearly 70%.

As Ryan noted, when people say there’s an AI bubble, this is why it’s hilarious. Jensen keeps saying the need for inference is unbelievable and only going up exponentially. We all see this. I can’t get enough inference—I want to spin up 10 Ralphs running concurrently! The NVL72 rack-scale system achieves 3.6 exaFLOPS inference with 20.7TB total HBM, and it’s already shipping. Runway 4.5 is already running on the new platform, having ported their model from Hopper to Vera Rubin NVL72 in a single day.

NVIDIA also recently acqui-hidred Groq (with a Q) in a ~$20 billion deal, bringing the inference chip expertise from the guy who created Google’s TPUs in-house.

Nemotron Speech ASR & The Speed of Voice (X, HF, Blog)

NVIDIA also dropped Nemotron Speech ASR. This is a 600M parameter model that offers streaming transcription with 24ms latency.

We showed a demo from our friend Kwindla Kramer at Daily. He was talking to an AI, and the response was virtually instant. The pipeline is: Nemotron (hearing) -> Llama/Nemotron Nano (thinking) -> Magpie TTS (speaking). The total latency is under 500ms. It feels like magic. Instant voice agents are going to be everywhere this year.

XAI Raises $20B While Grok Causes Problems (Again)

So here’s the thing about covering anything Elon-related: it’s impossible to separate signal from noise because there’s an army of fans who hype everything and an army of critics who hate everything. But let me try to be objective here.

XAI raised another massive Round E of $20 billion! at a $230 billion valuation, with NVIDIA and Cisco as strategic investors. The speed of their infrastructure buildout is genuinely incredible. Grok’s voice mode is impressive. I use Grok for research and it’s really good, notable for it’s unprecedented access to X !

But. This raise happened in the middle of a controversy where Grok’s image model was being used to “put bikinis” on anyone in reply threads, including—and this is where I draw a hard line—minors. As Nisten pointed out on the show, it’s not even hard to implement guardrails. You just put a 2B VL model in front and ask “is there a minor in this picture?” But people tested it, asked Grok not to use the feature, and it did it anyway. And yeah, putting Bikini on Claude is funny, but basic moderation is lacking!

The response of “we’ll prosecute illegal users” is stupid when there’s no moderation built into the product. There’s an enormous difference between Photoshop technically being able to do something after hours of work, and a feature that generates edited images in one second as the first comment to a celebrity, then gets amplified by the platform’s algorithm to millions of people. One is a tool. The other is a product with amplification mechanics. Products need guardrails. I don’t often link to CNN (in fact this is the first time) but they have a great writeup about the whole incident here which apparently includes the quitting of a few trust and safety folks and Elon’s pushback on guardrails. Crazy

That said, Grok 5 is in training and XAI continues to ship impressive technology. I just wish they’d put the same engineering effort into safety as they do into capabilities!

OpenAI Launches GPT Health

This one’s exciting. OpenAI CEO Fidji Simo announced ChatGPT Health, a privacy-first space for personalized health conversations that can connect to electronic health records, Apple Health, Function Health, Peloton, and MyFitnessPal.

Here’s why this matters: health already represents about 5% of all ChatGPT messages globally and touches 25% of weekly active users—often outside clinic hours or in underserved areas. People are already using these models for health advice constantly.

Nisten, who has worked on AI doctors since the GPT-3 days and even published papers on on-device medical AI, gave us some perspective: the models have been fantastic for health stuff for two years now. The key insight is that medical data seems like a lot, but there are really only about 2,000 prescription drugs and 2,000 diseases (10,000 if you count rare ones). That’s nothing for an LLM. The models excel at pattern recognition across this relatively contained dataset.

The integration with Function Health is particularly interesting to me. Function does 160+ lab tests, but many doctors won’t interpret them because they didn’t order them. ChatGPT could help bridge that gap, telling you “hey, this biomarker looks off, you should discuss this with your doctor.” The bad news is, this is just a waitlist and you can add yourself to the waitlist here, we’ll keep monitoring the situation and let you know when it opens up

Doctronic: AI Prescribing Without Physician Oversight

Speaking of healthcare, Doctronic launched a pilot in Utah where AI can autonomously renew prescriptions for chronic conditions without any physician in the loop. The system covers about 190 routine medications (excluding controlled substances) at just $4 per renewal. Trial data showed 99.2% concordance with physician treatment plans, and they’ve secured pioneering malpractice insurance that treats the AI like a clinician.

Nisten made the case that it’s ethically wrong to delay this kind of automation when ER wait times keep increasing and doctors are overworked. The open source models are already excellent at medical tasks. Governments should be buying GPUs rather than creating administrative roadblocks. Strong strong agree here!

Google Brings Gmail into the Gemini Era (X)

Breaking news from the day of our show: Google announced Gmail’s biggest AI transformation since its 2004 launch, powered by Gemini 3. This brings AI Overviews that summarize email threads, natural language queries (”Who gave me a plumber quote last year?”), Help Me Write, contextual Suggested Replies matching your writing style, and the upcoming AI Inbox that filters noise to surface VIPs and urgent items.

For 3 billion Gmail users, this is huge. I’m very excited to test it—though not live on the show because I don’t want you reading my emails.

This weeks buzz - covering Weights & Biases updates

Not covered on the show, but a great update on stuff from WandB, Chris Van Pelt (@vanpelt), one of the 3 co-founders released a great project I wanted to tell you about! For coders, this is an app that allows you to run multiple Claude Codes on free Github sandboxes, so you can code (or Ralph) and control everything away from home!

GitHub gives personal users 120 free Codespaces hours/month, and Catnip automatically shuts down inactive instances so you can code for quite a while with Catnip!

It’s fully open source on Github and you can download the app here

Interview: Ryan Carson - What the hell is Ralph Wiggum?

Okay, let’s talk about the character everyone is seeing on their timeline: Ralph Wiggum. My co-host Ryan Carson went viral this week with an article about this technique, and I had to have him break it down.

Ralph isn’t a new model; it’s a technique for running agents in a loop to perform autonomous coding. The core idea is deceptively simple: Ralph is a bash script that loops an AI coding agent. In a loop, until it a certain condition is met. But why is it blowing up?

Normally when you use a coding agent like Cursor, Claude Code, or AMP, you need to be in the loop. You approve changes, look at code, fix things when the agent hits walls or runs out of context. Ralph solves this by letting the agent run autonomously while you sleep.

Here’s how it works: First, you write a Product Requirements Doc (PRD) by talking to your agent for a few minutes about what you want to build. Then you convert that PRD into a JSON file containing atomic user stories with clear acceptance criteria. Each user story is small enough for the agent to complete in one focused thread.

The Ralph script then loops: it picks the first incomplete user story, the agent writes code to implement it, tests against the acceptance criteria, commits the changes, marks the story as complete, writes what it learned to a shared “agents.md” file, and loops to the next story. That compound learning step is crucial—without it, the agent would keep making the same mistakes.

What makes this work is the pre-work. As Ryan put it, “no real work is done one-shot.” This is how software engineering has always worked—you break big problems into smaller problems into user stories and solve them incrementally. The innovation is letting AI agents work through that queue autonomously while you sleep! Ryan’s excellent (and viral) X article is here!

Vision & Video

LTX-2 Goes Fully Open Source (HF, Paper)

Lightricks finally open-sourced LTX-2, marking a major milestone as the first fully open audio-video generation model. This isn’t just “we released the weights” open—it’s complete model weights (13B and 2B variants), distilled versions, controllable LoRAs, a full multimodal trainer, benchmarks, and evaluation scripts. For a video model that is aiming to be the open source SORA, supports audio and lipsync

The model generates synchronized audio and video in a single DiT-based architecture—motion, dialogue, ambience, and music flow simultaneously. Native 4K at up to 50 FPS with audio up to 10 seconds. And there’s also a distilled version (Thanks Pruna AI!) hosted on Replicate

ComfyUI provided day-0 native support, and community testing shows an A6000 generating 1280x720 at 120 frames in 50 seconds. This is near Sora-level quality that you can fine-tune on your own data for custom styles and voices in about an hour.

What a way to start 2026. From chips that are 5x faster to AI doctors prescribing meds in Utah, the pace is only accelerating. If anyone tells you we’re in an AI bubble, just show them what we covered today. Even if the models stopped improving tomorrow, the techniques like “Ralph” prove we have years of work ahead of us just figuring out how to use the intelligence we already have.

Thank you for being a ThursdAI subscriber. See you next week!

As always, here’s the show notes and TL;DR links:

* Hosts & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @nisten, @ldjconfirmed

* Special Guest - Ryan Carson (@ryancarson) breaking down the Ralph Wiggum technique.

* Open Source LLMs

* Solar Open 100B - Upstage’s 102B MoE model. Trained on 19.7T tokens with a heavy focus on “data factory” synthetic data and high-performance Korean reasoning (X, HF, Tech Report).

* MiroThinker 1.5 - A 30B parameter search agent that uses “Interactive Scaling” to beat trillion-parameter models on search benchmarks like BrowseComp (X, HF, GitHub).

* Liquid AI LFM 2.5 - A family of 1B models designed for edge devices. Features a revolutionary end-to-end audio model that skips the ASR-LLM-TTS pipeline (X, HF).

* NousCoder-14B - competitive coding model from Nous Research that saw a 7% LiveCodeBench accuracy jump in just 4 days of RL (X, WandB Dashboard).

* Zhipu AI IPO - The makers of GLM became the first major LLM firm to go public on the HKEX, raising $558M (Announcement).

* Big Co LLMs & APIs

* NVIDIA Vera Rubin - Jensen Huang’s CES reveal of the next-gen platform. Delivers 5x Blackwell inference performance and 75% fewer GPUs needed for MoE training (Blog).

* OpenAI ChatGPT Health - A privacy-first vertical for EHR and fitness data integration (Waitlist).

* Google Gmail Era - Gemini 3 integration into Gmail for 3 billion users, featuring AI Overviews and natural language inbox search (Blog).

* XAI $20B Raise - Elon’s XAI raises Series E at a $230B valuation, even as Grok faces heat over bikini-gate and safety guardrails (CNN Report).

* Doctronic - The first US pilot in Utah for autonomous AI prescription renewals without a physician in the loop (Web).

* Alexa+ Web - Amazon brings the “Smart Alexa” experience to browser-based chat (Announcement).

* Autonomous Coding & Tools

* Ralph Wiggum - The agentic loop technique for autonomous coding using small, atomic user stories. Ryan Carson’s breakdown of why this is the death of “vibe coding” (Viral X Article).

* Catnip by W&B - Chris Van Pelt’s open-source iOS app to run Claude Code anywhere via GitHub Codespaces (App Store, GitHub).

* Vision & Video

* LTX-2 - Lightricks open-sources the first truly open audio-video generation model with synchronized output and full training code (GitHub, Replicate Demo).

* Avatar Forcing - KAIST’s framework for real-time interactive talking heads with ~500ms latency (Arxiv).

* Qwen Edit 2512 - Optimized by PrunaAI to generate high-res realistic images in under 7 seconds (Replicate).

* Voice & Audio

* Nemotron Speech ASR - NVIDIA’s 600M parameter streaming model with sub-100ms stable latency for massive-scale voice agents (HF).

Happy new year! This is Alex, writing to you for the very fresh start of this year, it’s 2026 already, can you believe it?

There was no live stream today, I figured the cohosts deserve a break and honestly it was a very slow week. Even the chinese labs who don’t really celebrate X-mas and new years didn’t come out with a banger AFAIK.

ThursdAI - AI moves fast, we’re here to make sure you never miss a thing! Subscribe :)

Tho I thought it was an incredible opportunity to finally post the Will Brow interview I recorded in November during the AI Engineer conference.

Will is a researcher at Prime Intellect (big fans on WandB btw!) and is very known on X as a hot takes ML person, often going viral for tons of memes!

Will is the creator and maintainer of the Verifiers library (Github) and his talk at AI Engineer was all about RL Environments (what they are, you can hear in the interview, I asked him!)

TL;DR last week of 2025 in AI

Besides this, my job here is to keep you up to date, and honestly this was very easy this week, as… almost nothing has happened, but here we go:

Meta buys Manus

The year ended with 2 huge acquisitions / aquihires. First we got the news from Alex Wang that Meta has bought Manus.ai which is an agentic AI startup we covered back in March for an undisclosed amount (folks claim $2-3B)

The most interesting thing here is that Manus is a Chinese company, and this deal requires very specific severance from Chinese operations.

Jensen goes on a new years spending spree, Nvidia buys Groq (not GROK) for $20B

Groq which we covered often here, and are great friends, is going to NVIDIA, in a… very interesting acqui-hire, which is a “non binding license” + most of Groq top employees apparently are going to NVIDIA. Jonathan Ross the CEO of Groq, was the co-creator of the TPU chips at Google before founding Groq, so this seems like a very strategic aquihire for NVIDIA! Congrats to our friends from Groq on this amazing news for the new year!

Tencent open-sources HY-MT1.5 translation models with 1.8B edge-deployable and 7B cloud variants supporting 33 languages (X, HF, HF, GitHub)

It seems that everyone’s is trying to de-throne whisper and this latest attempt from Tencent is a interesting one. a 1.8B and 7B translation models with very interesting stats.

Alibaba’s Qwen-Image-2512 drops on New Year’s Eve as strongest open-source text-to-image model, topping AI Arena with photorealistic humans and sharper textures (X, HF, Arxiv)

Our friends in Tongyi decided to give is a new years present in the form of an updated Qwen-image, with much improved realism

That’s it folks, this was a quick one, hopefully you all had an amazing new year celebration, and are gearing up to an eventful and crazy 2026.

I wish you all happiness, excitement and energy to keep up with everything in the new year, and will make sure that we’re here to keep you up to date as always!

P.S - I got a little news of my own this yesterday, not related to AI. She said yes 🎉

Merry Christmas (to those who celebrate) and welcome to the very special yearly ThursdAI recap! This was an intense year in the world of AI, and after 51 weekly episodes (this is episode 52!) we have the ultimate record of all the major and most important AI releases of this year!

So instead of bringing you a weekly update (it’s been a slow week so far, most AI labs are taking a well deserved break, the Cchinese AI labs haven’t yet surprised anyone), I’m dropping a comprehensive yearly AI review! Quarter by quarter, month by month, both in written form and as a pod/video!

Why do this? Who even needs this? Isn’t most of it obsolete? I have asked myself this exact question while prepping for the show (it was quite a lot of prep, even with Opus’s help). I eventually landed on, hey, if nothing else, this will serve as a record of the insane week of AI progress we all witnessed. Can you imagine that the term Vibe Coding is less than 1 year old? That Claude Code was released at the start of THIS year?

We get hedonicly adapt to new AI goodies so quick, and I figured this will serve as a point in time check, we can get back to and feel the acceleration!

With that, let’s dive in - P.S. the content below is mostly authored by my co-author for this, Opus 4.5 high, which at the end of 2025 I find the best creative writer with the best long context coherence that can imitate my voice and tone (hey, I’m also on a break! 🎅)

“Open source AI has never been as hot as this quarter. We’re accelerating as f*ck, and it’s only just beginning—hold on to your butts.” — Alex Volkov, ThursdAI Q1 2025

🏆 The Big Picture — 2025 - The Year the AI Agents Became Real

Looking back at 51 episodes and 12 months of relentless AI progress, several mega-themes emerged:

1. 🧠 Reasoning Models Changed Everything

From DeepSeek R1 in January to GPT-5.2 in December, reasoning became the defining capability. Models now think for hours, call tools mid-thought, and score perfect on math olympiads.

2. 🤖 2025 Was Actually the Year of Agents

We said it in January, and it came true. Claude Code launched the CLI revolution, MCP became the universal protocol, and by December we had ChatGPT Apps, Atlas browser, and AgentKit.

3. 🇨🇳 Chinese Labs Dominated Open Source

DeepSeek, Qwen, MiniMax, Kimi, ByteDance — despite chip restrictions, Chinese labs released the best open weights models all year. Qwen 3, Kimi K2, DeepSeek V3.2 were defining releases.

4. 🎬 We Crossed the Uncanny Valley

VEO3’s native audio, Suno V5’s indistinguishable music, Sora 2’s social platform — 2025 was the year AI-generated media became indistinguishable from human-created content.

5. 💰 The Investment Scale Became Absurd

$500B Stargate, $1.4T compute obligations, $183B valuations, $100-300M researcher packages, LLMs training in space. The numbers stopped making sense.

6. 🏆 Google Made a Comeback

After years of “catching up,” Google delivered Gemini 3, Antigravity, Nano Banana Pro, VEO3, and took the #1 spot (briefly). Don’t bet against Google.

By the Numbers

Q1 2025 — The Quarter That Changed Everything

DeepSeek R1 crashed NVIDIA’s stock, reasoning models went mainstream, and Chinese labs took over open source. The quarter that proved AI isn’t slowing down—it’s just getting started.

Key Themes:

* 🧠 Reasoning models went mainstream (DeepSeek R1, o1, QwQ)

* 🇨🇳 Chinese labs dominated open source (DeepSeek, Alibaba, MiniMax, ByteDance)

* 🤖 2025 declared “The Year of Agents” (OpenAI Operator, MCP won)

* 🖼️ Image generation revolution (GPT-4o native image gen, Ghibli-mania)

* 💰 Massive infrastructure investment (Project Stargate $500B)

January — DeepSeek Shakes the World

(Jan 02 | Jan 10 | Jan 17 | Jan 24 | Jan 30)

The earthquake that shattered the AI bubble. DeepSeek R1 dropped on January 23rd and became the most impactful open source release ever:

* Crashed NVIDIA stock 17% — $560B loss, largest single-company monetary loss in history

* Hit #1 on the iOS App Store

* Cost allegedly only $5.5M to train (sparking massive debate)

* Matched OpenAI’s o1 on reasoning benchmarks at 50x cheaper pricing

* The 1.5B model beat GPT-4o and Claude 3.5 Sonnet on math benchmarks 🤯

“My mom knows about DeepSeek—your grandma probably knows about it, too” — Alex Volkov

Also this month:

* OpenAI Operator — First agentic ChatGPT (browser control, booking, ordering)

* Project Stargate — $500B AI infrastructure (Manhattan Project for AI)

* NVIDIA Project Digits — $3,000 desktop that runs 200B parameter models

* Kokoro TTS — 82M param model hit #1 on TTS Arena, Apache 2, runs in browser

* MiniMax-01 — 4M context window from Hailuo

* Gemini Flash Thinking — 1M token context with thinking traces

February — Reasoning Mania & The Birth of Vibe Coding

(Feb 07 | Feb 13 | Feb 20 | Feb 28)

The month that redefined how we work with AI.

OpenAI Deep Research (Feb 6) — An agentic research tool that scored 26.6% on Humanity’s Last Exam (vs 10% for o1/R1). Dr. Derya Unutmaz called it “a phenomenal 25-page patent application that would’ve cost $10,000+.”

Claude 3.7 Sonnet & Claude Code (Feb 24-27) — Anthropic’s coding beast hit 70% on SWE-Bench with 8x more output (64K tokens). Claude Code launched as Anthropic’s agentic coding tool — marking the start of the CLI agent revolution.

“Claude Code is just exactly in the right stack, right around the right location... You can do anything you want with a computer through the terminal.” — Yam Peleg

GPT-4.5 (Orion) (Feb 27) — OpenAI’s largest model ever (rumored 10T+ parameters). 62.5% on SimpleQA, foundation for future reasoning models.

Grok 3 (Feb 20) — xAI enters the arena with 1M token context and “free until GPUs melt.”

Andrej Karpathy coins “Vibe Coding” (Feb 2) — The 5.2M view tweet that captured a paradigm shift: developers describe what they want, AI handles implementation.

OpenAI Roadmap Revelation (Feb 13) — Sam Altman announced GPT-4.5 will be the last non-chain-of-thought model. GPT-5 will unify everything.

March — Google’s Revenge & The Ghibli Explosion

(Mar 06 | Mar 13 | Mar 20 | Mar 27)

Gemini 2.5 Pro Takes #1 (Mar 27) — Google reclaimed the LLM crown with AIME jumping nearly 20 points, 1M context, “thinking” integrated into the core model.

GPT-4o Native Image Gen — Ghibli-mania (Mar 27) — The internet lost its collective mind and turned everything into Studio Ghibli. Auto-regressive image gen with perfect text rendering, incredible prompt adherence.

“The internet lost its collective mind and turned everything into Studio Ghibli” — Alex Volkov

MCP Won (Mar 27) — OpenAI officially adopted Anthropic’s Model Context Protocol. No VHS vs Betamax situation. Tools work across Claude AND GPT.

DeepSeek V3 685B — AIME jumped from 39.6% → 59.4%, MIT licensed, best non-reasoning open model.

ThursdAI Turns 2! (Mar 13) — Two years since the first episode about GPT-4.

Open Source Highlights:

* Gemma 3 (1B-27B) — 128K context, multimodal, 140+ languages, single GPU

* QwQ-32B — Qwen’s reasoning model matches R1, runs on Mac

* Mistral Small 3.1 — 24B, beats Gemma 3, Apache 2

* Qwen2.5-Omni-7B — End-to-end multimodal with speech output

Q2 2025 — The Quarter That Shattered Reality

VEO3 crossed the uncanny valley, Claude 4 arrived with 80% SWE-bench, and Qwen 3 proved open source can match frontier models. The quarter we stopped being able to tell what’s real.

Key Themes:

* 🎬 Video AI crossed the uncanny valley (VEO3 with native audio)

* 🧠 Tool-using reasoning models emerged (o3 calling tools mid-thought)

* 🇨🇳 Open source matched frontier (Qwen 3, Claude 4)

* 📺 Google I/O delivered everything

* 💸 AI’s economic impact accelerated ($300B valuations, 80% price drops)

April — Tool-Using Reasoners & Llama Chaos

(Apr 03 | Apr 10 | Apr 17 | Apr 24)

OpenAI o3 & o4-mini (Apr 17) — The most important reasoning upgrade ever. For the first time, o-series models can use tools during reasoning: web search, Python, image gen. Chain 600+ consecutive tool calls. Manipulate images mid-thought.

“This is almost AGI territory — agents that reason while wielding tools” — Alex Volkov

GPT-4.1 Family (Apr 14) — 1 million token context across all models. Near-perfect recall. GPT-4.5 deprecated.

Meta Llama 4 (Apr 5) — Scout (17B active/109B total) & Maverick (17B active/400B total). LMArena drama (tested model ≠ released model). Community criticism. Behemoth teased but never released.

Gemini 2.5 Flash (Apr 17) — Set “thinking budget” per API call. Ultra-cheap at $0.15/$0.60 per 1M tokens.

ThursdAI 100th Episode! 🎉

May — VEO3 Crosses the Uncanny Valley & Claude 4 Arrives

(May 01 | May 09 | May 16 | May 23 | May 29)

VEO3 — The Undisputed Star of Google I/O (May 20) — Native multimodal audio generation (speech, SFX, music synced perfectly). Perfect lip-sync. Characters understand who’s speaking. Spawned viral “Prompt Theory” phenomenon.

“VEO3 isn’t just video generation — it’s a world simulator. We crossed the uncanny valley this quarter.” — Alex Volkov

Claude 4 Opus & Sonnet — Live Drop During ThursdAI! (May 22) — Anthropic crashed the party mid-show. First models to cross 80% on SWE-bench. Handles 6-7 hour human tasks. Hybrid reasoning + instant response modes.

Qwen 3 (May 1) — The most comprehensive open source release ever: 8 models, Apache 2.0. Runtime /think toggle for chain-of-thought. 4B dense beats Qwen 2.5-72B on multiple benchmarks. 36T training tokens, 119 languages.

“The 30B MoE is ‘Sonnet 3.5 at home’ — 100+ tokens/sec on MacBooks” — Nisten

Google I/O Avalanche:

* Gemini 2.5 Pro Deep Think (84% MMMU)

* Jules (free async coding agent)

* Project Mariner (browser control via API)

* Gemini Ultra tier ($250/mo)

June — The New Normal

(Jun 06 | Jun 13 | Jun 20 | Jun 26)

o3 Price Drop 90% (Jun 12) — From $40/$10 → $8/$2 per million tokens. o3-pro launched at 87% cheaper than o1-pro.

Meta’s $15B Scale AI Power Play (Jun 12) — 49% stake in Scale AI. Alex Wang leads new “Superintelligence team” at Meta. Seven-to-nine-figure comp packages for researchers.

MiniMax M1 — Reasoning MoE That Beats R1 (Jun 19) — 456B total / 45B active parameters. Full weights on Hugging Face.

Gemini CLI (Jun 26) — Google’s open source terminal agent brings Gemini 2.5 Pro to your command line.

Flux Kontext — SOTA image editing with character consistency.

Q3 2025 — The Quarter of GPT-5 & Trillion-Parameter Open Source

GPT-5 arrived after 32 months. Open source hit trillion-parameter scale. World models became playable. Chinese labs continued their dominance.

Key Themes:

* 👑 GPT-5 Era began (unified reasoning + chat)

* 🇨🇳 Open source hit trillion-scale (Kimi K2, Qwen3-Coder)

* 🌍 World models became playable (Google Genie-3)

* 🎥 Video reached “can’t tell” quality

* 💰 Unprecedented investment ($100B pledges, $183B valuations)

July — Trillion-Parameter Open Source Arrives

(Jul 03 | Jul 11 | Jul 17 | Jul 24)

Kimi K2 — The Trillion-Parameter King (Jul 17) — Moonshot dropped a 1 trillion parameter MoE model: 65.8% on SWE-bench Verified (beating Claude Sonnet without reasoning), 32B active parameters, 128K context, Modified MIT license.

“This isn’t just another model release. This is ‘Sonnet at home’ if you have the hardware.” — Alex Volkov

Grok-4 & Grok Heavy (Jul 10) — 50% on Humanity’s Last Exam with tools. 100% on AIME25. xAI finally became a serious contender.

ChatGPT Agent (Odyssey) (Jul 17) — Unified agentic AI: browser + terminal + research. 41.6% on HLE (double o3).

Chinese Open Source Explosion:

* Baidu ERNIE 4.5 (10 models, Apache 2.0)

* Tencent Hunyuan-A13B (80B MoE, 256K context)

* Huawei Pangu Pro (trained entirely on Ascend NPUs — no Nvidia!)

* Qwen3-Coder-480B (69.6% SWE-bench)

August — GPT-5 Month

(Aug 01 | Aug 07 | Aug 15 | Aug 21)

GPT-5 Launch (Aug 7) — 32 months after GPT-4:

* 400K context window

* $1.25/$10 per million tokens (Opus is $15/$75)

* Unified thinking + chat model

* Router-based architecture (initially buggy)

* Free tier access for back-to-school

“32 months since GPT-4 release, 32 months of ThursdAI” — Alex Volkov

GPT-OSS (Aug 5) — OpenAI goes Apache 2.0 open source for the first time since GPT-2: 120B and 20B models, configurable reasoning, full chain-of-thought access.

Google Genie-3 (Aug 7) — DeepMind’s world model generates fully interactive 3D environments: real-time at 24fps, memory/consistency breakthrough, walk/fly/control in generated worlds.

DeepSeek V3.1 Hybrid (Aug 21) — Matches/beats R1 with fewer thinking tokens. 66% SWE-bench Verified. Tool calls inside thinking. MIT licensed.

September — Shiptember Delivers

(Sep 05 | Sep 12 | Sep 19 | Sep 26)

GPT-5-Codex (Sep 18) — Works 7+ hours independently. 93% fewer tokens on simple tasks. Reviews majority of OpenAI’s own PRs. Perfect 12/12 on 2025 ICPC.

Meta Connect 25 (Sep 18) — AI glasses with built-in display, neural band wristband, live translation with subtitles, $799 shipping immediately.

Qwen-mas Strikes Again (Sep 26):

* Qwen3-VL-235B (vision reasoner, 1M context for video)

* Qwen3-Omni-30B (end-to-end omni-modal)

* Qwen-Max (over 1T parameters, roadmap to 100M token context)

NVIDIA $100B pledge to OpenAI — “Biggest infrastructure project in history”

Suno V5 — The music generation model where we officially can’t tell anymore.

“I can no longer tell which music is AI and which is human. This is it. We’ve passed the Rubicon.” — Alex Volkov

Q4 2025 — The Quarter of Agents, Gemini’s Crown & The Reasoning Wars

The densest quarter in AI history. Google took the throne with Gemini 3, OpenAI fired back with GPT-5.2, and agents became real products. Someone trained an LLM in space.

Key Themes:

* 🚀 Reasoning wars peaked (Gemini 3 → GPT-5.2 → DeepSeek gold medals)

* 🤖 Agents became products (Atlas, AgentKit, ChatGPT Apps)

* 👑 Google’s comeback (Gemini 3, Antigravity, Nano Banana)

* 🏃 ASI race accelerated ($1.4T compute, 2028 autonomous researchers)

* 🎬 Sora 2 launched AI-native social media

October — Sora Changes Social Media Forever

(Oct 03 | Oct 10 | Oct 17 | Oct 24 | Oct 30)

Sora 2 — AI Social Media is Born (Oct 2):

* Shot to #3 on iOS App Store within days

* Cameos: upload your face, star in any video

* Sam Altman shared his Cameo publicly, becoming the internet’s most meme-able person

* All content is AI-generated — no uploads, only creations

“This is the first social media with UGC where content can ONLY be generated” — Alex Volkov

OpenAI Dev Day (Oct 9):

* ChatGPT Apps for 800M+ weekly active users

* AgentKit: drag-and-drop agent builder

* GPT-5-Pro in API

* Sam revealed $1.4 trillion in compute obligations

AI Makes Novel Cancer Discovery (Oct 16) — A 27B Gemma-based model generated a novel hypothesis about cancer cells validated in a wet lab. First confirmed case of AI creating genuinely new scientific knowledge.

Claude Sonnet 4.5 — 61.4% OS World (computer use)Claude Haiku 4.5 — 73.3% SWE-Bench, lightning fast

November — The Week That Changed Everything

(Nov 07 | Nov 13 | Nov 20 | Nov 27)

THE MOST INSANE WEEK IN AI HISTORY. In a single span of ~10 days:

* Grok 4.1 — #1 LMArena (briefly)

* Gemini 3 Pro — Took the throne with 45.14% on ARC-AGI-2 (Deep Think)

* GPT-5.1-Codex-Max — 24+ hour autonomous coding

* Nano Banana Pro — 4K image generation with perfect text rendering

* Meta SAM 3 & SAM 3D — Open-vocabulary segmentation

* Claude Opus 4.5 — 80.9% SWE-Bench Verified, beats GPT-5.1

“This week almost broke me as a person whose full-time job is to cover and follow AI releases.” — Alex Volkov

Gemini 3 Pro + Deep Think (Nov 20) — Google finally took the LLM throne: 45.14% on ARC-AGI-2, roughly double previous SOTA.

Google Antigravity IDE (Nov 20) — Free agent-first VS Code fork with browser integration, multiple parallel agents.

Nano Banana Pro (Nov 20) — Native 4K resolution with “thinking” traces, perfect text rendering.

Claude Opus 4.5 (Nov 27) — 80.9% SWE-Bench Verified. $5/$25 per MTok (1/3 previous cost). “Effort” parameter for reasoning control.

“Opus 4.5 is unbelievable. You can ship a full feature on a mature code base in one day, always. It’s just mind blowing.” — Ryan Carson

1X NEO (Oct 30) — First consumer humanoid robot, pre-orders at $20,000, delivery early 2026.

December — GPT-5.2 Fires Back

(Dec 02 | Dec 05 | Dec 12 | Dec 19)

GPT-5.2 — OpenAI’s Answer to Gemini 3 (Dec 11) — Dropped live during ThursdAI:

* 90.5% on ARC-AGI-1 (Pro X-High configuration)

* 54%+ on ARC-AGI-2 — reclaiming frontier from Gemini 3

* 100% on AIME 2025 — perfect math olympiad score

* 70% on GDPval (up from 47% in Sept!)

* Reports of models thinking for 1-3 hours on hard problems

DeepSeek V3.2 & V3.2-Speciale — Gold Medal Reasoning (Dec 4):

* 96% on AIME (vs 94% for GPT-5 High)

* Gold medals on IMO (35/42), CMO, ICPC (10/12), IOI (492/600)

* $0.28/million tokens on OpenRouter

MCP Donated to Linux Foundation (Dec 11) — Agentic AI Foundation launched under Linux Foundation. MCP, AGENTS.md, and goose donated to vendor-neutral governance.

Mistral 3 Returns to Apache 2.0 (Dec 4) — Mistral Large 3 (675B MoE), Ministral 3 (vision, edge-optimized).

Starcloud: LLM Training in Space (Dec 11) — An H100 satellite trained nanoGPT on Shakespeare. SSH into an H100… in space… with a US flag in the corner.

“Peak 2025 energy — the era of weird infra ideas has begun.” — Karpathy reacts

Gemini 3 Flash (Dec 18) — Fastest frontier model, pairs with Gemini 3 Pro for speed vs depth tradeoffs.

🙏 Thank You

This has been an incredible year of ThursdAI. 51 episodes, countless releases, and a community that keeps showing up every week to make sense of the madness together.

Huge thanks to our amazing co-hosts and friends of the pod:

* Alex Volkov — AI Evangelist, Weights & Biases (@altryne)

* Wolfram Ravenwolf (@WolframRvnwlf)

* Yam Peleg (@yampeleg)

* Nisten Tahiraj (@nisten)

* LDJ (@ldjconfirmed)

* Ryan Carson (@ryancarson)

* Kwindla Hultman Kramer — CEO of Daily (@kwindla)

And to everyone who tunes in — whether you’re listening on your commute, doing dishes, or just trying to keep up with the insanity — thank you. You make this possible.

📢 Stay Connected

* 🎧 Subscribe: thursdai.news

* 🐦 Follow Alex: @altryne

* 💻 This recap is open source: github.com/altryne/thursdAI_yearly_recap

“We’re living through the early days of a technological revolution, and we get to be part of it. That’s something to be genuinely thankful for.” — Alex Volkov

Happy Holidays, and see you in 2026! 🚀

The best is yet to come. Hold on to your butts.

We’re wrapping up 2025, and the AI labs decided they absolutely could NOT let the year end quietly. This week was an absolute banger—we had Gemini 3 Flash dropping with frontier intelligence at flash prices, OpenAI firing off GPT 5.2 Codex as breaking news DURING our show, ChatGPT Images 1.5, Nvidia going all-in on open source with Nemotron 3 Nano, and the voice AI space heating up with Grok Voice and Chatterbox Turbo. Oh, and Google dropped FunctionGemma for all your toaster-to-fridge communication needs (yes, really).

Today’s show was over three and a half hours long because we tried to cover both this week AND the entire year of 2025 (that yearly recap is coming next week—it’s a banger, we went month by month and you’ll really feel the acceleration). For now, let’s dive into just the insanity that was THIS week.

00:00 Introduction and Overview

00:39 Weekly AI News Highlights

01:40 Open Source AI Developments

01:44 Nvidia's Nemotron Series

09:09 Google's Gemini 3 Flash

19:26 OpenAI's GPT Image 1.5

20:33 Infographic and GPT Image 1.5 Discussion

20:53 Nano Banana vs GPT Image 1.5

21:23 Testing and Comparisons of Image Models

23:39 Voice and Audio Innovations

24:22 Grok Voice and Tesla Integration

26:01 Open Source Robotics and Voice Agents

29:44 Meta's SAM Audio Release

32:14 Breaking News: Google Function Gemma

33:23 Weights & Biases Announcement

35:19 Breaking News: OpenAI Codex 5.2 Max

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Big Companies LLM updates

Google’s Gemini 3 Flash: The High-Speed Intelligence King

If we had to title 2025, as Ryan Carson mentioned on the show, it might just be “The Year of Google’s Comeback.” Remember at the start of the year when we were asking “Where is Google?” Well, they are here. Everywhere.

This week they launched Gemini 3 Flash, and it is rightfully turning heads. This is a frontier-class model—meaning it boasts Pro-level intelligence—but it runs at Flash-level speeds and, most importantly, Flash-level pricing. We are talking $0.50 per 1 million input tokens. That is not a typo. The price-to-intelligence ratio here is simply off the charts.

I’ve been using Gemini 2.5 Flash in production for a while because it was good enough, but Gemini 3 Flash is a different beast. It scores 71 on the Artificial Analysis Intelligence Index (a 13-point jump from the previous Flash), and it achieves 78% on SWE-bench Verified. That actually beats the bigger Gemini 3 Pro on some agentic coding tasks!

What impressed me most, and something Kwindla pointed out, is the tool calling. Previous Gemini models sometimes struggled with complex tool use compared to OpenAI, but Gemini 3 Flash can handle up to 100 simultaneous function calls. It’s fast, it’s smart, and it’s integrated immediately across the entire Google stack—Workspace, Android, Chrome. Google isn’t just releasing models anymore; they are deploying them instantly to billions of users.

For anyone building agents, this combination of speed, low latency, and 1 million context window (at this price!) makes it the new default workhorse.

Google’s FunctionGemma Open Source release

We also got a smaller, quirkier release from Google: FunctionGemma. This is a tiny 270M parameter model. Yes, millions, not billions.

It’s purpose-built for function calling on edge devices. It requires only 500MB of RAM, meaning it can run on your phone, in your browser, or even on a Raspberry Pi. As Nisten joked on the show, this is finally the model that lets your toaster talk to your fridge.

Is it going to write a novel? No. But after fine-tuning, it jumped from 58% to 85% accuracy on mobile action tasks. This represents a future where privacy-first agents live entirely on your device, handling your calendar and apps without ever pinging a cloud server.

OpenAI Image 1.5, GPT 5.2 Codex and ChatGPT Appstore

OpenAI had a busy week, starting with the release of GPT Image 1.5. It’s available now in ChatGPT and the API. The headline here is speed and control—it’s 4x faster than the previous model and 20% cheaper. It also tops the LMSYS Image Arena leaderboards.

However, I have to give a balanced take here. We’ve been spoiled recently by Google’s “Nano Banana Pro” image generation (which powers Gemini). When we looked at side-by-side comparisons, especially with typography and infographic generation, Gemini often looked sharper and more coherent. This is what we call “hedonistic adaptation”—GPT Image 1.5 is great, but the bar has moved so fast that it doesn’t feel like the quantum leap DALL-E 3 was back in the day. Still, for production workflows where you need to edit specific parts of an image without ruining the rest, this is a massive upgrade.

🚨 BREAKING: GPT 5.2 Codex

Just as we were nearing the end of the show, OpenAI decided to drop some breaking news: GPT 5.2 Codex.

This is a specialized model optimized specifically for agentic coding, terminal workflows, and cybersecurity. We quickly pulled up the benchmarks live, and they look significant. It hits 56.4% on SWE-Bench Pro and a massive 64% on Terminal-Bench 2.0.

It supports up to 400k token inputs with native context compaction, meaning it’s designed for those long, complex coding sessions where you’re debugging an entire repository. The coolest (and scariest?) stat: a security researcher used this model to find three previously unknown vulnerabilities in React in just one week.

OpenAI is positioning this for “professional software engineering,” and the benchmarks suggest a 30% improvement in token efficiency over the standard GPT 5.2. We are definitely going to be putting this through its paces in our own evaluations soon.

ChatGPT ... the AppStore!

Also today (OpenAI is really throwing everything they have to the end of the year release party), OpenAI has unveiled how their App Store is going to look and opened the submission forms to submit your own apps!

Reminder, ChatGPT apps are powered by MCP and were announced during DevDay, they let companies build a full UI experience right inside ChatGPT, and given OpenAi’s almost 900M weekly active users, this is a big deal! Do you have an app you’d like in there? let me know in the comments!

Open Source AI

🔥 Nvidia Nemotron 3 Nano: The Most Important Open Source Release of the Week (X, HF)

I think the most important release of this week in open source was Nvidia Nemotron 3 Nano, and it was pretty much everywhere. Nemotron is a series of models from Nvidia that’s been pushing efficiency updates, finetune innovations, pruning, and distillations—all the stuff Nvidia does incredibly well.

Nemotron 3 Nano is a 30 billion parameter model with only 3 billion active parameters, using a hybrid Mamba-MoE architecture. This is huge. The model achieves 1.5 to 3.3x faster inference than competing models like Qwen 3 while maintaining competitive accuracy on H200 GPUs.

But the specs aren’t even the most exciting part. NVIDIA didn’t just dump the weights over the wall. They released the datasets—all 25 trillion tokens of pre-training and post-training data. They released the recipes. They released the technical reports. This is what “Open AI” should actually look like.

What’s next? Nemotron 3 Super at 120B parameters (4x Nano) and Nemotron 3 Ultra at 480B parameters (16x Nano) are coming in the next few months, featuring their innovative Latent Mixture of Experts architecture.

Check out the release on HuggingFace

Other Open Source Highlights

LDJ brought up BOLMO from Allen AI—the first byte-level model that actually reaches parity with similar-size models using regular tokenization. This is really exciting because it could open up new possibilities for spelling accuracy, precise code editing, and potentially better omnimodality since ultimately everything is bytes—images, audio, everything.

Wolfram highlighted OLMO 3.1, also from Allen AI, which is multimodal with video input in three sizes (4B, 7B, 8B). The interesting feature here is that you can give it a video, ask something like “how many times does a ball hit the crown?” and it’ll not only give you the answer but mark the precise coordinates on the video frames where it happens. Very cool for tracking objects throughout a video!

Mistral OCR 3 (X)

Mistral also dropped Mistral OCR 3 this week—their next-generation document intelligence model achieving a 74% win rate over OCR 2 across challenging document types. We’re talking forms, low-quality scans, handwritten text, complex tables, and multilingual documents.

The pricing is aggressive at just $2 per 1,000 pages (or $1 with Batch API discount), and it outperforms enterprise solutions like AWS Textract, Azure Doc AI, and Google DocSeek. Available via API and their new Document AI Playground.

🐝 This Week’s Buzz: Wolfram Joins Weights & Biases!

I am so, so hyped to announce this. Our very own co-host and evaluation wizard, Wolfram RavenWlf, is officially joining the Weights & Biases / CoreWeave family as an AI Evangelist and “AIvaluator” starting in January!

Wolfram has been the backbone of the “vibe checks” and deep-dive evals on this show for a long time. Now, he’ll be doing it full-time, building out benchmarks for the community and helping all of us make sense of this flood of models. Expect ThursdAI to get even more data-driven in 2026. Match made in heaven! And if you’re as excited as we are, give Weave a try, it’s free to get started!

Voice & Audio: Faster, Cheaper, Better

If 2025 was the year of the LLM comeback, the end of 2025 is the era of Voice AI commoditization. It is getting so cheap and so fast.

Grok Voice Agent API (X)

xAI launched their Grok Voice Agent API, and the pricing is aggressive: $0.05 per minute flat rate. That significantly undercuts OpenAI and others. But the real killer feature here is the integration.

If you drive a Tesla, this is what powers the voice command when you hold down the button. It has native access to vehicle controls, but for developers, it has native tool calling for Real-time X Search. This means your voice agent can have up-to-the-minute knowledge about the world, something purely pre-trained models struggle with. It ranks #1 on Big Bench Audio, and with that pricing, we’re going to see voice ubiquity very soon.

Kwindla had great insights here: it feels like they optimized for the Tesla use case where it’s a question and an answer. You can see this because Big Bench Audio is a hard audio Q&A benchmark but not multi-turn. So it’s super exciting, but it’s not necessarily what we’ll use for multi-turn conversational voice agents yet.

Here’s what’s really interesting: the entire voice stack was built in-house with custom VAD, tokenizer, and audio models for end-to-end optimization. Tesla was a critical design partner—Grok now powers millions of Tesla vehicles. If you’re building AI voice agents, will you give Grok Voice SDK a try?

Resemble AI’s Chatterbox Turbo (X, HF, GitHub, Blog)

For the open-source heads, Resemble AI dropped a bombshell with Chatterbox Turbo. This is a 350M parameter open-source TTS model that is beating proprietary giants like ElevenLabs in blind tests.

It allows for zero-shot voice cloning from just 5 seconds of audio and supports paralinguistic tags—meaning you can type [laugh] or [sigh]and the model actually acts it out naturally. Plus, it has built-in watermarking for safety. It’s MIT licensed, so you can run this yourself. The fact that an open model is winning on quality against the paid APIs is a huge moment for the community.

Meta SAM Audio

Finally, Meta extended their “Segment Anything” magic to audio with SAM Audio. You know how you can click an object in an image to select it? Now you can do that with sound.

With Sam Audio, you could isolate just the sound of a train from a messy audio track, or pick out a specific instrument from a song. You can prompt it with text (”guitar”), visual clicks on a video, or time stamps. It’s incredible for creators and audio engineers, effectively automating what used to be painful manual editing.

Wrapping Up

What a week to close out 2025. Google proved once again that they’re the gorilla that’s learned to dance—Gemini 3 Flash delivering frontier intelligence at flash prices is going to change how people build AI applications. Nvidia showed that the most valuable company in the world is all-in on open source. OpenAI fired off GPT 5.2 Codex just to make sure we don’t forget about them. And the voice AI space is heating up with options that would have seemed impossible just a year ago.

Look out for the full 2025 yearly recap episode coming next week—it’s a banger. We went month by month through every major AI release and talked about what we thought were the best overall. You’ll really feel the acceleration from that one.

Happy holidays, folks! And as always, thanks for being part of the ThursdAI community.

TL;DR and Show Notes

Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed, @ryancarson

* Special Guest: @kwindla - CEO of Daily

Open Source LLMs

* NVIDIA Nemotron 3 Nano - 30B-3A hybrid Mamba-MoE model (X, HF, HF FP8)

* FunctionGemma - 270M parameter function calling model (X, Blog, Docs)

* Mistral OCR 3 - Document intelligence model with 74% win rate over v2 (X, Blog, Console)

* BOLMO from Allen AI - First byte-level model reaching parity with regular tokenization (X)

* OLMO 2 from Allen AI - Multimodal with video input (4B, 7B, 8B sizes) (X)

Big CO LLMs + APIs

* Google Gemini 3 Flash - Frontier intelligence at $0.50/1M input tokens, 78% SWE-bench Verified (X, Announcement)

* OpenAI GPT Image 1.5 - 4x faster, 20% cheaper, #1 on LMSYS Image Arena (X)

* OpenAI GPT 5.2 Codex - 56.4% SWE-Bench Pro, 64% Terminal-Bench 2.0, 400K context (X, Blog)

* ChatGPT App Store - MCP-powered apps submission now open (X)

This Week’s Buzz

* 🐝 Wolfram joins Weights & Biases / CoreWeave as AI Evangelist and AIvaluator!

* Try Weave for AI evaluations

Voice & Audio

* xAI Grok Voice Agent API - #1 Big Bench Audio (92.3%), $0.05/min flat rate, powers Tesla vehicles (X)

* Resemble AI Chatterbox Turbo - MIT-licensed 350M TTS, beats ElevenLabs in blind tests (X, HF, GitHub, Blog)

* Meta SAM Audio - Audio source separation with text/visual/temporal prompts (X, HF, GitHub)

Show Links

* Full 2025 Yearly Recap - Coming next week!

December started strong and does NOT want to slow down!? OpenAI showed us their response to the Code Red and it’s GPT 5.2, which doesn’t feel like a .1 upgrade! We got it literally as breaking news at the end of the show, and oh boy! The new kind of LLMs is here.

GPT, then Gemini, then Opus and now GPT again... Who else feels like we’re on a trippy AI rolercoaster? Just me? 🫨

I’m writing this newsletter from a fresh “traveling podcaster” setup in SF (huge shoutout to the Chroma team for the studio hospitality).

P.S - Next week we’re doing a year recap episode (52st episode of the year, what is my life), but today is about the highest-signal stuff that happened this week.

Alright. No more foreplay. Let’s dive in. Please subscribe.

🔥 The main event: OpenAI launches GPT‑5.2 (and it’s… a lot)

We started the episode with “garlic in the air” rumors (OpenAI holiday launches always have that Christmas panic energy), and then… boom: GPT‑5.2 actually drops while we’re live.

What makes this release feel significant isn’t “one benchmark went up.” It’s that OpenAI is clearly optimizing for the things that have become the frontier in 2025: long-horizon reasoning, agentic coding loops, long context reliability, and lower hallucination rates when browsing/tooling is involved.

5.2 Instant, Thinking and Pro in ChatGPT and in the API

OpenAI shipped multiple variants, and even within those there are “levels” (medium/high/extra-high) that effectively change how much compute the model is allowed to burn. At the extreme end, you’re basically running parallel thoughts and selecting winners. That’s powerful, but also… very expensive.

It’s very clearly aimed at the agentic world: coding agents that run in loops, tool-using research agents, and “do the whole task end-to-end” workflows where spending extra tokens is still cheaper than spending an engineer day.

Benchmarks

I’m not going to pretend benchmarks tell the full story (they never do), but the shape of improvements matters. GPT‑5.2 shows huge strength on reasoning + structured work.

It hits 90.5% on ARC‑AGI‑1 in the Pro X‑High configuration, and 54%+ on ARC‑AGI‑2 depending on the setting. For context, ARC‑AGI‑2 is the one where everyone learns humility again.

On math/science, this thing is flexing. We saw 100% on AIME 2025, and strong performance on FrontierMath tiers (with the usual “Tier 4 is where dreams go to die” vibe still intact). GPQA Diamond is up in the 90s too, which is basically “PhD trivia mode.”

But honestly the most practically interesting one for me is GDPval (knowledge-work tasks: slides, spreadsheets, planning, analysis). GPT‑5.2 lands around 70%, which is a massive jump vs earlier generations. This is the category that translates directly into “is this model useful at my job.” - This is a bench that OpenAI launched only in September and back then, Opus 4.1 was a “measly” 47%! Talk about acceleration!

Long context: MRCR is the sleeper highlight

On MRCR (multi-needle long-context retrieval), GPT‑5.2 holds up absurdly well even into 128k and beyond. The graph OpenAI shared shows GPT‑5.1 falling off a cliff as context grows, while GPT‑5.2 stays high much deeper into long contexts.

If you’ve ever built a real system (RAG, agent memory, doc analysis) you know this pain: long context is easy to offer, hard to use well. If GPT‑5.2 actually delivers this in production, it’s a meaningful shift.

Hallucinations: down (especially with browsing)

One thing we called out on the show is that a bunch of user complaints in 2025 have basically collapsed into one phrase: “it hallucinates.” Even people who don’t know what a benchmark is can feel when a model confidently lies.

OpenAI’s system card shows lower rates of major incorrect claims compared to GPT‑5.1, and lower “incorrect claims” overall when browsing is enabled. That’s exactly the direction they needed.

Real-world vibes:

We did the traditional “vibe tests” mid-show: generate a flashy landing page, do a weird engineering prompt, try some coding inside Cursor/Codex.

Early testers broadly agree on the shape of the improvement. GPT‑5.2 is much stronger in reasoning, math, long‑context tasks, visual understanding, and multimodal workflows, with multiple reports of it successfully thinking for one to three hours on hard problems. Enterprise users like Box report faster execution and higher accuracy on real knowledge‑worker tasks, while researchers note that GPT‑5.2 Pro consistently outperforms the standard “Thinking” variant. The tradeoffs are also clear: creative writing still slightly favors Claude Opus, and the highest reasoning tiers can be slow and expensive. But as a general‑purpose reasoning model, GPT‑5.2 is now the strongest publicly available option.

AI in space: Starcloud trains an LLM on an H100 in orbit

This story is peak 2025.

Starcloud put an NVIDIA H100 on a satellite, trained Andrej Karpathy’s nanoGPT on Shakespeare, and ran inference on Gemma. There’s a viral screenshot vibe here that’s impossible to ignore: SSH into an H100… in space… with a US flag in the corner. It’s engineered excitement, and I’m absolutely here for it.

But we actually had a real debate on the show: is “GPUs in space” just sci‑fi marketing, or does it make economic sense?

Nisten made a compelling argument that power is the real bottleneck, not compute, and that big satellites already operate in the ~20kW range. If you can generate that power reliably with solar in orbit, the economics start looking less insane than you’d think. LDJ added the long-term land/power convergence argument: Earth land and grid power get scarcer/more regulated, while launch costs trend down—eventually the curves may cross.

I played “voice of realism” for a minute: what happens when GPUs fail? It’s hard enough to swap a GPU in a datacenter, now imagine doing it in orbit. Cooling and heat dissipation become a different engineering problem too (radiators instead of fans). Networking is nontrivial. But also: we are clearly entering the era where people will try weird infra ideas because AI demand is pulling the whole economy.

Big Company: MCP gets donated, OpenRouter drops a report on AI

Agentic AI Foundation Lands at the Linux Foundation

This one made me genuinely happy.

Block, Anthropic, and OpenAI came together to launch the Agentic AI Foundation under the Linux Foundation, donating key projects like MCP, AGENTS.md, and goose. This is exactly how standards should happen: vendor‑neutral, boring governance, lots of stakeholders.

It’s not flashy work, but it’s the kind of thing that actually lets ecosystems grow without fragmenting.

BTW, I was recording my podcast while Latent.Space were recording theirs in the same office, and they have a banger episode upcoming about this very topic! All I’ll say is Alessio Fanelli introduced me to David Soria Parra from MCP 👀 Watch out for that episode on Latent space dropping soon!

OpenRouter’s “State of AI”: 100 Trillion Tokens of Reality

OpenRouter and a16z dropped a massive report analyzing over 100 trillion tokens of real‑world usage. A few things stood out:

Reasoning tokens now dominate. Above 50%, around 60% of all tokens since early 2025 are reasoning tokens. Remember when we went from “LLMs can’t do math” to reasoning models? That happened in about a year.

Programming exploded. From 11% of usage early 2025 to over 50% recently. Claude holds 60% of the coding market. (at least.. on Open Router)

Open source hit 30% market share, led by Chinese labs: DeepSeek (14T tokens), Qwen (5.59T), Meta LLaMA (3.96T).

Context lengths grew massively. Average prompt length went from 1.5k to 6k+ tokens (4x growth), completions from 133 to 400 tokens (3x).

The “Glass Slipper” effect. When users find a model that fits their use case, they stay loyal. Foundational early-user cohorts retain around 40% at month 5. Claude 4 Sonnet still had 50% retention after three months.

Geography shift. Asia doubled to 31% of usage (China key), while North America is at 47%.

Yam made a good point that we should be careful interpreting these graphs—they’re biased toward people trying new models, not necessarily steady usage. But the trends are clear: agentic, reasoning, and coding are the dominant use cases.

Open Source Is Not Slowing Down (If Anything, It’s Accelerating)

One of the strongest themes this week was just how fast open source is closing the gap — and in some areas, outright leading. We’re not talking about toy demos anymore. We’re talking about serious models, trained from scratch, hitting benchmarks that were frontier‑only not that long ago.

Essential AI’s Rnj‑1: A Real Frontier 8B Model

This one deserves real attention. Essential AI — led by Ashish Vaswani, yes Ashish from the original Transformers paper — released Rnj‑1, a pair of 8B open‑weight models trained fully from scratch. No distillation. No “just a fine‑tune.” This is a proper pretrain.

What stood out to me isn’t just the benchmarks (though those are wild), but the philosophy. Rnj‑1 is intentionally focused on pretraining quality: data curation, code execution simulation, STEM reasoning, and agentic behaviors emerging during pretraining instead of being bolted on later with massive RL pipelines.

In practice, that shows up in places like SWE‑bench Verified, where Rnj‑1 lands in the same ballpark as much larger closed models, and in math and STEM tasks where it punches way above its size. And remember: this is an 8B model you can actually run locally, quantize aggressively, and deploy without legal gymnastics thanks to its Apache 2.0 license.

Mistral Devstral 2 + Vibe: Open Coding Goes Hard

Mistral followed up last week’s momentum with Devstral 2, and Mistral Vibe!

The headline numbers are: the 123B Devstral 2 model lands right at the top of open‑weight coding benchmarks, nearly matching Claude 3.5 Sonnet on SWE‑bench Verified. But what really excited the panel was the 24B Devstral Small 2, which hits high‑60s SWE‑bench scores while being runnable on consumer hardware.

This is the kind of model you can realistically run locally as a coding agent, without shipping your entire codebase off to someone else’s servers. Pair that with Mistral Vibe, their open‑source CLI agent, and you suddenly have a credible, fully open alternative to things like Claude Code, Codex, or Gemini CLI.

We talked a lot about why this matters. Some teams can’t send code to closed APIs. Others just don’t want to pay per‑token forever. And some folks — myself included — just like knowing what’s actually running under the hood. Devstral 2 checks all those boxes.

🐝 This week’s Buzz (W&B): Trace OpenRouter traffic into Weave with zero code

We did a quick “Buzz” segment on a feature that I think a lot of builders will love:

OpenRouter launched Broadcast, which can stream traces to observability vendors. One of those destinations is W&B Weave.

The magic here is: if you’re using a tool that already talks to OpenRouter, you can get tracing into Weave without instrumenting your code. That’s especially useful when instrumentation is hard (certain agent frameworks, black-box tooling, restricted environments, etc.).

If you want to set it up: OpenRouter Broadcast settings.

Vision Models Are Getting Practical (and Weirdly Competitive)

Vision‑language models quietly had a massive week.

Jina‑VLM: Small, Multilingual, and Very Good at Docs

Jina released a 2.4B VLM that’s absolutely dialed in on document understanding, multilingual VQA, and OCR‑heavy tasks. This is exactly the kind of model you’d want for PDFs, charts, scans, and messy real‑world docs — and it’s small enough to deploy without sweating too much.

Z.ai GLM‑4.6V: Long Context, Tool Calling, Serious Agent Potential

Z.ai’s GLM‑4.6V impressed us with its 128K context, native tool calling from vision inputs, and strong performance on benchmarks like MathVista and WebVoyager. It’s one of the clearest examples yet of a VLM that’s actually built for agentic workflows, not just answering questions about images.

That said, I did run my unofficial “bee counting test” on it… and yeah, Gemini still wins there 😅

Perceptron Isaac 0.2: Tiny Models, Serious Perception

Perceptron’s Isaac 0.2 (1B and 2B variants) showed something I really like seeing: structured outputs, focus tools, and reliability in very small models. Watching a 2B model correctly identify, count, and point to objects in an image is still wild to me.

These are the kinds of models that make physical AI, robotics, and edge deployments actually feasible.

🧰 Tools: Cursor goes visual, and Google Stitch keeps getting scarier (in a good way)

Cursor: direct visual editing inside the codebase

Cursor shipped a new feature that lets you visually manipulate UI elements—click/drag/resize—directly in the editor. We lumped this under “tools” because it’s not just a nicety; it’s the next step in “IDE as design surface.”

Cursor is also iterating fast on debugging workflows. The meta trend: IDEs are turning into agent platforms, not text editors.

Stitch by Google: Gemini 3 Pro as default, plus clickable prototypes

I showed Stitch on the show because it’s one of the clearest examples of “distribution beats raw capability.”

Stitch (Google’s product born from the Galileo AI acquisition) is doing Shipmas updates and now defaults to “Thinking with Gemini 3 Pro.” It can generate complex UIs, export them, and even stitch multiple screens into prototypes. The killer workflow is exporting directly into AI Studio / agent tooling so you can go from UI idea → code → repo without playing copy-paste Olympics.

Site:

https://stitch.withgoogle.com

🎬 Disney invests $1B into OpenAI (and Sora gets Disney characters)

This is the corporate story that made me do a double take.

Disney—arguably the most IP-protective company on Earth—is investing $1B into OpenAI and enabling use of Disney characters in Sora. That’s huge. It signals the beginning of a more explicit “licensed synthetic media” era, where major IP holders decide which model vendors get official access.

It also raises the obvious question: does Disney now go harder against other model providers that generate Disney-like content without permission?

We talked about how weird the timing is too, given Disney has also been sending legal pressure in the broader space. The next year of AI video is going to be shaped as much by licensing and distribution as by model quality.

Closing thoughts: the intelligence explosion is loud, messy, and accelerating

This episode had everything: open-source models catching up fast, foundation-level standardization around agents, a usage report that shows what developers actually do with LLMs, voice models getting dramatically better, and OpenAI shipping what looks like a serious “we’re not losing” answer to Gemini 3.

And yes: we’re also apparently putting GPUs in space.

Next week’s episode is our year recap, and—of course—we now have to update it because GPT‑5.2 decided to show up like the final boss.

If you missed any part of the show, check out the chapters in the podcast feed and jump around. See you next week.

TL;DR + Show Notes (links for everything)

Hosts

* Alex Volkov — AI Evangelist @ Weights & Biases: @altryne. I host ThursdAI and spend an unhealthy amount of time trying to keep up with this firehose of releases.

* Co-hosts — @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed. Each of them brings a different “lens” (agents, infra, evaluation, open source, tooling), and it’s why the show works.

Open Source LLMs

* Essential AI — RNJ‑1 (8B base + instruct): tweet, blog, HF instruct, HF base. This is a from-scratch open pretrain led by Ashish Vaswani, and it’s one of the most important “Western open model” signals we’ve seen in a while.

* Mistral — Devstral 2 + Devstral Small 2 + Mistral Vibe: tweet, Devstral Small 2 HF, Devstral 2 HF, news, mistral-vibe GitHub. Devstral is open coding SOTA territory, and Vibe is Mistral’s swing at the CLI agent layer.

AI in Space

* Starcloud trains and runs an LLM in orbit on an H100: Philip Johnston, Adi Oltean, CNBC, Karpathy reaction. A satellite H100 trained nanoGPT on Shakespeare and ran Gemma inference, igniting a real debate about power, cooling, repairability, and future orbital compute economics.

Putnam Math Competition

* Nous Research — Nomos 1 (Putnam scoring run): tweet, HF, GitHub harness, Hillclimb. This is a strong open-weight math reasoning model plus an open harness, and it shows how orchestration matters as much as raw weights.

* Axiom — AxiomProver formal Lean proofs on Putnam: tweet, repo. Formal proofs are the “no excuses” version of math reasoning, and this is a serious milestone even if you argue about exact framing.

Big Company LLMs + APIs

* OpenAI — GPT‑5.2 release: Alex tweet, OpenAI announcement, ARC Prize verification, Sam Altman tweet. GPT‑5.2 brings major jumps in reasoning, long context, and agentic workflows, and it’s clearly positioned as an answer to the Gemini 3 era.

* OpenRouter x a16z — State of AI report (100T+ tokens): tweet, landing page, PDF. The report highlights the dominance of programming/agents, the rise of reasoning tokens, and real-world usage patterns that explain why everyone is shipping agent harnesses.

* Agentic AI Foundation under Linux Foundation (AAIF): Goose tweet, Block blog, aaif.io, Linux Foundation tweet. MCP + AGENTS.md + Goose moving into vendor-neutral governance is huge for interoperability and long-term ecosystem stability.

* Disney invests $1B into OpenAI / Sora characters: (covered on the show as a major IP + distribution moment). This is an early signal of licensed synthetic media becoming a first-class business line rather than a legal gray zone.

This week’s Buzz (W&B)

* OpenRouter Broadcast → W&B Weave tracing: Broadcast settings. You can trace OpenRouter-based traffic into Weave with minimal setup, which is especially useful when you can’t (or don’t want to) instrument code directly.

Vision & Video

* Jina — jina‑VLM (2.4B): tweet, arXiv, HF, blog. A compact multilingual VLM optimized for doc understanding and VQA.

* Z.ai — GLM‑4.6V + Flash: tweet, HF collection, GLM‑4.6V, Flash, blog. Strong open VLMs with tool calling and long context, even if my bee counting test still humbled it.

* Perceptron — Isaac 0.2 (1B/2B): tweet, HF 2B, HF 1B, blog, demo. The Focus/zoom tooling and structured outputs point toward “VLMs as reliable perception modules,” not just chatty describers.

Voice & Audio

* Google DeepMind — Gemini 2.5 TTS (Flash + Pro): AI Studio tweet, GoogleAI devs tweet, blog, AI Studio speech playground. The key upgrades are control and consistency (emotion, pacing, multi-speaker) across many languages.

* OpenBMB — VoxCPM 1.5: tweet, HF, GitHub. Open TTS keeps getting better, and this release is especially interesting for fine-tuning and voice cloning workflows.

Tools

* Cursor — direct visual editing (new UI workflow): (covered on the show as a major step toward “IDE as design surface”). Cursor continues to push the agentic IDE category into new territory.

* Stitch by Google — Shipmas updates + Gemini 3 Pro “Thinking” + Prototypes: tweet 1, tweet 2, site, plus background articles: TechCrunch launch, acquisition detail. Stitch is turning prompt-to-UI into a full prototype-to-code pipeline with real export paths.

Welcome to the first ThursdAI of December! Snow is falling in Colorado, and AI releases are falling even harder. This week was genuinely one of those “drink from the firehose” weeks where every time I refreshed my timeline, another massive release had dropped.

We kicked off the show asking our co-hosts for their top AI pick of the week, and the answers were all over the map: Wolfram was excited about Mistral’s return to Apache 2.0, Yam couldn’t stop talking about Claude Opus 4.5 after a full week of using it, and Nisten came out of left field with an AWQ quantization of Prime Intellect’s model that apparently runs incredibly fast on a single GPU. As for me? I’m torn between Opus 4.5 (which literally fixed bugs that Gemini 3 created in my code) and DeepSeek’s gold-medal winning reasoning model.

Speaking of which, let’s dive into what happened this week, starting with the open source stuff that’s been absolutely cooking.

Open Source LLMs

DeepSeek V3.2: The Whale Returns with Gold Medals

The whale is back, folks! DeepSeek released two major updates this week: V3.2 and V3.2-Speciale. And these aren’t incremental improvements—we’re talking about an open reasoning-first model that’s rivaling GPT-5 and Gemini 3 Pro with actual gold medal Olympiad wins.

Here’s what makes this release absolutely wild: DeepSeek V3.2-Speciale is achieving 96% on AIME versus 94% for GPT-5 High. It’s getting gold medals on IMO (35/42), CMO, ICPC (10/12), and IOI (492/600). This is a 685 billion parameter MOE model with MIT license, and it literally broke the benchmark graph on HMMT 2025—the score was so high it went outside the chart boundaries. That’s how you DeepSeek, basically.

But it’s not just about reasoning. The regular V3.2 (not Speciale) is absolutely crushing it on agentic benchmarks: 73.1% on SWE-Bench Verified, first open model over 35% on Tool Decathlon, and 80.3% on τ²-bench. It’s now the second most intelligent open weights model and ranks ahead of Grok 4 and Claude Sonnet 4.5 on Artificial Analysis.

The price is what really makes this insane: 28 cents per million tokens on OpenRouter. That’s absolutely ridiculous for this level of performance. They’ve also introduced DeepSeek Sparse Attention (DSA) which gives you 2-3x cheaper 128K inference without performance loss. LDJ pointed out on the show that he appreciates how transparent they’re being about not quite matching Gemini 3’s efficiency on reasoning tokens, but it’s open source and incredibly cheap.

One thing to note: V3.2-Speciale doesn’t support tool calling. As Wolfram pointed out from the model card, it’s “designed exclusively for deep reasoning tasks.” So if you need agentic capabilities, stick with the regular V3.2.

Check out the full release on Hugging Face or read the announcement.

Mistral 3: Europe’s Favorite AI Lab Returns to Apache 2.0

Mistral is back, and they’re back with fully open Apache 2.0 licenses across the board! This is huge news for the open source community. They released two major things this week: Mistral Large 3 and the Ministral 3 family of small models.

Mistral Large 3 is a 675 billion parameter MOE with 41 billion active parameters and a quarter million (256K) context window, trained on 3,000 H200 GPUs. There’s been some debate about this model’s performance, and I want to address the elephant in the room: some folks saw a screenshot showing Mistral Large 3 very far down on Artificial Analysis and started dunking on it. But here’s the key context that Merve from Hugging Face pointed out—this is the only non-reasoning model on that chart besides GPT 5.1. When you compare it to other instruction-tuned (non-reasoning) models, it’s actually performing quite well, sitting at #6 among open models on LMSys Arena.

Nisten checked LM Arena and confirmed that on coding specifically, Mistral Large 3 is scoring as one of the best open source coding models available. Yam made an important point that we should compare Mistral to other open source players like Qwen and DeepSeek rather than to closed models—and in that context, this is a solid release.

But the real stars of this release are the Ministral 3 small models: 3B, 8B, and 14B, all with vision capabilities. These are edge-optimized, multimodal, and the 3B actually runs completely in the browser with WebGPU using transformers.js. The 14B reasoning variant achieves 85% on AIME 2025, which is state-of-the-art for its size class. Wolfram confirmed that the multilingual performance is excellent, particularly for German.

There’s been some discussion about whether Mistral Large 3 is a DeepSeek finetune given the architectural similarities, but Mistral claims these are fully trained models. As Nisten noted, even if they used similar architecture (which is Apache 2.0 licensed), there’s nothing wrong with that—it’s an excellent architecture that works. Lucas Atkins later confirmed on the show that “Mistral Large looks fantastic... it is DeepSeek through and through architecture wise. But Kimi also does that—DeepSeek is the GOAT. Training MOEs is not as easy as just import deepseak and train.”

Check out Mistral Large 3 and Ministral 3 on Hugging Face.

Arcee Trinity: US-Trained MOEs Are Back

We had Lucas Atkins, CTO of Arcee AI, join us on the show to talk about their new Trinity family of models, and this conversation was packed with insights about what it takes to train MOEs from scratch in the US.

Trinity is a family of open-weight MOEs fully trained end-to-end on American infrastructure with 10 trillion curated tokens from Datology.ai. They released Trinity-Mini (26B total, 3B active) and Trinity-Nano-Preview (6B total, 1B active), with Trinity-Large (420B parameters, 13B active) coming in mid-January 2026.

The benchmarks are impressive: Trinity-Mini hits 84.95% on MMLU (0-shot), 92.1% on Math-500, and 65% on GPQA Diamond. But what really caught my attention was the inference speed—Nano generates at 143 tokens per second on llama.cpp, and Mini hits 157 t/s on consumer GPUs. They’ve even demonstrated it running on an iPhone via MLX Swift.

I asked Lucas why it matters where models come from, and his answer was nuanced: for individual developers, it doesn’t really matter—use the best model for your task. But for Fortune 500 companies, compliance and legal teams are getting increasingly particular about where models were trained and hosted. This is slowing down enterprise AI adoption, and Trinity aims to solve that.

Lucas shared a fascinating insight about why they decided to do full pretraining instead of just post-training on other people’s checkpoints: “We at Arcee were relying on other companies releasing capable open weight models... I didn’t like the idea of the foundation of our business being reliant on another company releasing models.” He also dropped some alpha about Trinity-Large: they’re going with 13B active parameters instead of 32B because going sparser actually gave them much faster throughput on Blackwell GPUs.

The conversation about MOEs being cheaper for RL was particularly interesting. Lucas explained that because MOEs are so inference-efficient, you can do way more rollouts during reinforcement learning, which means more RL benefit per compute dollar. This is likely why we’re seeing labs like MiniMax go from their original 456B/45B-active model to a leaner 220B/10B-active model—they can get more gains in post-training by being able to do more steps.

Check out Trinity-Mini and Trinity-Nano-Preview on Hugging Face, or read The Trinity Manifesto.

OpenAI Code Red: Panic at the Disco (and Garlic?)

It was ChatGPT’s 3rd birthday this week (Nov 30th), but the party vibes seem… stressful. Reports came out that Sam Altman has declared a “Code Red” at OpenAI.

Why? Gemini 3.The user numbers don’t lie. ChatGPT apparently saw a 6% drop in daily active users following the Gemini 3 launch. Google’s integration is just too good, and their free tier is compelling.

In response, OpenAI has supposedly paused “side projects” (ads, shopping bots) to focus purely on model intelligence and speed. Rumors point to a secret model codenamed “Garlic”—a leaner, more efficient model that beats Gemini 3 and Claude Opus 4.5 on coding reasoning, targeting a release in early 2026 (or maybe sooner if they want to save Christmas).

Wolfram and Yam nailed the sentiment here: Integration wins. Wolfram’s family uses Gemini because it’s right there on the Pixel, controlling the lights and calendar. OpenAI needs to catch up not just on IQ, but on being helpful in the moment.

Post the live show, OpenAI also finally added GPT 5.1 Codex Max we covered 2 weeks ago to their API and it’s now available in Cursor, for free, until Dec 11!

Amazon Nova 2: Enterprise Push with Serious Agentic Chops

Amazon came back swinging with Nova 2, and the jump on Artificial Analysis is genuinely impressive—from around 30% to 61% on their index. That’s a massive improvement.

The family includes Nova 2 Lite (7x cheaper, 5x faster than Nova Premier), Nova 2 Pro (93% on τ²-Bench Telecom, 70% on SWE-Bench Verified), Nova 2 Sonic (speech-to-speech with 1.39s time-to-first-audio), and Nova 2 Omni (unified text/image/video/speech with 1M token context window—you can upload 90 minutes of video!).

Gemini 3 Deep Think Mode

Google launched Gemini 3 Deep Think mode exclusively for AI Ultra subscribers, and it’s hitting some wild benchmarks: 45.1% on ARC-AGI-2 (a 2x SOTA leap using code execution), 41% on Humanity’s Last Exam, and 93.8% on GPQA Diamond. This builds on their Gemini 2.5 variants that earned gold medals at IMO and ICPC World Finals. The parallel reasoning approach explores multiple hypotheses simultaneously, but it’s compute-heavy—limited to 10 prompts per day at $77 per ARC-AGI-2 task.

This Week’s Buzz: Mid-Training Evals are Here!

A huge update from us at Weights & Biases this week: We launched LLM Evaluation Jobs. (Docs)

If you are training models or finetuning, you usually wait until the end to run your expensive benchmarks. Now, directly inside W&B, you can trigger evaluations on mid-training checkpoints.

It integrates with Inspect Evals (over 100+ public benchmarks). You just point it to your checkpoint or an API endpoint (even OpenRouter!), select the evals (MMLU-Pro, GPQA, etc.), and we spin up the managed GPUs to run it. You get a real-time leaderboard of your runs vs. the field.

Also, a shoutout to users of Neptune.ai—congrats on the acquisition by OpenAI, but since the service is shutting down, we have built a migration script to help you move your history over to W&B seamlessly. We aren’t going anywhere!

Video & Vision: Physics, Audio, and Speed

The multimodal space was absolutely crowded this week.

Runway Gen 4.5 (”Whisper Thunder”)

Runway revealed that the mysterious “Whisper Thunder” model topping the leaderboards is actually Gen 4.5. The key differentiator? Physics and Multi-step adherence. It doesn’t have that “diffusion wobble” anymore. We watched a promo video where the shot changes every 3-4 seconds, and while it’s beautiful, it shows we still haven’t cracked super long consistent takes yet. But for 8-second clips? It’s apparently the new SOTA.

Kling 2.6: Do you hear that?

Kling hit back with Video 2.6, and the killer feature is Native Audio. I generated a clip of two people arguing, and the lip sync was perfect. Not “dubbed over” perfect, but actively generated with the video. It handles multi-character dialogue, singing, and SFX. It’s huge for creators.

Kling was on a roll this week, releasing not one, but two Video Models (O1 Video is an omni modal one that takes Text, Images and Audio as inputs) and O1 Image and Kling Avatar 2.0 are also great updates! (Find all their releases on X)

P-Image: Sub-Second Generation at Half a Cent

Last week we talked about ByteDance’s Z-Image, which was super cool and super cheap. Well, this week Pruna AI came out with P-Image, which is even faster and cheaper: image generation under one second for $0.005, and editing under one second for $0.01.

I built a Chrome extension this week (completely rewritten by Opus 4.5, by the way—more on that in a second) that lets me play with these new image models inside the Infinite Craft game. When I tested P-Image Turbo against Z-Image, I was genuinely impressed by the quality at that speed. If you want quick iterations before moving to something like Nano Banana Pro for final 4K output, these sub-second models are perfect.

The extension is available on GitHub if you want to try it—you just need to add your Replicate or Fal API keys.

SeeDream 4.5: ByteDance Levels Up

ByteDance also launched SeeDream 4.5 in open beta, with major improvements in detail fidelity, spatial reasoning, and multi-image reference fusion (up to 10 inputs for consistent storyboards). The text rendering is much sharper, and it supports multilingual typography including Japanese. Early tests show it competing well with Nano Banana Pro in prompt adherence and logic.

🎤 Voice & Audio

Microsoft VibeVoice-Realtime-0.5B

In a surprise drop, Microsoft open-sourced VibeVoice-Realtime-0.5B, a compact TTS model optimized for real-time applications. It delivers initial audible output in just 300 milliseconds while generating up to 10 minutes of speech. The community immediately started creating mirrors because, well, Microsoft has a history of releasing things on Hugging Face and then having legal pull them down. Get it while it’s hot!

Use Cases: Code, Cursors, and “Antigravity”

We wrapped up with some killer practical tips:

* Opus 4.5 is a beast: As I mentioned, using Opus inside Cursor’s “Ask” mode is currently the supreme coding experience. It debugs logic flaws that Gemini misses completely. I also used Opus as a prompt engineer for my infographics, and it absolutely demolished GPT at creating the specific layouts I needed

* Google’s Secret: Nisten dropped a bomb at the end of the show—Opus 4.5 is available for free inside Google’s Antigravity (and Colab)! If you want to try the model that’s beating GPT-5 without paying, go check Antigravity now before they patch it or run out of compute.

* Microsoft VibeVoice: A surprise drop of a 0.5B speech model on HuggingFace that does real-time TTS (300ms latency). It was briefly questionable if it would stay up, but mirrors are already everywhere.

That’s a wrap for this week, folks. Next week is probably going to be our final episode of the year, so we’ll be doing recaps and looking at our predictions from last year. Should be fun to see how wrong we were about everything!

Thank you for tuning in. If you missed the live stream, subscribe to our Substack, YouTube, and wherever you get your podcasts. See you next Thursday!

TL;DR and Show Notes

Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Guest - Lucas Atkins (@latkins) - CTO Arcee AI

Open Source LLMs

* DeepSeek V3.2 and V3.2-Speciale - Gold medal olympiad wins, MIT license (X, HF V3.2, HF Speciale, Announcement)

* Mistral 3 family - Large 3 and Ministral 3, Apache 2.0 (X, Blog, HF Large, HF Ministral)

* Arcee Trinity - US-trained MOE family (X, HF Mini, HF Nano, Blog)

* Hermes 4.3 - Decentralized training, SOTA RefusalBench (X, HF)

Big CO LLMs + APIs

* OpenAI Code Red - ChatGPT 3rd birthday, Garlic model in development (The Information)

* Amazon Nova 2 - Lite, Pro, Sonic, and Omni models (X, Blog)

* Gemini 3 Deep Think - 45.1% ARC-AGI-2 (X, Blog)

* Cursor + GPT-5.1-Codex-Max - Free until Dec 11 (X, Blog)

This Week’s Buzz

* WandB LLM Evaluation Jobs - Evaluate any OpenAI-compatible API (X, Announcement)

Vision & Video

* Runway Gen-4.5 - #1 on text-to-video leaderboard, 1,247 Elo (X)

* Kling VIDEO 2.6 - First native audio generation (X)

* Kling O1 Image - Image generation (X)

Voice & Audio

* Microsoft VibeVoice-Realtime-0.5B - 300ms latency TTS (X, HF)

AI Art & Diffusion

* Pruna P-Image - Sub-second generation at $0.005 (X, Blog, Demo)

* SeeDream 4.5 - Multi-reference fusion, text rendering (X)

I recorded these conversations just in front of the AI Engineer auditorium, back to back, after these great folks gave their talks, and at the epitome of the most epic AI week we’ve seen since I started recording ThursdAI.

This is less our traditional live recording, and more a real podcast-y conversation with great folks, inspired by Latent.Space. I hope you enjoy this format as much as I’ve enjoyed recording and editing it.

AntiGravity with Kevin

Kevin Hou and team just launched Antigravity, Google’s brand new Agentic IDE based on VSCode, and Kevin (second timer on ThursdAI) was awesome enough to hop on and talk about some of the product decisions they made, what makes Antigravity special and highlighted Artifacts as a completely new primitive.

Gemini 3 in AI Studio

If you aren’t using Google’s AI Studio (ai.dev) then you’re missing out! We talk about AI Studio all the time on the show, and I’m a daily user! I generate most of my images with Nano Banana Pro in there, most of my Gemini conversations are happening there as well!

Ammaar and Kat were so fun to talk to, as they covered the newly shipped “build mode” which allows you to vibe code full apps and experiences inside AI Studio, and we also covered Gemini 3’s features, multimodality understanding, UI capabilities.

These folks gave a LOT of Gemini 3 demo’s so they know everything there is to know about this model’s capabilities!

Tried new things with this one, multi camera angels, conversation with great folks, if you found this content valuable, please subscribe :)

Topics Covered:

* Inside Google’s new “AntiGravity” IDE

* How the “Agent Manager” changes coding workflows

* Gemini 3’s new multimodal capabilities

* The power of “Artifacts” and dynamic memory

* Deep dive into AI Studio updates & Vibe Coding

* Generating 4K assets with Nano Banana Pro

Timestamps for your viewing convenience.

00:00 - Introduction and Overview

01:13 - Conversation with Kevin Hou: Anti-Gravity IDE

01:58 - Gemini 3 and Nano Banana Pro Launch Insights

03:06 - Innovations in Anti-Gravity IDE

06:56 - Artifacts and Dynamic Memory

09:48 - Agent Manager and Multimodal Capabilities

11:32 - Chrome Integration and Future Prospects

20:11 - Conversation with Ammar and Kat: AI Studio Team

21:21 - Introduction to AI Studio

21:51 - What is AI Studio?

22:52 - Ease of Use and User Feedback

24:06 - Live Demos and Launch Week

26:00 - Design Innovations in AI Studio

30:54 - Generative UIs and Vibe Coding

33:53 - Nano Banana Pro and Image Generation

39:45 - Voice Interaction and Future Roadmap

44:41 - Conclusion and Final Thoughts

Looking forward to seeing you on Thursday 🫡

P.S - I’ve recorded one more conversation during AI Engineer, and will be posting that soon, same format, very interesting person, look out for that soon!

Just wrapped up the third (1, 2) Thanksgiving special Episode of ThursdAI, can you believe November is almost over?

We had another banger week in AI, with a full feast of AI released, Anthropic dropped the long awaited Opus 4.5, which quickly became the top coding LLM, DeepSeek resurfaced with a math model, BFL and Tongyi both tried to take on Nano Banana, and Microsoft dropped a 7B computer use model in Open Source + Intellect 3 from Prime Intellect!

With so much news to cover, we also had an interview with Ido Sal & Liad Yosef (their second time on the show!) about MCP-Apps, the new standard they are spearheading together with Anthropic, OpenAI & more!

Exciting episode, let’s get into it! (P.S - I started generating infographics, so the show became much more visual, LMK if you like them)

ThursdAI - I put a lot of work on a weekly basis to bring you the live show, podcast and a sourced newsletter! Please subscribe if you find this content valuable!

Anthropic’s Opus 4.5: The “Premier Intelligence” Returns (Blog)

Folks, Anthropic absolutely cooked. After Sonnet and Haiku had their time in the sun, the big brother is finally back. Opus 4.5 launched this week, and it is reclaiming the throne for coding and complex agentic tasks.

First off, the specs are monstrous. It hits 80.9% on SWE-bench Verified, topping GPT-5.1 (77.9%) and Gemini 3 Pro (76.2%). But the real kicker? The price! It is now $5 per million input tokens and $25 per million output—literally one-third the cost of the previous Opus.

Yam, our resident coding wizard, put it best during the show: “Opus knows a lot of tiny details about the stack that you didn’t even know you wanted... It feels like it can go forever.” Unlike Sonnet, which sometimes spirals or loses context on extremely long tasks, Opus 4.5 maintains coherence deep into the conversation.

Anthropic also introduced a new “Effort” parameter, allowing you to control how hard the model thinks (similar to o1 reasoning tokens). Set it to high, and you get massive performance gains; set it to medium, and you get Sonnet-level performance at a fraction of the token cost. Plus, they’ve added Tool Search (cutting enormous token overhead for agents with many tools) and Programmatic Tool Calling, which effectively lets Opus write and execute code loops to manage data.

If you are doing heavy software engineering or complex automations, Opus 4.5 is the new daily driver.

📱 The Agentic Web: MCP Apps & MCP-UI Standard

Speaking of MCP updates, Can you believe it’s been exactly one year since the Model Context Protocol (MCP) launched? We’ve been “MCP-pilled” for a while, but this week, the ecosystem took a massive leap forward.

We brought back our friends Ido and Liad, the creators of MCP-UI, to discuss huge news: MCP-UI has been officially standardized as MCP Apps. This is a joint effort adopted by both Anthropic and OpenAI.

Why does this matter? Until now, when an LLM used a tool (like Spotify or Zillow), the output was just text. It lost the brand identity and the user experience. With MCP Apps, agents can now render full, interactive HTML interfaces directly inside the chat!

Ido and Liad explained that they worked hard to avoid an “iOS vs. Android” fragmentation war. Instead of every lab building their own proprietary app format, we now have a unified standard for the “Agentic Web.” This is how AI stops being a chatbot and starts being an operating system.

Check out the standard at mcpui.dev.

🦃 The Open Source Thanksgiving Feast

While the big labs were busy, the open-source community decided to drop enough papers and weights to feed us for a month.

Prime Intellect unveils INTELLECT-3, a 106B MoE (X, HF, Blog, Try It)

Prime Intellect releases INTELLECT-3, a 106B parameter Mixture-of-Experts model (12B active params) based on GLM-4.5-Air, achieving state-of-the-art performance for its size—including ~90% on AIME 2024/2025 math contests, 69% on LiveCodeBench v6 coding, 74% on GPQA-Diamond reasoning, and 74% on MMLU-Pro—outpacing larger models like DeepSeek-R1.

Trained over two months on 512 H200 GPUs using their fully open-sourced end-to-end stack (PRIME-RL async trainer, Verifiers & Environments Hub, Prime Sandboxes), it’s now hosted on Hugging Face, OpenRouter, Parasail, and Nebius, empowering any team to scale frontier RL without big-lab resources. Especially notable is their very detailed release blog, covering how a lab that previously trained 32B, finetunes a monster 106B MoE model!

Tencent’s HunyuanOCR: Small but Mighty (X, HF, Github, Blog)

Tencent released HunyuanOCR, a 1 billion parameter model that is absolutely crushing benchmarks. It scored 860 on OCRBench, beating massive models like Qwen3-VL-72B. It’s an end-to-end model, meaning no separate detection and recognition steps. Great for parsing PDFs, docs, and even video subtitles. It’s heavily restricted (no EU/UK usage), but technically impressive.

Microsoft’s Fara-7B: On-Device Computer Use

Microsoft quietly dropped Fara-7B, a model fine-tuned from Qwen 2.5, specifically designed for computer use agentic tasks. It hits 73.5% on WebVoyager, beating OpenAI’s preview models, all while running locally on-device. This is the dream of a local agent that can browse the web for you, click buttons, and book flights without sending screenshots to the cloud.

DeepSeek-Math-V2: open-weights IMO-gold math LLM (X, HF)

DeepSeek-Math-V2 is a 685B-parameter, Apache-2.0 licensed, open-weights mathematical reasoning model claiming gold-medal performance on IMO 2025 and CMO 2024, plus a near-perfect 118/120 on Putnam 2024.

Nisten did note some limitations—specifically that the context window can get choked up on extremely long, complex proofs—but having an open-weight model of this caliber is a gift to researchers everywhere.

🐝 This Week’s Buzz: Serverless LoRA Inference

A huge update from us at Weights & Biases! We know fine-tuning is powerful, but serving those fine-tunes can be a pain and expensive. We just launched Serverless LoRA Inference.

This means you can upload your small LoRA adapters (which you can train cheaply) to W&B Artifacts, and we will serve them instantly on CoreWeave GPUs on top of a base model. No cold starts, no dedicated expensive massive GPU instances for just one adapter.

I showed a demo of a “Mocking SpongeBob” model I trained in 25 minutes. It just adds that SaRcAsTiC tExT style to the Qwen 2.5 base model. You pass the adapter ID in the API call, and boom—customized intelligence instantly. You can get more details HERE and get started with your own LORA in this nice notebook the team made!

🎨 Visuals: Image & Video Generation Explosion

Flux.2: The Multi-Reference Image Creator from BFL (X, HF, Blog)

Black Forest Labs released Flux.2, a series of models including a 32B Flux 2[DEV]. The killer feature here is Multi-Reference Editing. You can feed it up to 10 reference images to maintain character consistency, style, or specific objects. It also outputs native 4-megapixel images.

Honestly, the launch timing was rough, coming right after Google’s Nano Banana Pro and alongside Z-Image, but for precise, high-res editing, this is a serious tool.

Tongyi drops Z-Image Turbo: 6B single-stream DiT lands sub‑second, 8‑step text‑to‑image (GitHub, Hugging Face)

Alibaba’s Tongyi Lab released Z-Image Turbo, a 6B parameter model that generates images in sub-second time on H800s (and super fast on consumer cards).

I built a demo to show just how fast this is. You know that “Infinite Craft“ game? I hooked it up to Z-Image Turbo so that every time you combine elements (like Pirate + Ghost), it instantly generates the image for “Ghost Pirate.” It changes the game completely when generation is this cheap and fast.

HunyuanVideo 1.5 – open video gets very real

Tencent also shipped HunyuanVideo 1.5, which they market as “the strongest open‑source video generation model.” For once, the tagline isn’t entirely hype.

Under the hood it’s an 8.3B‑parameter Diffusion Transformer (DiT) model with a 3D causal VAE in front. The VAE compresses videos aggressively in both space and time, and the DiT backbone models that latent sequence.

The important bits for you and me:

* It generates 5–10 second clips at 480p/720p with good motion coherence and physics.

* With FP16 or FP8 configs you can run it on a single consumer GPU with around 14GB VRAM.

* There’s a built‑in path to upsample to 1080p for “real” quality.

LTX Studio Retake: Photoshop for Video (X, Try It)

For the video creators, LTX Studio launched Retake. This isn’t just “regenerate video.” This allows you to select a specific 2-second segment of a video, change the dialogue (keeping the voice!), change the emotion, or edit the action, all for like $0.10. It blends it perfectly back into the original clip. We are effectively getting a “Director Mode” for AI video where you can fix mistakes without rolling the dice on a whole new generation.

A secret new model on the Arena called Whisper Thunder beats even Veo 3?

This was a surprise of the week, while new video models get released often, Veo 3 has been the top one for a while, and now we’re getting a reshuffling of the video giants! But... we don’t yet know who this video model is from! You can sometimes get its generations at the Artificial Analysis video arena here, and the outputs look quite awesome!

Thanksgiving reflections from the ThursdAI team

As we wrapped up the show, Wolfram suggested we take a moment to think about what we’re thankful for in AI, and I think that’s a perfect note to end on.

Wolfram put it well: he’s thankful for everyone contributing to this wonderful community—the people releasing models, creating open source tools, writing tutorials, sharing knowledge. It’s not just about the money; it’s about the love of learning and building together.

Yam highlighted something I think is crucial: we’ve reached a point where there’s no real competition between open source and closed source anymore. Everything is moving forward together. Even if you think nobody’s looking at that random code you posted somewhere, chances are someone found it and used it to accelerate their own work. That collective effort is what’s driving this incredible pace of progress.

For me, I want to thank Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Ilya Polosukhin for the 2017 paper “Attention Is All You Need.” Half Joking! But without the seminal attention is you need paper none of this AI was possible. But mostly I want to thank all of you—the audience, the co-hosts, the guests—for making ThursdAI what it is.

If you go back and watch our 2024 Thanksgiving episode, or the one from 2023, you’ll be shocked at how far we’ve come. Tools that seemed magical a year ago are now just... normal. That’s hedonic adaptation at work, but it’s also a reminder to stay humble and appreciate just how incredible this moment in history really is.

We’re living through the early days of a technological revolution, and we get to document it, experiment with it, and help shape where it goes. That’s something to be genuinely thankful for.

TL;DR and Show Notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

* Guests: @idosal1 @liadyosef - MCP-UI/MCP Apps

* Big CO LLMs + APIs

* Anthropic launches Claude Opus 4.5 - world’s top model for coding, agents, and tool use (X, Announcement, Blog)

* OpenAI Integrates ChatGPT Voice Mode Directly into Chats (X)

* Open Source LLMs

* Prime Intellect - INTELLECT-3 106B MoE (X, HF, Blog, Try It)

* Tencent - HunyuanOCR 1B SOTA OCR model (X, HF, Github, Blog)

* Microsoft - Fara-7B on-device computer-use agent (X, Blog, HF, Github)

* DeepSeek - Math-V2 IMO-gold math LLM (HF)

* Interview: MCP Apps

* MCP-UI standardized as MCP Apps by Anthropic and OpenAI (X, Blog, Announcement)

* Vision & Video

* Tencent - HunyuanVideo 1.5 lightweight DiT open video model (X, GitHub, HF)

* LTX Studio - Retake AI video editing tool (X, Try It)

* Whisper Thunder - mystery #1 ranked video model on arena

* AI Art & Diffusion

* Black Forest Labs - FLUX.2 32B multi-reference image model (X, HF, Blog)

* Tongyi - Z-Image Turbo sub-second 6B image gen (GitHub, HF)

* This Week’s Buzz

* W&B launches Serverless LoRA Inference on CoreWeave (X, Blog, Notebook)

I’m writing this one from a noisy hallway at the AI Engineer conference in New York, still riding the high (and the sleep deprivation) from what might be the craziest week we’ve ever had in AI.

In the span of a few days:

Google dropped Gemini 3 Pro, a new Deep Think mode, generative UIs, and a free agent-first IDE called Antigravity.xAI shipped Grok 4.1, then followed it up with Grok 4.1 Fast plus an Agent Tools API.OpenAI answered with GPT‑5.1‑Codex‑Max, a long‑horizon coding monster that can work for more than a day, and quietly upgraded ChatGPT Pro to GPT‑5.1 Pro.Meta looked at all of that and said “cool, we’ll just segment literally everything and turn photos into 3D objects” with SAM 3 and SAM 3D.Robotics folks dropped a home robot trained with almost no robot data.And Google, just to flex, capped Thursday with Nano Banana Pro, a 4K image model and a provenance system while we were already live!

For the first time in a while it doesn’t just feel like “new models came out.” It feels like the future actually clicked forward a notch.

This is why ThursdAI exists. Weeks like this are basically impossible to follow if you have a day job, so my co‑hosts and I do the no‑sleep version so you don’t have to. Plus, being at AI Engineer makes it easy to get super high quality guests so this week we had 3 folks join us, Swyx from Cognition/Latent Space, Thor from DeepMind (on his 3rd day) and Dominik from OpenAI! Alright, deep breath. Let’s untangle the week.

TL;DR

If you only skim one section, make it this one (links in the end):

* Google

* Gemini 3 Pro: 1M‑token multimodal model, huge reasoning gains - new LLM king

* ARC‑AGI‑2: 31.11% (Pro), 45.14% (Deep Think) – enormous jumps

* Antigravity IDE: free, Gemini‑powered VS Code fork with agents, plans, walkthroughs, and browser control

* Nano Banana Pro: 4K image generation with perfect text + SynthID provenance; dynamic “generative UIs” in Gemini

* xAI

* Grok 4.1: big post‑training upgrade – #1 on human‑preference leaderboards, much better EQ & creative writing, fewer hallucinations

* Grok 4.1 Fast + Agent Tools API: 2M context, SOTA tool‑calling & agent benchmarks (Berkeley FC, T²‑Bench, research evals), aggressive pricing and tight X + web integration

* OpenAI

* GPT‑5.1‑Codex‑Max: “frontier agentic coding” model built for 24h+ software tasks with native compaction for million‑token sessions; big gains on SWE‑Bench, SWE‑Lancer, TerminalBench 2

* GPT‑5.1 Pro: new “research‑grade” ChatGPT mode that will happily think for minutes on a single query

* Meta

* SAM 3: open‑vocabulary segmentation + tracking across images and video (with text & exemplar prompts)

* SAM 3D: single‑image → 3D objects & human bodies; surprisingly high‑quality 3D from one photo

* Robotics

* Sunday Robotics – ACT‑1 & Memo: home robot foundation model trained from a $200 skill glove instead of $20K teleop rigs; long‑horizon household tasks with solid zero‑shot generalization

* Developer Tools

* Antigravity and Marimo’s VS Code / Cursor extension both push toward agentic, reactive dev workflows

Live from AI Engineer New York: Coding Agents Take Center Stage

We recorded this week’s show on location at the AI Engineer Summit in New York, inside a beautiful podcast studio the team set up right on the expo floor. Huge shout out to Swyx, Ben, and the whole AI Engineer crew for that — last time I was balancing a mic on a hotel nightstand, this time I had broadcast‑grade audio while a robot dog tried to steal the show behind us.

This year’s summit theme is very on‑the‑nose for this week: coding agents.

Everywhere you look, there’s a company building an “agent lab” on top of foundation models. Amp, Cognition, Cursor, CodeRabbit, Jules, Google Labs, all the open‑source folks, and even the enterprise players like Capital One and Bloomberg are here, trying to figure out what it means to have real software engineers that are partly human and partly model.

Swyx framed it nicely when he said that if you take “vertical AI” seriously enough, you eventually end up building an agent lab. Lawyers, healthcare, finance, developer tools — they all converge on “agents that can reason and code.”

The big labs heard that theme loud and clear. Almost every major release this week is about agents, tools, and long‑horizon workflows, not just chat answers.

Google Goes All In: Gemini 3 Pro, Antigravity, and the Agent Revolution

Let’s start with Google because, after years of everyone asking “where’s Google?” in the AI race, they showed up this week with multiple bombshells that had even the skeptics impressed.

Gemini 3 Pro: Multimodal Intelligence That Actually Delivers

Google finally released Gemini 3 Pro, and the numbers are genuinely impressive. We’re talking about a 1 million token context window, massive benchmark improvements, and a model that’s finally competing at the very top of the intelligence charts. Thor from DeepMind joined us on the show (literally on day 3 of his new job!) and you could feel the excitement.

The headline numbers: Gemini 3 Pro with Deep Think mode achieved 45.14% on ARC-AGI-2—that’s roughly double the previous state-of-the-art on some splits. For context, ARC-AGI has been one of those benchmarks that really tests genuine reasoning and abstraction, not just memorization. The standard Gemini 3 Pro hits 31.11% on the same benchmark, both scores are absolutely out of this world in Arc!

On GPQA Diamond, Gemini 3 Pro jumped about 10 points compared to prior models. We’re seeing roughly 81% on MMLU-Pro, and the coding performance is where things get really interesting—Gemini 3 Pro is scoring around 56% on SciCode, representing significant improvements in actual software engineering tasks.

But here’s what made Ryan from Amp switch their default model to Gemini 3 Pro immediately: the real-world usability. Ryan told us on the show that they’d never switched default models before, not even when GPT-5 came out, but Gemini 3 Pro was so noticeably better that they made it the default on Tuesday. Of course, they hit rate limits almost immediately (Google had to scale up fast!), but those have since been resolved.

Antigravity: Google’s Agent-First IDE

Then Google dropped Antigravity, and honestly, this might be the most interesting part of the whole release. It’s a free IDE (yes, free!) that’s basically a fork of VS Code, but reimagined around agents rather than human-first coding.

The key innovation here is something they call the “Agent Manager”—think of it like an inbox for your coding agents. Instead of thinking in folders and files, you’re managing conversations with agents that can run in parallel, handle long-running tasks, and report back when they need your input.

I got early access and spent time playing with it, and here’s what blew my mind: you can have multiple agents working on different parts of your codebase simultaneously. One agent fixing bugs, another researching documentation, a third refactoring your CSS—all at once, all coordinated through this manager interface.

The browser integration is crazy too. Antigravity can control Chrome directly, take screenshots and videos of your app, and then use those visuals to debug and iterate. It’s using Gemini 3 Pro for the heavy coding, and even Nano Banana for generating images and assets. The whole thing feels like it’s from a couple years in the future.

Wolfram on the show called out how good Gemini 3 is for creative writing too—it’s now his main model, replacing GPT-4.5 for German language tasks. The model just “gets” the intention behind your prompts rather than following them literally, which makes for much more natural interactions.

Nano Banana Pro: 4K Image Generation With Thinking

And because Google apparently wasn’t done announcing things, they also dropped Nano Banana Pro on Thursday morning—literally breaking news during our live show. This is their image generation model that now supports 4K resolution and includes “thinking” traces before generating.

I tested it live by having it generate an infographic about all the week’s AI news (which you can see on the top), and the results were wild. Perfect text across the entire image (no garbled letters!), proper logos for all the major labs, and compositional understanding that felt way more sophisticated than typical image models. The file it generated was 8 megabytes—an actual 4K image with stunning detail.

What’s particularly clever is that Nano Banana Pro is really Gemini 3 Pro doing the thinking and planning, then handing off to Nano Banana for the actual image generation. So you get multimodal reasoning about your request, then production-quality output. You can even upload reference images—up to 14 of them—and it’ll blend elements while maintaining consistency.

Oh, and every image is watermarked with SynthID (Google’s invisible watermarking tech) and includes C2PA metadata, so you can verify provenance. This matters as AI-generated content becomes more prevalent.

Generative UIs: The Future of Interfaces

One more thing Google showed off: generative UIs in the Gemini app. Wolfram demoed this for us, and it’s genuinely impressive. Instead of just text responses, Gemini can generate full interactive mini-apps on the fly—complete dashboards, data visualizations, interactive widgets—all vibe-coded in real time.

He asked for “four panels of the top AI news from last week” and Gemini built an entire news dashboard with tabs, live market data (including accurate pre-market NVIDIA stats!), model comparisons, and clickable sections. It pulled real information, verified facts, and presented everything in a polished UI that you could interact with immediately.

This isn’t just a demo—it’s rolling out in Gemini now. The implication is huge: we’re moving from static responses to dynamic, contextual interfaces generated just-in-time for your specific need.

xAI Strikes Back: Grok 4.1 and the Agent Tools API

Not to be outdone, xAI released Grok 4.1 at the start of the week, briefly claimed the #1 spot on LMArena (at 1483 Elo, not 2nd to Gemini 3), and then followed up with Grok 4.1 Fast and a full Agent Tools API.

Grok 4.1: Emotional Intelligence Meets Raw Performance

Grok 4.1 brought some really interesting improvements. Beyond the benchmark numbers (64% win rate over the previous Grok in blind tests), what stood out was the emotional intelligence. On EQ-Bench3, Grok 4.1 Thinking scored 1586 Elo, beating every other model including Gemini, GPT-5, and Claude.

The creative writing scores jumped by roughly 600 Elo points compared to earlier versions. And perhaps most importantly for practical use, hallucination rates dropped from around 12% to 4%—that’s roughly a 3x improvement in reliability on real user queries.

xAI’s approach here was clever: they used “frontier agentic reasoning models as reward models” during RL training, which let them optimize for subjective qualities like humor, empathy, and conversational style without just scaling up model size.

Grok 4.1 Fast: The Agent Platform Play

Then came Grok 4.1 Fast, released just yesterday, and this is where things get really interesting for developers. It’s got a 2 million token context window (compared to Gemini 3’s 1 million) and was specifically trained for agentic, tool-calling workflows.

The benchmark performance is impressive: 93-100% on τ²-Bench Telecom (customer support simulation), ~72% on Berkeley Function Calling v4 (top of the leaderboard), and strong scores across research and browsing tasks. But here’s the kicker: the pricing is aggressive.

At $0.20 per million input tokens and $0.50 per million output tokens, Grok 4.1 Fast is dramatically cheaper than GPT-5 and Claude while matching or exceeding their agentic performance. For the first two weeks, it’s completely free via the xAI API and OpenRouter, which is smart—get developers hooked on your agent platform.

The Agent Tools API gives Grok native access to X search, web browsing, code execution, and document retrieval. This tight integration with X is a genuine advantage—where else can you get real-time access to breaking news, sentiment, and conversation? Yam tested it on the show and confirmed that Grok will search Reddit too, which other models often refuse to do. I’ve used both these models this week in my N8N research agent and I gotta say, 4.1 fast is a MASSIVE improvement!

OpenAI’s Endurance Play: GPT-5.1-Codex-Max and Pro

OpenAI clearly saw Google and xAI making moves and decided they weren’t going to let this week belong to anyone else. They dropped two significant releases: GPT-5.1-Codex-Max and an update to GPT-5.1 Pro.

GPT-5.1-Codex-Max: Coding That Never Stops

This is the headline: GPT-5.1-Codex-Max can work autonomously for over 24 hours. Not 24 minutes, not 24 queries—24 actual hours on a single software engineering task. I talked to someone from OpenAI at the conference who told me internal checkpoints ran for nearly a week on and off.

How is this even possible? The secret is something OpenAI calls “compaction”—a native mechanism trained into the model that lets it prune and compress its working session history while preserving the important context. Think of it like the model taking notes on itself, discarding tool-calling noise and keeping only the critical design decisions and state.

The performance numbers back this up:

* SOTA 77.9% on SWE-Bench Verified (up from 73.7%)

* SOTA 79.9% on SWE-Lancer IC SWE (up from 66.3%)

* 58.1% on TerminalBench 2.0 (up from 52.8%)

And crucially, in medium reasoning mode, it uses 30% fewer thinking tokens while achieving better results. There’s also an “Extra High” reasoning mode for when you truly don’t care about latency and just want maximum capability.

Yam, one of our co-hosts who’s been testing extensively, said you can feel the difference immediately. The model just “gets it” faster, powers through complex problems, and the earlier version’s quirk of ignoring your questions and just starting to code is fixed—now it actually responds and collaborates.

Dominic from OpenAI joined us on the show and confirmed that compaction was trained natively into the model using RL, similar to how Claude trained natively for MCP. This means the model doesn’t waste reasoning tokens on maintaining context—it just knows how to do it efficiently.

GPT-5.1 Pro: Research-Grade Intelligence & ChatGPT joins your group chat1

Then there’s GPT-5.1 Pro, which is less about coding and more about deep, research-level reasoning. This is the model that can run for 10-17 minutes on a single query, thinking through complex problems with the kind of depth that previously required human experts.

OpenAI also quietly rolled out group chats—basically, you can now have multiple people in a ChatGPT conversation together, all talking to the model simultaneously. Useful for planning trips, brainstorming with teams, or working through problems collaboratively. If agent mode works in group chats (we haven’t confirmed yet), that could get really interesting.

Meta drops SAM3 & SAM3D - image and video segmentation models powered by natural language

Phew ok, big lab releases now done, oh.. wait not yet! Because Meta has decided to also make a dent on this Week with SAM3 and SAM3D, which both are crazy. I’ll just add their video release here instead of going on and on!

This Week’s Buzz from Weights & Biases

It’s been a busy week at Weights & Biases as well! We are proud Gold Sponsors of the AI Engineer conference here in NYC. If you’re at the event, please stop by our booth—we’re even giving away a $4,000 robodog!

This week, I want to highlight a fantastic update from Marimo, the reactive Python notebook company we acquired.

Marimo just shipped a native VS Code and Cursor extension. This brings Marimo’s reactive, Git-friendly notebooks directly into your favorite editors.

Crucially, it integrates deeply with uv for lightning-fast package installs and reproducible environments. If you import a package you don’t have, the extension prompts you to install it and records the dependency in the script metadata. This bridges the gap between experimental notebooks and production-ready code, and it’s a huge boost for AI-native development workflows. (Blog , GitHub )

The Future Arrived Early

Phew... if you read all the way until this point, can you leave a ⚡ emoji in the comemnts? I was writing this and it.. is a lot! I was wondering who would even read all the way till here!

This week we felt the acceleration! 🔥 I can barely breathe, I need a nap!

A huge thank you to our guests—Ryan, Swyx, Thor, and Dominik—for navigating the chaos with us live on stage, and to the AI Engineer team for hosting us.

We’ll be back next week to cover whatever the AI world throws at us next. Stay tuned, because at this rate, AGI might be here by Christmas.

TL;DR - show notes and links

Hosts and Co‑hosts

* Alex Volkov – AI Evangelist at Weights & Biases / CoreWeave, host of ThursdAI (X)

* Co‑hosts - Wolfram Ravenwolf – (X), Yam Peleg (X) LDJ (X)

Guests

* Swyx – Founder of AI Engineer World’s Fair and Summit, now at Cognition ( Latent.Space , X)

* Ryan Carson – Amp (X)

* Thor Schaeff – Google DeepMind, Gemini API and AI Studio (X)

* Dominik Kundel – Developer Experience at OpenAI (X)

Open Source LLMs

* Allen Institute Olmo 3 - 7B/32B fully open reasoning suite with end-to-end training transparency (X, Blog)

Big CO LLMs + APIs

* Google Gemini 3 Pro - 1M-token, multimodal, agentic model with Generative UIs (X, X, X)

* Google Antigravity - Agent-first IDE powered by Gemini 3 Pro (Blog, X)

* xAI Grok 4.1 and Grok 4.1 Thinking - big gains in Coding, EQ, creativity, and honesty (X, Blog)

* xAI Grok 4.1 Fast and Agent Tools API - 2M-token context, state-of-the-art tool-calling (X)

* OpenAI GPT-5.1-Codex-Max - long-horizon agentic coding model for 24-hour+ software tasks (X, X)

* OpenAI GPT-5.1 Pro - research-grade reasoning model in ChatGPT Pro

* Microsoft, NVIDIA, and Anthropic partnership - to scale Claude on Azure with massive GPU investments (Announcement, NVIDIA, Microsoft Blog)

This weeks Buzz

* Marimo ships native VS Code & Cursor extension with reactive notebooks and uv-powered environments (X, Blog, GitHub)

Vision & Video & 3D

* Meta SAM 3 & SAM 3D - promptable segmentation, tracking, and single-image 3D reconstruction (X, Blog, GitHub)

AI Art & Diffusion

* Google Nano Banana Pro and SynthID verification - 4K image generation with provenance (Blog)

Show Notes and other Links

* AI Engineer Summit NYC - Live from the conference

* Full livestream available on YouTube

* ThursdAI - Nov 20, 2025

We’re finally so back! Tons of open source releases, OpenAI updates GPT and a few breakthroughs in audio as well, makes this a very dense week!

Today on the show, we covered the newly released GPT 5.1 update, a few open source releases like Terminal Bench and Project AELLA (renamed OASSAS), and Baidu’s Ernie 4.5 VL that shows impressive visual understanding!

Also, chatted with Paul from 11Labs and Dima Duev from the wandb SDK team, who brought us a delicious demo of LEET, our new TUI for wandb!

Tons of news coverage, let’s dive in 👇 (as always links and show notes in the end)

Open Source AI

Let’s jump directly into Open Source as this week has seen some impressive big company models.

Terminal-Bench 2.0 - a harder, highly‑verified coding and terminal benchmark (X, Blog, Leaderboard)

We opened with Terminal‑Bench 2.0 plus its new harness, Harbor, because this is the kind of benchmark we’ve all been asking for.

Terminal‑Bench focuses on agentic coding in a real shell. Version 2.0 is a hard set of 89 terminal tasks, each one painstakingly vetted by humans and LLMs to make sure it’s solvable and realistic. Think “I checked out master and broke my personal site, help untangle the git mess” or “implement GPT‑2 code golf with the fewest characters.”

On the new leaderboard, top agents like Warp’s agentic console and Codex CLI + GPT‑5 sit around fifty percent success. That number is exactly what excites me: we’re nowhere near saturation. When everyone is in the 90‑something range, tiny 0.1 improvements are basically noise. When the best models are at fifty percent, a five‑point jump really means something.

A huge part of our conversation focused on reproducibility. We’ve seen other benchmarks like OSWorld turn out to be unreliable, with different task sets and non‑reproducible results making scores incomparable. Terminal‑Bench addresses this with Harbor, a harness designed to run sandboxed, containerized agent rollouts at scale in a consistent environment. This means results are actually comparable. It’s a ton of work to build an entire evaluation ecosystem like this, and with over a thousand contributors on their Discord, it’s a fantastic example of a healthy, community‑driven effort. This is one to watch!

Baidu’s ERNIE‑4.5‑VL “Thinking”: a 3B visual reasoner that punches way up (X, HF, GitHub)

Next up, Baidu dropped a really interesting model, ERNIE‑4.5‑VL‑28B‑A3B‑Thinking. This is a compact, 3B active‑parameter multimodal reasoning model focused on vision, and it’s much better than you’d expect for its size. Baidu’s own charts show it competing with much larger closed models like Gemini‑2.5‑Pro and GPT‑5‑High on a bunch of visual benchmarks like ChartQA and DocVQA.

During the show, I dropped a fairly complex chart into the demo, and ERNIE‑4.5‑VL gave me a clean textual summary almost instantly—it read the chart more cleanly than I could. The model is built to “think with images,” using dynamic zooming and spatial grounding to analyze fine details. It’s released under an Apache‑2.0 license, making it a serious candidate for edge devices, education, and any product where you need a cheap but powerful visual brain.

Open Source Quick Hits: OSSAS, VibeThinker, and Holo Two

We also covered a few other key open-source releases. Project AELLA was quickly rebranded to OSSAS (Open Source Summaries At Scale), an initiative to make scientific literature machine‑readable. They’ve released 100k paper summaries, two fine-tuned models for the task, and a 3D visualizer. It’s a niche but powerful tool if you’re working with massive amounts of research. (X, HF)

WeiboAI (from the Chinese social media company) released VibeThinker‑1.5B, a tiny 1.5B‑parameter reasoning model that is making bold claims about beating the 671B DeepSeek R1 on math benchmarks.

We discussed the high probability of benchmark contamination, especially on tests like AIME24, but even with that caveat, getting strong chain‑of‑thought math out of a 1.5B model is impressive and useful for resource‑constrained applications. (X, HF, Arxiv)

Finally, we had some breaking news mid‑show: H Company released Holo Two, their next‑gen multimodal agent for controlling desktops, websites, and mobile apps. It’s a fine‑tune of Qwen3‑VL and comes in 4B and 8B Apache‑2.0 licensed versions, pushing the open agent ecosystem forward. (X, Blog, HF)

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Big Companies & APIs

GPT‑5.1: Instant vs Thinking, and a new personality bar

The biggest headline of the week was OpenAI shipping GPT‑5.1, and this was a hot topic of debate on the show. The update introduces two modes: “Instant” for fast, low‑compute answers, and “Thinking” for deeper reasoning on hard problems. OpenAI claims Instant mode uses 57% fewer tokens on easy tasks, while Thinking mode dedicates 71% more compute to difficult ones. This adaptive approach is a smart evolution.

The release also adds a personality dropdown with options like Professional, Friendly, Quirky, and Cynical, aiming for a more “warm” and customizable experience.

Yam and I felt this was a step in the right direction, as GPT‑5 could often feel a bit cold and uncommunicative. However, Wolfram had a more disappointing experience, finding that GPT‑5.1 performed significantly worse on his German grammar and typography tasks compared to GPT‑4 or Claude Sonnet 4.5. It’s a reminder that “upgrades” can be subjective and task‑dependent.

Since the show was recorded, GPT 5.1 is also released in the API and they have published a prompting guide and some evals! With some significant jumps across SWE-bench verified and GPQA Diamond! We’ll be testing this model out all week.

The highlight for this model is the creative writing, it was made public that this model was being tested on OpenRouter as Polaris-alpha and that one tops the eqbench creative writing benchmarks beating Sonnet 4.5 and Gemini!

Grok‑4 Fast: 2M context and a native X superpower

Grok‑4 Fast from xAI apparenly quietly got a substantial upgrade to a 2M‑token context window, but the most interesting part is its unique integration with X. The API version has access to internal tools for semantic search over tweets, retrieving top quote tweets, and understanding embedded images and videos. I’ve started using it as a research agent in my show prep, and it feels like having a research assistant living inside X’s backend—something you simply can’t replicate with public tools.

I still have my gripes about their “stealth upgrade” versioning strategy, which makes rigorous evaluation difficult, but as a practical tool, Grok‑4 Fast is incredibly powerful. It’s also surprisingly fast and cost‑effective, holding its own against other top models on benchmarks while offering a superpower that no one else has.

Google SIMA 2: Embodied Agents in Virtual Worlds

Google’s big contribution this week was SIMA 2, DeepMind’s latest embodied agent for 3D virtual worlds. SIMA lives inside real games like No Man’s Sky and Goat Simulator, seeing the screen and controlling the game via keyboard and mouse, using Gemini as its reasoning brain. Demos showed it following complex, sketch‑based instructions, like finding an object that looks like a drawing of a spaceship and jumping on top of it.

When you combine this with Genie 3—Google’s world model that can generate playable environments from a single image—you see the bigger picture: agents that learn physics, navigation, and common sense by playing in millions of synthetic worlds. We’re not there yet, but the pieces are clearly being assembled. We also touched on the latest Gemini Live voice upgrade, which users are reporting feels much more natural and responsive

More Big Company News: Qwen Deep Research, Code Arena, and Cursor

We also briefly covered Qwen’s new Deep Research feature, which offers an OpenAI‑style research agent inside their ecosystem. LMSYS launched Blog, a fantastic live evaluation platform where models build real web apps agentically, with humans voting on the results. And in the world of funding, the AI‑native code editor Cursor raised a staggering $2.3 billion, a clear sign that AI is becoming the default way developers interact with code.

This Week’s Buzz: W&B LEET – a terminal UI that sparks joy

For this week’s buzz, I brought on Dima Duev from our SDK team at Weights & Biases to show off a side project that has everyone at the company excited: LEET, the Lightweight Experiment Exploration Tool. Imagine you’re training on an air‑gapped HPC cluster, living entirely in your terminal. How do you monitor your runs? With LEET.

You run your training script in W&B offline mode, and in another terminal, you type wandb beta leet. Your terminal instantly turns into a full TUI dashboard with live metric plots, system stats, and run configs. You can zoom into spikes in your loss curve, filter metrics, and see everything updating in real time, all without a browser or internet connection. It’s one of those tools that just sparks joy. It ships with the latest wandb SDK (v0.23.0+), so just upgrade and give it a try!

Voice & Audio: Scribe v2 Realtime and Omnilingual ASR

ElevenLabs Scribe v2 Realtime: ASR built for agents (X, Announcement, Demo)

We’ve talked a lot on this show about ElevenLabs as “the place you go to make your AI talk.” This week, they came for the other half of the conversation. Paul Asjes from ElevenLabs joined us to walk through Scribe v2 Realtime, their new low‑latency speech‑to‑text model. If you’re building a voice agent, you need ears, a brain, and a mouth. ElevenLabs already nailed the mouth, and now they’ve built some seriously good ears.

Scribe v2 Realtime is designed to run at around 150 milliseconds median latency, across more than ninety languages. Watching Paul’s live demo, it felt comfortably real‑time. When he switched from English to Dutch mid‑sentence, the system just followed along without missing a beat. Community benchmarks and our own impressions show it holding its own or beating competitors like Whisper and Deepgram in noisy, accented, and multi‑speaker scenarios. It’s also context‑aware enough to handle code, initialisms, and numbers correctly, which is critical for real‑world agents. This is a production‑ready ASR for anyone building live voice experiences.

Meta’s drops Omnilingual ASR: 1,600+ languages, many for the first time + a bunch of open source models (X, Blog, Announcement, HF)

On the other end of the spectrum, Meta released something that’s less about ultra‑low latency and more about sheer linguistic coverage: Omnilingual ASR. This is a family of models and a dataset designed to support speech recognition for more than 1,600 languages, including about 500 that have never had any ASR support before. That alone is a massive contribution.

Technically, it uses a wav2vec 2.0 backbone scaled up to 7B parameters with both CTC and LLM‑style decoders. The LLM‑like architecture allows for in‑context learning, so communities can add support for new languages with only a handful of examples. They’re also releasing the Omnilingual ASR Corpus with data for 350 underserved languages. The models and code are Apache‑2.0, making this a huge step forward for more inclusive speech tech.

AI Art, Diffusion & 3D

Qwen Image Edit + Multi‑Angle LoRA: moving the camera after the fact (X, HF, Fal)

This one was pure fun. A new set of LoRAs for Qwen Image Edit adds direct camera control to still images. A Hugging Face demo lets you upload a photo and use sliders to rotate the camera up to 90 degrees, tilt from a bird’s‑eye to a worm’s‑eye view, and adjust the lens. We played with it live on the show with a portrait of Wolfram and a photo of my cat, generating different angles and then interpolating them into a short “fly‑around” video. It’s incredibly cool and preserves details surprisingly well, feeling like you have a virtual camera inside a 2D picture.

NVIDIA ChronoEdit‑14B Upscaler LoRA (X, HF)

Finally, NVIDIA released an upscaler LoRA based on their ChronoEdit‑14B model and merged the pipeline into Hugging Face Diffusers. ChronoEdit reframes image editing as a temporal reasoning task, like generating a tiny video. This makes it good for maintaining consistency in edits and upscales. It’s a heavy model, requiring ~34GB of VRAM, and for aggressive upscaling, specialized tools might still be better. But for moderate upscales where temporal coherence matters, it’s a very interesting new tool in the toolbox.

Phew, we made it through this dense week! Looking to next week, I’ll be recoridng the show live from the AI Engieer CODE summit in NY, and we’ll likely see a few good releases from the big G? Maybe? finally?

As always, if this was helpful, please subscribe to ThursdAI and share it with 2 friends, see you next week 🫡

TL;DR and Show Notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @yampeleg, @ldjconfirmed

* Guest: Dima Duev - SDK team Wandb

* Guest: Paul Asjes - Eleven Labs (@paul_asjes)

* Open Source LLMs

* Terminal-Bench 2.0 and Harbor launch (X, Blog, Docs, Announcement)

* Baidu releases ERNIE-4.5-VL-28B-A3B-Thinking (X, HF, GitHub, Blog, Platform)

* Project AELLA (OSSAS): 100K LLM-generated paper summaries (X, HF)

* WeiboAI’s VibeThinker-1.5B (X, HF, Arxiv, Announcement)

* Code Arena — live, agentic coding evaluations (X, Blog, Announcement)

* Big CO LLMs + APIs

* Grok 4 Fast, Grok Imagine and Nano Banana v1/v2 (X, X, X, X)

* OpenAI launches GPT-5.1 (X, X)

* This weeks Buzz

* W&B LEET — an open-source Terminal UI (TUI) to monitor runs (X, Blog)

* Voice & Audio

* ElevenLabs launches Scribe v2 Realtime (X, Blog, Docs)

* Meta releases Omnilingual ASR for 1,600+ languages (X, Blog, Paper, HF Dataset, HF Demo, GitHub)

* Gemini Live conversational upgrade (X)

* AI Art & Diffusion & 3D

* Qwen Image Edit + Multi‑Angle LoRA for camera control (X, HF, Fal)

* NVIDIA releases ChronoEdit-14B Upscaler LoRA (X, HF, Docs)

Quick note, while preparing for this week, I posted on X that I don’t remember such a quiet week in AI since I started doing ThursdAI regularly, but then 45 min before the show started, Kimi dropped a SOTA oss reasoning model, turning a quiet week into an absolute banger.

Besides Kimi, we covered the updated MCP thinking from Anthropic, and had Kenton Varda from cloudflare as a guest to talk about Code Mode, chatted about Windsurf and Cursor latest updates and covered OpenAI’s insane deals.

Also, because it was a quiet week, I figured I’d use the opportunity to create an AI powered automation, and used N8N for that, and shared it on the stream, so if you’re interested in automating with AI with relatively low code, this episode is for you. Let’s dive in

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Kimi K2 Thinking is Here and It’s a 1 Trillion Parameter Beast! (X, HF, Tech Blog)

Let’s start with the news that got everyone’s energy levels skyrocketing right as we went live. Moonshot AI dropped Kimi K2 Thinking, an open-source, 1 trillion-parameter Mixture-of-Experts (MoE) model, and it’s an absolute monster.

This isn’t just a numbers game; Kimi K2 Thinking is designed from the ground up to be a powerful agent. With just around 32 billion active parameters during inference, a massive 256,000 token context window, and an insane tool-calling capacity. They’re claiming it can handle 200-300 sequential tool calls without any human intervention.

The benchmarks are just as wild. On the Humanities Last Exam (HLE), they’re reporting a score of 44.9%, beating out both GPT-5 and Claude 4.5 Thinking. While it doesn’t quite top the charts on SWE-bench verified, it’s holding its own against the biggest closed-source models out there. Seeing an open-source model compete at this level is incredibly exciting.

During the show, we saw some truly mind-blowing demos, from a beautiful interactive visualization of gradient descent to a simulation of a virus attacking cells, all generated by the model. The model’s reasoning traces, which are exposed through the API, also seem qualitatively different from other models, showing a deep and thoughtful process. My co-hosts and I were blown away. The weights and a very detailed technical report are available on Hugging Face, so you can dive in and see for yourself. Shout out to the entire Moonshot AI team for this incredible release!

Other open source updates from this week

* HuggingFace released an open source “Smol Training Playbook” on training LLMs, it’s a 200+ interactive beast with visualizations, deep dives into pretraining, dataset, postraining and more! (HF)

* Ai2 launches OlmoEarth — foundation models + open, end-to-end platform for fast, high-resolution Earth intelligence (X, Blog)

* LongCat-Flash-Omni — open-source omni-modal system with millisecond E2E spoken interaction, 128K context and a 560B ScMoE backbone (X, HF, Announcement)

Big Tech’s Big Moves: Apple, Amazon, and OpenAI

The big companies were making waves this week, starting with a blockbuster deal that might finally make Siri smart. Apple is reportedly will be paying Google around $1 billion per year to license a custom 1.2 trillion-parameter version of Gemini to power a revamped Siri.

This is a massive move. The Gemini model will run on Apple’s Private Cloud Compute, keeping user data walled off from Google, and will handle Siri’s complex summarizer and planner functions. After years of waiting for Apple to make a significant move in GenAI, it seems they’re outsourcing the heavy lifting for now while they work to catch up with their own in-house models. As a user, I don’t really care who builds the model, as long as Siri stops being dumb!

In more dramatic news, Perplexity revealed that Amazon sent them a legal threat to block their Comet AI assistant from shopping on Amazon.com. This infuriated me. My browser is my browser, and I should be able to use whatever tools I want to interact with the web. Perplexity took a strong stand with their blog post, “Bullying is Not Innovation,” arguing that user agents are distinct from scrapers and act on behalf of the user with their own credentials. An AI assistant is just that—an assistant. It shouldn’t matter if I ask my wife or my AI to buy something for me on Amazon. This feels like a move by Amazon to protect its ad revenue at the expense of user choice and innovation, and I have to give major props to Perplexity for being so transparent and fighting back.

Finally, OpenAI continues its quest for infinite compute, announcing a multi-year strategic partnership with AWS. This comes on top of massive deals with NVIDIA, Microsoft, Oracle, and others, bringing their total commitment to compute into the trillions of dollars. It’s getting to a point where OpenAI seems “too big to fail,” as any hiccup could have serious repercussions for the entire tech economy, which is now heavily propped up by AI investment. Sam has clarified that they don’t think OpenAI wants to be too big to fail in a recent post on X, and that the recent miscommunications around the US government backstopping OpenAI’s infrastructure bailouts were taken out of context. 🤔

Coding with AI: The Evolution of MCP and New Dev Tools

This week, we kicked off a new segment on the show: Coding with AI! Essentially realizing that we talk about AI coding a LOT, and decided to add a dedicated corner to it! And we started with a fascinating development in the world of agentic tooling. Anthropic published a blog post arguing that the standard way of using the Model Context Protocol (MCP) — by loading full tool definitions into the context window — is inefficient.

Their solution? Have LLMs write code to interact with tools instead. This approach can slash token usage by over 98% in some cases. This idea sounded familiar, and that’s because Cloudflare had already explored it with a feature called “Code Mode.” We were lucky enough to have Kenton Varda, one of the authors of the Code Mode post and head of engineering for Cloudflare Workers, join us to discuss this shift.

Kenton explained that LLMs are trained on vast amounts of code, making it a more “native language” for them than the artificial construct of tool calls. By generating code, agents can chain multiple tool calls together, process intermediate results, and operate much more efficiently without sending everything back through the neural network. While MCP still provides crucial standardization for discovering and authorizing tools, this “code execution” pattern seems to be the way forward for building more powerful and scalable agents.

Windsurfs CodeMaps and Cursor multi agent executions

In other coding with AI news, Windsurf has pushed an incredible feature, called CodeMaps. They will use their SWE-1 model to (quickly) generate Codemaps that will expalins a code-base to you, in a visual way. What starts where and goes where. It’s really useful to understand a new codebase or re-understand one you forgot about already! You can even chat with codemaps, to see if your overall system’s design is solid! Great addition that I’m sure will help many folks adopt Windsurf!

And Cursor, another popular AI-native IDE, released a super-performant in-IDE browser and a wild multi-agent feature that queries multiple LLMs in parallel and then synthesizes their answers.

This Week’s Tutorial

I finally got around to building some serious automations for ThursdAI, and folks, N8N has been a game-changer. What used to take me 30+ minutes of manual work now happens automatically in the background.

Here’s what I built: A Telegram bot that takes Twitter/X links, fetches the tweets and all linked content, uses AI agents to extract and summarize the information, and then posts it to our announcement channel and my notes app. The coolest part? I built this whole thing in about 4 hours with the help of Atlas browser and GPT-5 literally telling me what to do at each step.

During the show, we even live-tested swapping out GPT-4o-mini for Kimi K2 - took literally 30 seconds to connect via OpenRouter. I went through my node and explains how this all works on the show, so if you’ve wanted to learn about n8n, check it out starting around 01:13:00. If you want to see how my automation turned out, it will be posting all my links to the new telegram channel t.me/thursdai_news (expect it to be messy at first as I’m testing out the automation)

Robotics - Xpeng’s “Iron” humanoid: big vibes, few specs

Another week, another humanoid robot that is supposedly “coming” in 2026!

A humanoid from Xpeng went viral this week, marketed as “the most human‑like” robot with soft skin, bionic muscles, customizable sexes (yes, really, they have a woman humanoid), something called a VLT brain, and a 2026 production goal. Here’s what we didn’t get: a spec sheet. No DOF, speed, payload, compute TOPS, battery capacity, runtime, or safety pathway. No pricing, manufacturing strategy, or clear target markets. In other words: lots of sizzle, no steak.

Apparently, there was folks thinking Xpend pulled an Elon and put a human in a robot suit, making the CEO do the “we’ll cut a part of the soft skin to expose the robot underneath so you don’t think we’re lying” stunt. Which I agree, was very effective.

But, If Xpeng is serious, the next thing we’ll see should be a crisp engineering document: joints, actuation, sensors, compute, and a locomotion/manipulation demo with independent measurements. Until then, treat this as a branding salvo and a reminder that the humanoid category is still sorting itself into “industrial payload first” versus “human likeness first” approaches.

Voice & Audio

Maya‑1: open‑source voice design from natural language

We highlighted Maya‑1, a 3B Llama‑backboned TTS system designed to generate voices from natural language descriptions. Instead of picking from a menu, you describe the voice—age, accent, affect—and Maya conjures it. It supports real‑time streaming and over twenty “emotion tags.” The quality is compelling for its size and the Apache 2 license will make a lot of builders happy. There’s a growing middle class of TTS: tiny but expressive, good enough for in‑app narrators, prototyping, and even stylized content when you don’t want the constraints of commercial voice marketplaces.

Inworld TTS: a new leader on independent rankings

We also listened to Inworld’s latest, which currently tops the Artificial Analysis TTS leaderboard. It’s not open source, but the combo of expressivity, speed (sub‑250 ms), and multilingual support puts it firmly in the “commercially viable at scale” tier alongside the usual suspects. If you need SaaS TTS today and care about emotional range, add this to your shortlist. Pricing on their site targets availability rather than hobbyist tinkering, but the quality argues for itself.

Whew! For a week that started slow, it certainly ended with a bang. It just goes to show you can never count AI out. We’re seeing open source continue to push the boundaries, big tech making landscape-defining moves, and agentic AI becoming more powerful and accessible every day.

As always, thanks for tuning in. If you’re going to be at the AI.engineer conference in New York, please hit me up—I’d love to meet you.

TL;DR and Show Notes + Links

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten

* Kenton Varda @ Cloudflare (@KentonVarda)

* Open Source LLMs

* Smol Training Playbook — a 200+ page, end-to-end guide to reliably pretrain and operate LLMs (X, Announcement)

* Ai2 launches OlmoEarth — foundation models + open, end-to-end platform for fast, high-resolution Earth intelligence (X, Blog)

* Moonshot AI releases Kimi K2 Thinking — an open-source 1T-parameter MoE agent with 256K context and huge tool-calling capacity (X, HF, Blog, Arxiv)

* LongCat flash Omni - 560B (27A) omni model (text, audio, video input)

* Big CO LLMs + APIs

* Apple will pay roughly $1B/year to license a custom 1.2 trillion‑parameter Google Gemini model to power a revamped Siri (X, Announcement)

* Perplexity says Amazon issued a legal threat to block Comet AI assistants from shopping on Amazon (X, Blog)

* AWS announces multi-year strategic infrastructure partnership with OpenAI to power ChatGPT inference, training, and agentic AI (X)

* Robotics

* Xpeng unveils ‘Iron’ humanoid claiming ‘most human-like’ design with soft skin, bionic muscles, VLT brain and a 2026 production plan (X)

* Coding with AI

* Anthropic shows how running MCP-connected tools as code slashes token use and scales agents (X, Blog)

* Windsurf Codemaps — AI‑annotated, navigable maps of your codebase powered by SWE-1.5 (Fast) and Sonnet 4.5 (Smart) (X, Announcement)

* Conversation with Kenton Varda (@KentonVarda) from Cloudflare about MCP and Code Mode

* Cursor added in IDE browser - very performant!

* Audio & Video

* Maya-1 - Open source voice generation model.

* Inworld TTS - new #1 on artifical analysis benchmark.

* Tools & Gadgets

* Sandbar launches Stream — a voice-first personal assistant — and Stream Ring, a wearable ‘mouse for voice’, available for preorder (X, Blog)

I’m excited to bring you this weeks (spooky) AI updates! We started the show today with MiniMax M2, the currently top Open Source LLM, with an interview with their head of eng, Skyler Miao, continued to dive into OpenAIs completed restructuring into a non-profit and a PBC, including a deep dive into a live stream Sam Altman had, with a ton of spicy details, and finally chatted with Arjun Desai from Cartesia, following a release of Sonic 3, a sub 49ms voice model!

So, 2 interviews + tons of news, let’s dive in! (as always, show notes in the end)

Hey, if you like this content, it would mean a lot if you subscribe as a paid subscriber.

Open Source AI

MiniMax M2: open-source agentic model at 8% of Claude’s price, 2× speed (X, Hugging Face )

We kicked off our open-source segment with a banger of an announcement and a special guest. The new king of open-source LLMs is here, and it’s called MiniMax M2. We were lucky enough to have Skyler Miao, Head of Engineering at Minimax, join us live to break it all down.

M2 is an agentic model built for code and complex workflows, and its performance is just staggering. It’s already ranked in the top 5 globally on the Artificial Analysis benchmark, right behind giants like OpenAI and Anthropic. But here’s the crazy part: it delivers nearly twice the speed of Claude 3.5 Sonnet at just 8% of the price. This is basically Sonnet-level performance, at home, in open source.

Skylar explained that their team saw an “impossible triangle” in the market between performance, cost, and speed—you could only ever get two. Their goal with M2 was to build a model that could solve this, and they absolutely nailed it. It’s a 200B parameter Mixture-of-Experts (MoE) model, but with only 10B active parameters per inference, making it incredibly efficient.

One key insight Skylar shared was about getting the best performance. M2 supports multiple APIs, but to really unlock its reasoning power, you need to use an API that passes the model’s “thinking” tokens back to it on the next turn, like the Anthropic API. Many open-source tools don’t support this yet, so it’s something to watch out for.

Huge congrats to the MiniMax team on this Open Weights (MIT licensed) release, you can find the model on HF!

MiniMax had quite a week, with 3 additional releases, MiniMax speech 2.6, an update to their video model Hailuo 2.3 and just after the show, they released a music 2.0 model as well! Congrats on the shipping folks!

OpenAI drops gpt-oss-safeguard - first open-weight safety reasoning models for classification ( X, HF )

OpenAI is back on the open weights bandwagon, with a finetune release of their previously open weighted gpt-oss models, with gpt-oss-safeguard.

These models were trained exclusively to help companies build safeguarding policies to make sure their apps remains safe! With gpt-oss-safeguards 20B and 120B, OpenAI is achieving near parity with their internal safety models, and as Nisten said on the show, if anyone knows about censorship and safety, it’s OpenAI!

The highlight of this release is, unlike traditional pre-trained classifiers, these models allow for updates to policy via natural language!

These models will be great for businesses that want to safeguard their products in production, and I will advocate to bring these models to W&B Inference soon!

A Humanoid Robot in Your Home by 2026? 1X NEO announcement ( X, Order page, Keynote )

Things got really spooky when we started talking about robotics. The company 1X, which has been on our radar for a while, officially launched pre-orders for NEO, the world’s first consumer humanoid robot designed for your home. And yes, you can order one right now for $20,000, with deliveries expected in early 2026.

The internet went crazy over this announcement, with folks posting receipts of getting one, other folks stoking the uncanny valley fears that Sci-fi has built into many people over the years, of the Robot uprising and talking about the privacy concerns of having a human tele-operate this Robot in your house to do chores.

It can handle chores like cleaning and laundry, and for more complex tasks that it hasn’t learned yet, it uses a teleoperation system where a human “1X Expert” can pilot the robot remotely to perform the task. This is how it collects the data to learn to do these tasks autonomously in your specific home environment.

The whole release is very interesting, from the “soft and quiet” approach 1X is taking, making their robot a 66lbs short king, draped in a knit sweater, to the $20K price point (effectively at loss given how much just the hands cost), the teleoperated by humans addition, to make sure the Robot learns about your unique house layout.

The conversation on the show was fascinating. We talked about all the potential use cases, from having it water your plants and look after your pets while you’re on vacation to providing remote assistance for elderly relatives. Of course, there are real privacy concerns with having a telepresence device in your home, but 1X says these sessions are scheduled by you and have strict no-go zones.

Here’s my prediction: by next Halloween, we’ll see videos of these NEO robots dressed up in costumes, helping out at parties. The future is officially here. Will you be getting one? If not this one, when will you think you’ll get one?

OpenAI’s Grand Plan: From Recapitalization to ASI

This was by far the biggest update about the world of AI for me this week! Sam Altman was joined by Jakub Pachocki, chief scientist and Wojciech Zaremba, a co-founder, on a live stream to share an update about their corporate structure, plans for the future, and ASI goals (Artificial Superintelligence)

First, the company now has a new structure: a non-profit OpenAI Foundation governs the for-profit OpenAI Group. The foundation starts with about 26% equity and has a mission to use AI for public good, including an initial $25 billion commitment to curing diseases and building an “AI Resilience” ecosystem.

But the real bombshells were about their research timeline. Chief Scientist Jakub Pachocki stated that they believe deep learning systems are less than a decade away from superintelligence (ASI). He said that at this point, AGI isn’t even the right goal anymore. To get there, they’re planning to have an “AI research intern” by September 2026 and a fully autonomous AI researcher comparable to their human experts by March 2028. This is insane if you think about it. As Yam mentioned, OpenAI is already shipping at an insane speed, releasing Models and Products, Sora, Atlas, Pulse, ChatGPT app store, and this is with humans, assisted by AI.

And here, they are talking about complete and fully autonomous researchers, that will be infinitely more scalable than humans, in the next 2 years. The outcomes of this are hard to imagine and are honestly mindblowing.

To power all this innovation, Sam revealed they have over $1.4 trillion in obligations for compute (over 30 GW). And said even that’s not enough. Their aspiration is to build a “compute factory” capable of standing up one gigawatt of new compute per week, and he hinted they may need to “rethink their robotics strategy” to build the data centers fast enough. Does this mean OpenAI humanoid robots building factories? 🤔

Plus, don’t forget, Sam is one of the investors in Helion energy, working on power solutions like Fusion, and the above graphic has an Energy block that Sam said they will give an update on later (that’s also what he told me during Dev Day when I asked him about it).

Super exciting and honestly mind-blowing stuff, Gigawats per week, fully autonomous researchers, the world is going to look way different in a few years!

The Agent Labs Race: Cursor 2.0 vs. Cognition’s SWE-1.5 (X, Blog)

This week also saw a major showdown in the agentic coding space. On the very same day, both Cursor and Cognition launched major updates and their own new models, signaling a new era where agent labs are training their own specialized AI.

First up, Cursor 2.0 was released with a completely redesigned multi-agent interface and their new model, Composer. Composer is claimed to be four times faster than comparable models, and the new UI is built around managing a fleet of agents that can work in parallel on your codebase. It’s a clear shift from being just an IDE to a full-fledged agent platform. Look, the UI even looks like ChatGPT and no code in sight (until you switch to IDE mode)

Their Composer model is also very interesting, and got a lot of folks excited, but the evaluations they shared, and the fact that they didn’t disclose if that’s a finetune of a chinese model (it likely is). Regardless, folks are saying that it’s a very good model that’s also VERY fast!

Cognition own coding model - SWE 1.5 ( Blog, X, Windsurf )

Then, just hours later, Cognition punched right back with SWE-1.5, their new frontier agent model that now powers Windsurf. The headline here is pure speed. Powered by Cerebras, SWE-1.5 hits a blistering 950 tokens per second—13 times faster than Sonnet 4.5—while achieving near-SOTA performance on SWE-Bench Pro. They’ve achieved this through a co-designed stack where the agent harness, inference system, and model were all built together and optimized with end-to-end reinforcement learning in real coding environments.

This competition is fantastic news for all of us. We’re seeing specialized, highly-performant models being developed outside of the big labs, putting more power back in the hands of developers.

This Week’s Buzz

Just a few quick updates from the world of Weights & Biases and our parent company, CoreWeave.

First, big news! CoreWeave announced the acquisition of Marimo, the company behind the popular open-source, reactive notebook for Python. This is another exciting step in building out the essential cloud for AI, adding powerful development tools to the stack alongside best-in-class GPU infrastructure and MLOps with Weights & Biases. Welcome to the Marimo team!

Also, Fully Connected is coming to London next week! It’s our premier conference, and we’ll have speakers from Mistral, Google, LlamaIndex, and more. If you’re in Europe, please come join us. DM me if you need tickets!

And if you’re in New York from November 19-22, come say hi at the AI Engineer Code Summit. We’re sponsoring and will have a big booth. It’s always a great place to meet folks from this community.

Video & Voice: The Multimodal Explosion

The world of video and voice AI was on fire this week.

The absolute highlight was Odyssey ML V2, a new real-time interactive AI video platform. This thing is not like other video models that take minutes to generate a clip. With Odyssey, you type a prompt, and a video starts streaming instantly. Then, you can edit it live. We did a demo on the show where we prompted “army of robots in a starship corridor” and then typed “turn these robots into fluffy covered cat robots,” and the video changed in real time. It’s mind-blowing. This is a glimpse into the future of user-driven, playable media.

On the more traditional video front, Sora is now invite-free in the US and Japan, and they launched Character Cameos. You can now upload photos of your pets or objects (like your kid’s carved pumpkin!) and turn them into characters that you and others can use in videos. I, of course, immediately made a cameo of my cat, Sonia.

Voice and Audio - Cartesia launches Sonic 3, sub 50ms AI speech model

In the world of voice, we had Arjun Desai from Cartesia join us to talk about Sonic-3, their new real-time TTS engine. Backed by a new $100M funding round, Sonic-3 is built on State Space Models (not Transformers) and can achieve insane speeds—we’re talking under 50ms latency. But it’s not just fast; it’s also incredibly expressive. It can laugh, emote, and speak 42 languages with natural code-switching. I used their Pro Voice cloning feature to create an AI version of myself, and the results were scarily good. We even had my AI clone host a segment of the show, see it yourself here, powered by Argil and Sonic 3, this is... AI Alex

Wrapping Up This Spooky Week 🎃

As I sit here in my Halloween costume reflecting on this week, I can’t help but feel we’re at an inflection point. We have:

* Open source models competing with the best proprietary ones

* Humanoid robots becoming consumer products

* ASI timelines measured in single-digit years

* Real-time interactive AI across all modalities

And yet, nothing about this scares me. If anything, I’m more excited than ever about what we’re building together. Yes, the pace is insane. Yes, keeping up with everything is becoming nearly impossible (and it’s literally my job!). But we’re living through the most transformative period in human history, and we get to be part of it.

To everyone building, experimenting, and pushing boundaries - keep going. To everyone worried about what’s coming - join us in shaping it responsibly. And to everyone who celebrated Halloween today - I hope your costume was as epic as the AI developments we covered! 👻

Until next week, this is Alex signing off. Remember to subscribe, give us five stars, and I’ll see you next ThursdAI!

TL;DR - All Topics Covered

ThursdAI - Oct 30 - Halloween Special 👻

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson

* Guest: Skyler Miao - Head of Engineering, MiniMax (@SkylerMiao7)

* Guest: Arjun Desai - CoFounder Cartesia (@jundesai)

* Open Source LLMs

* MiniMax M2: Open-source agentic model at 8% of Claude’s price, 2× speed (X, Hugging Face)

* OpenAI GPT-OSS-Safeguard: First open-weight safety reasoning models (X, HF)

* IBM Granite 4.0 Nano: Ultra-efficient tiny models for edge deployment (X, Artificial Analysis)

* Ming-flash-omni Preview: Sparse MoE omni-modal model (X, HuggingFace)

* Kimi Linear: 48B parameter model with 1M context (HF)

* Robotics

* 1X NEO: First consumer humanoid robot, $20k, delivery 2026 (X, Order page, Keynote)

* Big Companies & APIs

* OpenAI Restructuring: ASI within 10 years, AI researcher by 2028 (X)

* Cursor 2.0 & Composer: 4x faster coding, new model (X, Blog)

* Cognition SWE-1.5: 950 tok/s, 40% SWE-bench Pro (Blog, X, Windsurf)

* Perplexity Email Assistant: Privacy-first AI inbox management (X, Assistant Site)

* This Week’s Buzz

* Fully Connected London - fullyconnected.com

* AI Engineer Code Summit NYC - Nov 19-22

* CoreWeave acquires Marmo notebooks (X)

* Vision & Video

* Odyssey ML V2: Real-time interactive AI video (X, Experience)

* Sora: Now invite-free + Character Cameos feature (X, Sonia Cameo)

* Hailuo 2.3: Cinema-grade video generation (X)

* Voice & Audio

* MiniMax Speech 2.6: <250ms ultra-human voice AI (X, MiniMax, API Docs)

* Cartesia Sonic 3: Real-time TTS with emotion & laughter, $100M funding (X, Website, Docs)

* Tools

* Pokee: Agentic workflow builder (X)

* Pomelli: Google’s AI marketing agent (X, Labs)

Welcome... to the browser war II - the AI edition! This week we chatted in depth about ChatGPT’s new Atlas agentic browser, and the additional agentic powers Microsoft added to Edge with Copilot Mode (tho it didn’t work for me)

Also this week was a kind of crazy OCR week, with more than 4 OCR models releasing, and the crown one is DeepSeek OCR, that turned the whole industry on it’s head (more later)

Quite a few video updates as well, with real time lipsync from Decart, and a new update from LTX with 4k native video generation, it’s been a busy AI week for sure!

Additionally, I’ve had the pleasure to talk about AI Browsing agents with Paul from BrowserBase and real time video with Kwindla Kramer from Pipecat/Daily, so make sure to tune in for those interviews, buckle up, let’s dive in!

Thanks for reading ThursdAI - Recaps of the most high signal AI weekly spaces! This post is public so feel free to share it.

Open Source: OCR is Not What You Think It Is (X, HF, Paper)

The most important and frankly mind-bending release this week came from DeepSeek. They dropped DeepSeek-OCR, and let me tell you, this is NOT just another OCR model. The cohost were buzzing about this, and once I dug in, I understood why. This isn’t just about reading text from an image; it’s a revolutionary approach to context compression.

We think that DeepSeek needed this as an internal tool, so we’re really grateful to them for open sourcing this, as they did something crazy here. They are essentially turning text into a visual representation, compressing it, and then using a tiny vision decoder to read it back with incredible accuracy. We’re talking about a compression ratio of up to 10x with 97% decoding accuracy. Even at 20x compression they are achieving 60% decoding accuracy! My head exploded live on the show when I read that. This is like the middle-out compression algorithm joke from Silicon Valley, but it’s real. As Yam pointed out, this suggests our current methods of text tokenization are far from optimal.

With only 3B and ~570M active parameters, they are taking a direct stab at long context inefficiency, imagine taking 1M tokens, encoding them into 100K visual tokens, and then feeding those into a model. Since the model is tiny, it’s very cheap to run, for example, alphaXiv claimed they have OCRd’ all of the papers on ArXiv with this model for $1000, a task that would have cost $7500 using MistalOCR - as per their paper, with DeepSeek OCR, on a single H100 GPU, its possible to scan up to 200K pages! 🤯 Really innovative stuff!

OCR and VLM models had quite a week, with multiple models besides DeepSeek OCR releasing, models like Liquids LFM2-VL-3B (X, HF), and the newly updated 2B and 32B of Qwen3-VL (X, Hugging Face), and AI2’s olmo-ocr 2-7B (X, HF).

The Qwen models are particularly interesting, as the 2B model is a generic VLM (can also do OCR) and is close to previous weeks 4B and 8B brothers, and the newly updated 32B model outperforms GPT-5 mini and Claud 4 sonnet even!

The Browser Wars are BACK: OpenAI & Microsoft Go Agentic

Look, I may be aging myself here, but I remember, as a young frontend dev, having to install 5 browers at once to test them out, Chrome, Internet Explorer, Firefox, Opera etc’. That was then, and now, I have Dia, Comet, and the newly released Atlas, and, yeah, today I even installed Microsoft Edge to test their AI features! It seems like the AI boom brought with it a newly possible reason for folks to try and take a bite out of Chrome (who’s agentic features are long rumored with project mariner but are nowhere to be found/shipped yet)

OpenAI’s ChatGPT Atlas: The Browser Reimagined (X, Download)

OpenAI is proving that besides just models, they are a product powerhouse, stepping into categories like Shopping (with a shopify integration), app stores (with ChatGPT apps), social (with Sora2) and now... browsers! This week, they have launched their tightly integrated into ChatGPT browser called Atlas, and it’s a big release!

I’ll split my review here to 2 parts, the browser features part and the agentic part.

New fresh take on a chromium based browser

The tight integration into ChatGPT is everywhere in this browser, from the new tab that looks like the basic ChatGPT interaface, one line of text, to the sidebar on the left that... is the ChatGPT web sidebar with all your chats, projects, custom GPTs etc.

The integration doesn’t stop there, as you have to sign in to your ChatGPT account to even use this browser (available only to MacOS users, and Pro, Plus and Nano tiers). The browser has a few neat tricks, like a special tool that allows you to search your browsing history with natural language, a-la “what were those shoes I was looking at a few days ago” will find your the tabs you browsed for shoes.

A special and cool feature is called, confusingly “Cursor”, wherein you can select a text, and then click the little OpenAI logo that pops up, allowing you to ask ChatGPT for changes to that selected text (like fix typos, spruce up your writing etc). It’s surprisingly convenient to rewrite tweets or for any type of document editing.

ChatGPT Atlas also stores memories about your browsing patterns, which will be additional to the ChatGPT memories it stores about you from chats, helping even more by knowing your browsing patterns, which software you prefer to use, which websites you prefer to order food from etc. This IMO is one of the hugest unlocks for folks inside the ChatGPT ecosystem, as much of a stanard persons peferences can be gleaned from their browser usage and patterns.

Lastly, the “Ask ChatGPT” sidepane on the right (which can be opened with cmd+.) is really great for chatting with a webpage, or going down search rabbit holes. It receives the context of the webpage you’re looking at by default (only 1 page so far, competitors allow you to add additional tabs with @, (which is supposedly coming to ChatGPT soon) and ask... ChatGPT anything about this.

Agentic SOTA? not so fast

The most important “change” to how browsers work in Atlas imo is the agentic mode. This isn’t new, we remember when ChatGPT launched thier Operator Agent back in January of this year (our coverage) and then renamed it Agent Mode and integrated into ChatGPT itself back in July.

So, web browsing agents are not entirely new, what’s novel here though, is the integration into your browser, and the ability for the Atlas browser to use your logged in sessions and cookies, to pretend to be you! This... can be quite scary for some, as prompt injection attacks are getting more popular (where-in malicious a******s add hidden instructions to their website that will get the agent to do something you don’t like) but it’s also very exciting, as the agent can do much much more, without getting blocked by providers who could previously just block Agent Mode as it ran on OpenAI servers!

Until today, there were 2 main Agentic browsers in the mix, Perplexity’s Comet (where you can choose which model runs the agent) and Atlas. Comet seems to be doing a little bit better on some stuff on my tests, but not by much. I have the same agentic task (go to X.com, find my bookmarks, open all links, summarize per my specific format) that I’ve been running for a while now, and Comet outdid Atlas this week on that task.

Who needs agentic browsing?

For some reason, most of the demos for agentic browsing are showing the same, boring-ish examples. Book some flights, collect a grocery shopping cart. I’ve tried new and different things this week, for example, letting Atlas choose and order food for me (as ChatGPT knows my pescatarian preferences, it’s better than Comet for personal stuff), and one of the longest task I’ve had an agent do yet, I asked it to complete a Compliance training I had to take at work!

Mind you, this is a very complex task, even for regular people, as these compliance websites are built to not be messed with. They have video players that stop if you switch focus to some other tab, they have interactive quizes and games, drag and drop interfaces, audio buttons, to make sure you really are taking the test. I can happily report, that after 5 hours, and a few stops along the way (where I had to convince the agent to keep going), it completed this very hard task! (and now I have to take this course myself again to actualy be compliant 😅 it will probably take me 2 hours to do manually)

This experiment made me think, who needs the agentic browsing features and for what? Well, for tasks that require a lot of manual steps to do the same thing over and over again, agentic browser is going to make a lot of peoples browsing a lot easier. Things like kids schedules reviewing in multiple websites, collecitng data and formatting it differently etc.

Scary security implications

Atlas could only finish my compliance task while being logged in as me, and ChatGPT Atlas gives a all or nothing control. You can run your agent with full access to your logged in websites (think Gmail etc) or you can essentially give it an incognito mode.

This, again, due to the risk of promp injections in malicious websites being more and more prevalent. In a rare post detailing how they are thinking about this, OpenAI Chief Information Security officer offered a deep dive into their attempts to mitigate this issue (Simon Willison had a great breakdown of that information here) but that’s likely not enough, so definitely be aware when you’re running agent mode (which needs to be explicitly turned on right now by selecting Agent)

This Weeks Buzz - Weights & Biases // Coreweave

Weights & Biases (now proudly part of CoreWeave) had some exciting updates. Our Fully Connected conference series is hitting Tokyo on October 30-31 and London on November 4-5—perfect for ML practitioners and AI engineers. If you’re in the area, join us for talks, networking, and deep dives into the latest. Register at Fullyconnected.com—DM me if you need a hook-up!

We also collaborated with Meta and Stanford on Torch Forge, a new PyTorch-native library for scalable RL post-training and agent development. It’s built for massive GPU runs (we provided 520 H100s!), competing with Ray via tools like Monarch scheduler. If you’re training on clusters, check the blog —it’s a big deal for efficient multi-GPU workflows.

Microsoft goes after OpenAI with Edge copilot mode (X)

In a pretty surprising move, Microsoft announced today their take on the agentic browser war, with a bunch of enhancements to Copilot (their overall word for their AI assistance across Microsoft 360, Browser, Bing search etc), Think.. clippy, for the AI age (they even brought clippy back as an easter egg)

The short version is, Edge is getting more powerful with custom agentic features (which I enabled and couldn’t get to work no matter how much I tried, so I can’t tell you how they compare to Atlas/Comet), and they have a voice mode that allows you to talk to your browser, with Edge having a sense of what’s on the actual page! Of course, this being Microsoft, marketing aside and features aside, when I asked Copilot if it has access to other tabs (like the marketing video claims) it said it doesn’t have access, agentic mode didn’t work, and I’m very unlikely to be testing it further! But hey, if you use Copilot app on your mobile phone, and click the new Mico avatar like 25 times it will turn into Clippy, so.. yay?

Claude Code on the Web, Claude on Desktop upgraded (X, Anthropic)

Anthropic also made waves by bringing Claude Code to the web. Now you can delegate software tasks to Claude through a web interface with GitHub integration. Nisten was particularly excited about being able to manage his coding projects from his phone. It runs tasks in a secure sandbox, can handle multiple repos, and automatically create pull requests. It’s another powerful coding agent becoming more accessible to developers everywhere.

They have also made changes to the desktop Claude app, allowing it to see the context of your screen with screenshots, and file sharing, and even a new voice mode that allows you to talk to Claude (which is unfortunately mapped to the tab button, without the ability to remap)

Browser Automation and Delegated Authentication with Browserbase (X, Director.ai, Stagehand)

While OpenAI and Microsoft are building chat into the browser, what about bringing the browser into our chat-based agents? We had Paul Klein, the founder of Browserbase, join us to talk about this exact topic. His company is tackling one of the biggest hurdles for AI agents: authentication.

Paul and his team launched Director 2.0, a platform that lets you build web automation with natural language prompts. But the real innovation here is their integration with 1Password. Instead of giving an agent the “master keys” to all your logged-in sessions like Atlas does, Browserbase allows for delegated, per-site authentication. When an agent running in the cloud needs to log into a site on your behalf, you get a prompt on your local machine to approve it. This is a much safer, more granular way to give agents the access they need. As Paul said, you shouldn’t let an AI the master keys into your house; you should give it permission to enter one room at a time. It’s a brilliant paradigm for secure agentic workflows and I really like this approach of a piece-meal authentication for browser agents. I wish Atlas has something like this for the incognito mode!

Director 2.0 itself is like V0 for web automation—you give it a prompt, it performs the task, and then it gives you a repeatable script you can deploy. It’s a way to create robust automations without needing to be a developer, and it’s already being used to automate thousands of hours of manual work.

Video & Audio: The Race to Real-Time

The world of generative media is moving at lightning speed, with a clear trajectory towards real-time, interactive experiences.

Decart’s Real-Time Lip Sync API (X)

We had Kwindla Kramer, one of the worlds leading experts in real-time audio, join us to break down a phenomenal release from Decart AI: a real-time lip-sync API. This isn’t the pre-rendered, slightly-off lip-sync we’re used to. This is a pipeline of models working together to generate perfectly synchronized lip movements for an avatar in real-time.

Kwindla explained the tech stack: it captures your audio via WebRTC, sends it to Whisper for transcription, gets a response from an LLM like Grok, generates a voice with ElevenLabs, and then Decart’s model modifies the avatar’s video frames to match the new audio, all with a sub-two-second latency. This is how we get to truly interactive, believable AI characters. Kwindla even built a quick demo, though it didn’t seem to work the in the morning, probably GPU issues, so we just played the demo videos.

LTX-2 and Sora’s Pet Cameos

The trend towards high-fidelity, real-time generation continued with a breaking news release from Lightricks: LTX-2. This is an open-source (weights coming this fall!) engine that can generate native 4K video with synchronized audio. It’s fast, efficient, and is set to be a powerful open alternative to closed models like Sora. And it’s a native 4K, no upscaling!

Speaking of Sora, they announced that character cameos are getting an upgrade. Soon, you’ll be able to turn anything—your pet, a coffee cup, or even a sunny-side-up egg—into an animated, talking character. I’m really looking forward for this new Sora update and will let you know my impressions when it drops (soon, according to Bill from OpenAI)

What a week folks! What A WEEK! 😅 My head is still spinning!

From browsers that can do our work for us to OCR that redefines context, we’re seeing foundational shifts across the board. The tools are getting more powerful, more accessible, and more integrated into our daily workflows. The future is being built right now, and we get to watch it happen week by week.

Thank you for being a ThursdAI subscriber. As always, here are the show notes with all the links and details from this week’s whirlwind of AI news.

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @yampeleg @nisten @ldjconfirmed

* Paul Kelin @pk_iv - Browser Base

* Kwindla Kramer @kwindla - Pipecat & Daily

* Open Source LLMs

* DeepSeek-OCR: Efficient Vision-Text Compression for Massive Contexts (X, HF, Paper)

* Liquid AI LFM2-VL-3B: Tiny Multilingual Vision-Language Model (X, HF)

* PokeeResearch-7B: Open-source SOTA Deep Research Agent (X, HF, Web, ArXiv, GitHub)

* Qwen3-VL 2B & 32B: compact STEM-tuned multimodal powerhouses (X, Hugging Face)

* Big CO LLMs + APIs

* OpenAI announces Atlas - its agentic AI browser (X, Download)

* Security Implications, Injection + note from CISO

* Claude Code on the Web: Cloud Coding with Secure Sandboxing (X, Anthropic)

* Meta bans 1‑800‑ChatGPT on WhatsApp

* Microsoft agentic addition to Copilot Mode in Edge (X)

* Gemini AI Studio launches “Vibe Coding” (X, AI Studio Build)

* This weeks Buzz

* Fully connected comes to Tokyo (Oct 30-31) and London (Nov 4-5) ! (register at Fullyconnected.com)

* Vision & Video

* Sora is about to get pet cameos

* Krea open‑sources a 14‑billion‑parameter real‑time video model (X, HF)

* Reve’s unannounced video mode!? 1080p + sound

* LTX-2: open-source 4K audio+video generation engine from Lightricks (X, Website, GitHub)

* Voice & Audio

* Decart Lip Sync API: Real-Time Avatar Lip Movement (X)

* Tools

* Browserbase launches Director 2.0: prompt-powered web automation (X, Director.ai, Stagehand)

Can you believe it’s already the middle of October? This week’s show was a special one, not just because of the mind-blowing news, but because we set a new ThursdAI record with four incredible interviews back-to-back!

We had Jessica Gallegos from Google DeepMind walking us through the cinematic new features in VEO 3.1. Then we dove deep into the world of Reinforcement Learning with my new colleague Kyle Corbitt from OpenPipe. We got the scoop on Amp’s wild new ad-supported free tier from CEO Quinn Slack. And just as we were wrapping up, Swyx ( from Latent.Space , now with Cognition!) jumped on to break the news about their blazingly fast SWE-grep models.

But the biggest story? An AI model from Google and Yale made a novel scientific discovery about cancer cells that was then validated in a lab. This is it, folks. This is the “let’s f*****g go” moment we’ve been waiting for. So buckle up, because this week was an absolute monster. Let’s dive in!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source: An AI Model Just Made a Real-World Cancer Discovery

We always start with open source, but this week felt different. This week, open source AI stepped out of the benchmarks and into the biology lab.

Our friends at Qwen kicked things off with new 3B and 8B parameter versions of their Qwen3-VL vision model. It’s always great to see powerful models shrink down to sizes that can run on-device. What’s wild is that these small models are outperforming last generation’s giants, like the 72B Qwen2.5-VL, on a whole suite of benchmarks. The 8B model scores a 33.9 on OS World, which is incredible for an on-device agent that can actually see and click things on your screen. For comparison, that’s getting close to what we saw from Sonnet 3.7 just a few months ago. The pace is just relentless.

But then, Google dropped a bombshell. A 27-billion parameter Gemma-based model they developed with Yale, called C2S-Scale, generated a completely novel hypothesis about how cancer cells behave. This wasn’t a summary of existing research; it was a new idea, something no human scientist had documented before. And here’s the kicker: researchers then took that hypothesis into a wet lab, tested it on living cells, and proved it was true.

This is a monumental deal. For years, AI skeptics like Gary Marcus have said that LLMs are just stochastic parrots, that they can’t create genuinely new knowledge. This feels like the first, powerful counter-argument. Friend of the pod, Dr. Derya Unutmaz, has been on the show before saying AI is going to solve cancer, and this is the first real sign that he might be right. The researchers noted this was an “emergent capability of scale,” proving once again that as these models get bigger and are trained on more complex data—in this case, turning single-cell RNA sequences into “sentences” for the model to learn from—they unlock completely new abilities. This is AI as a true scientific collaborator. Absolutely incredible.

Big Companies & APIs

The big companies weren’t sleeping this week, either. The agentic AI race is heating up, and we’re seeing huge updates across the board.

Claude Haiku 4.5: Fast, Cheap Model Rivals Sonnet 4 Accuracy (X, Official blog, X)

First up, Anthropic released Claude Haiku 4.5, and it is a beast. It’s a fast, cheap model that’s punching way above its weight. On the SWE-bench verified benchmark for coding, it hit 73.3%, putting it right up there with giants like GPT-5 Codex, but at a fraction of the cost and twice the speed of previous Claude models. Nisten has already been putting it through its paces and loves it for agentic workflows because it just follows instructions without getting opinionated. It seems like Anthropic has specifically tuned this one to be a workhorse for agents, and it absolutely delivers.

The thing to note also is the very impressive jump in OSWorld (50.7%), which is a computer use benchmark, and at this price and speed ($1/$5 MTok input/output) is going to make computer agents much more streamlined and speedy!

ChatGPT will loose restrictions; age-gating enables “adult mode” with new personality features coming (X)

Sam Altman set X on fire with a thread announcing that ChatGPT will start loosening its restrictions. They’re planning to roll out an “adult mode” in December for age-verified users, potentially allowing for things like erotica. More importantly, they’re bringing back more customizable personalities, trying to recapture some of the magic of GPT-4.0 that so many people missed. It feels like they’re finally ready to treat adults like adults, letting us opt-in to R-rated conversations while keeping strong guardrails for minors. This is a welcome change, and we’ve been advocating for this for a while, and it’s a notable change from the XAI approach I covered last week. Opt in for adults with verification while taking precautions vs engagement bait in the form of a flirty animated waifu with engagement mechanics.

Microsoft is making every windows 11 an AI PC with copilot voice input and agentic powers (Blog,X)

And in breaking news from this morning, Microsoft announced that every Windows 11 machine is becoming an AI PC. They’re building a new Copilot agent directly into the OS that can take over and complete tasks for you. The really clever part? It runs in a secure, sandboxed desktop environment that you can watch and interact with. This solves a huge problem with agents that take over your mouse and keyboard, locking you out of your own computer. Now, you can give the agent a task and let it run in the background while you keep working. This is going to put agentic AI in front of hundreds of millions of users, and it’s a massive step towards making AI a true collaborator at the OS level.

NVIDIA DGX - the tiny personal supercomputer at $4K (X, LMSYS Blog)

NVIDIA finally delivered their promised AI Supercomputer, and while the excitement was in the air with Jensen hand delivering the DGX Spark to OpenAI and Elon (recreating that historical picture when Jensen hand delivered a signed DGX workstation while Elon was still affiliated with OpenAI). The workstation was sold out almost immediately. Folks from LMSys did a great deep dive into specs, all the while, folks on our feeds are saying that if you want to get the maximum possible open source LLMs inference speed, this machine is probably overpriced, compared to what you can get with an M3 Ultra Macbook with 128GB of RAM or the RTX 5090 GPU which can get you similar if not better speeds at significantly lower price points.

Anthropic’s “Claude Skills”: Your AI Agent Finally Gets a Playbook (Blog)

Just when we thought the week couldn’t get any more packed, Anthropic dropped “Claude Skills,” a huge upgrade that lets you give your agent custom instructions and workflows. Think of them as expertise folders you can create for specific tasks. For example, you can teach Claude your personal coding style, how to format reports for your company, or even give it a script to follow for complex data analysis.

The best part is that Claude automatically detects which “Skill” is needed for a given task, so you don’t have to manually load them. This is a massive step towards making agents more reliable and personalized, moving beyond just a single custom instruction and into a library of repeatable, expert processes. It’s available now for all paid users, and it’s a feature I’ve been waiting for. Our friend Simon Willison things skills may be a bigger deal than MCPs!

🎬 Vision & Video: Veo 3.1, Sora Gets Longer, and Real-Time Worlds

The AI video space is exploding. We started with an amazing interview with Jessica Gallegos, a Senior Product Manager at Google DeepMind, all about the new Veo 3.1. This is a significant 0.1 update, not a whole new model, but the new features are game-changers for creators.

The audio quality is way better, and they’ve massively improved video extensions. The model now conditions on the last second of a clip—including the audio. This means if you extend a video of someone talking, they keep talking in the same voice! This is huge, saving creators from complex lip-syncing and dubbing workflows. They also added object insertion and removal, which works on both generated and real-world video. Jessica shared an incredible story about working with director Darren Aronofsky to insert a virtual baby into a live-action film shot, something that’s ethically and practically very difficult to do on a real set. These are professional-grade tools that are becoming accessible to everyone. Definitely worth listening to the whole interview with Jessica, starting at 00:25:44

I’ve played with the new VEO in Google Flow, and while I was somewhat (still) disappointed with the UI itself (it froze sometimes and didn’t play). I wasn’t able to upload my own videos to use the insert/remove features Jessica mentioned yet, but saw examples online and they looked great!

Ingredients were also improved with VEO 3.1, where you can add up to 3 references, and they will be included in your video but not as first frame, the model will use them to condition the vidoe generation. Jessica clarified that if you upload sound, as in, your voice, it won’t show up in the model as your voice yet, but maybe they will add this in the future (at least this was my feedback to her).

SORA 2 extends video gen to 15s for all, 25 seconds to pro users with a new storyboard

Not to be outdone, OpenAI pushed a bit of an update for Sora. All users can now generate up to 15-second clips (up from 8-10), and Pro users can go up to 25 seconds using a new storyboard feature. I’ve been playing with it, and while the new scene-based workflow is powerful, I’ve noticed the quality can start to degrade significantly in the final seconds of a longer generation (posted my experiments here) as you can see. The last few shot so the cowboy don’t have any action, and the face is a blurry mess.

Worldlabs RTFM: Real-Time Frame Model renders 3D worlds at interactive speeds on a single H100 ( X, Blog, Demo )

And just when we thought we’d seen it all, World Labs dropped a breaking news release: RTFM, the Real-Time Frame Model. This is a generative world model that renders interactive, 3D-consistent worlds on the fly, all on a single H100 GPU. Instead of pre-generating a 3D environment, it’s a “learned renderer” that streams pixels as you move. We played with the demo live on the show, and it’s mind-blowing. The object permanence is impressive; you can turn 360 degrees and the scene stays perfectly coherent. It feels like walking around inside a simulation being generated just for you.

This Week’s Buzz: RL Made Easy with Serverless RL + interview with Kyle Corbitt

It was a huge week for us at Weights & Biases and CoreWeave. I was thrilled to finally have my new colleague Kyle Corbitt, founder of OpenPipe, back on the show to talk all things Reinforcement Learning (RL).

RL is the technique behind the massive performance gains we’re seeing in models for tasks like coding and math. At a high level, it lets a model try things, and then you “reward” it for good outcomes and penalize it for bad ones, allowing it to learn strategies that are better than what was in its original training data. The problem is, it’s incredibly complex and expensive to set up the infrastructure. You have to juggle an inference stack for generating the “rollouts” and a separate training stack for updating the model weights.

This is the problem Kyle and his team have solved with Serverless RL, which we just launched and we covered last week. It’s a new offering that lets you run RL jobs without managing any servers or GPUs. The whole thing is powered by the CoreWeave stack, with tracing and evaluation beautifully visualized in Weave.

We also launched a new model from the OpenPipe team on our inference service: a fine-tune-friendly “instruct” version of Qwen3 14B. The team is not just building amazing products, they’re also contributing great open-source models. It’s awesome to be working with them.

🛠️ Tools & Agents: Free Agents & Lightning-Fast Code Search

The agentic coding space saw two massive announcements this week, and we had the representatives of both companies on the show to discuss them!

First, Quinn Slack from Amp announced that they’re launching a completely free, ad-supported tier. I’ll be honest, my first reaction was, “Ads? In my coding agent? Eww.” But the more I thought about it, the more it made sense. My AI subscriptions are stacking up, and this model makes powerful agentic coding accessible to students and developers who can’t afford another $20/month. The ads are contextual to your codebase (think Baseten or Axiom), and they’re powered by a rotating mix of models using surplus capacity from providers. It’s a bold and fascinating business model.

This move was met with generally positive responses, though folks from a competing agent, claim that Amp is serving Grok-4-fast which XAI is giving out for free anyway? We’ll see how this shakes up.

Cognition announces SWE-grep: RL-powered multi-turn context retriever for agentic code search (Blog, X, Playground, Windsurf)

Then, just as we were about to sign off, friend of the pod Swyx (now from Cognition) dropped in with breaking news about SWE-grep. It’s a new, RL-tuned sub-agent for their Windsurf editor that makes code retrieval and context gathering ridiculously fast. We’re talking over 2,800 tokens per second. (yes, they are using Cerebras under the hood)

The key insight from Swyx is that their model was trained for natively parallel tool calling, running up to eight searches on a codebase simultaneously. This speeds up the “read” phase of an agent’s workflow—which is 60-70% of the work—by 3-5x. It’s all about keeping the developer in a state of flow, and this is a huge leap forward in making agent interactions feel instantaneous. Swyx also dropped a hint that the next thing that comes is CodeMaps and they will make these retrievers look trivial!

This was one for the books, folks. An AI making a novel cancer discovery, video models taking huge leaps, and the agentic coding space is on fire. The pace of innovation is just breathtaking. Thank you for being a ThursdAI subscriber, and as always, here’s the TL:DR and show notes for everything that happened in AI this week.

TL;DR and Show Notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

* Jessica Gallegos, Sr. Product Manager, Google DeepMind

* Kyle Corbitt (@corbtt) - OpenPipe//W&B

* Quinn Slack (@sqs) - Amp

* Swyx (@swyx) - Cognition

* Open Source LLMs

* KAIST KROMo - bilingual Korean/English 10B (HF, Paper)

* Qwen3-VL 3B and 8B (X post, HF)

* Google’s C2S-Scale 27B: AI Model Validates Cancer Hypothesis in Living Cells (X, Blog, Paper)

* Big CO LLMs + APIs

* Claude Haiku 4.5: Fast, Cheap Model Rivals Sonnet 4 Accuracy (X, Official blog)

* ChatGPT will loose restrictions; age-gating enables “adult mode” with new personality features coming (X)

* OpenAI updates memory management - no more “memory full” (X, FAQ)

* Microsoft is making every windows 11 an AI PC with copilot voice input (X)

* Claude Skills: Custom instructions for AI agents now live (X, Anthropic News, YouTube Demo)

* Hardware

* NVIDIA DGX Spark: desktop personal supercomputer for AI prototyping and local inference (LMSYS Blog)

* Apple announces M5 chip with double AI performance (Apple Newsroom)

* OpenAI and Broadcom set to deploy 10 gigawatts of custom AI accelerators (Official announcement)

* This weeks Buzz

* New model - OpenPipe Qwen3 14B instruct (link)

* Interview with Kyle Corbitt - RL, Serverless RL

* W&B Fully Connected London & Tokyo in 20 days - SIGN UP

* Vision & Video

* Veo 3.1: Google’s Next-Gen Video Model Launches with Cinematic Audio (Developers Blog)

* Sora up to 15s and pro now up to 25s generation with a new storyboard feature

* Baidu’s MuseStreamer has >20 second generations (X)

* AI Art & Diffusion & 3D

* Worldlabs RTFM: Real-Time Frame Model renders 3D worlds at interactive speeds on a single H100 (Blog, Demo)

* DiT360: SOTA Panoramic Image Generation with Hybrid Training (Project page, GitHub)

* Riverflow 1 tops the image‑editing leaderboard (Sourceful blog)

* Tools

* Amp launches a Free tier - powered by ads and surplus model capacity (Website)

* Cognition SWE-grep: RL-powered multi-turn context retriever for agentic code search (Blog, Playground)

We’re deep in the post-reality era now. Between Sora2, the latest waves of video models, and “is-that-person-real” cameos, it’s getting genuinely hard to trust what we see. Case in point: I recorded a short clip with (the real) Sam Altman this week and a bunch of friends thought I faked it with Sora-style tooling. Someone even added a fake Sora watermark just to mess with people. Welcome to 2025.

This week’s episode and this write-up focus on a few big arcs we’re all living through at once: OpenAI’s Dev Day and the beginning of the agent-app platform inside ChatGPT, a bizarre and exciting split-screen in model scaling where a 7M recursive model from Samsung is suddenly competitive on reasoning puzzles while inclusionAI is shipping a trillion-parameter mixture-of-reasoners, and Grok’s image-to-video now does audio and pushes the line on… taste. We also dove into practical evals for coding agents with Eric Provencher from Repo Prompt, and I’ve got big news from my day job world: W&B + CoreWeave launched Serverless RL, so training and deploying RL agents at scale is now one API call away.

Let’s get into it.

OpenAI’s 3rd Dev Day - Live Coverage + exclusive interviews

This is the third Dev Day that I got to attend in person, covering this for ThursdAI (2023, 2024), and this one was the best by far! The production quality of their events rises every year, and this year they’ve opened up the conference to >1500 people, had 3 main launches and a lot of ways to interact with the OpenAI folks!

I’ve also gotten an exclusive chance to sit in on a fireside chat with Sam Altman and Greg Brokman (snippets of which I’ve included in the podcast, starting 01:15:00 and I got to ask Sam a few questions after that as well.

Event Ambiance and Vibes

OpenAI folks outdid themselves with this event, the live demos were quite incredible, the location (Fort Mason), Food and just the whole thing was on point. The event concluded with a 1x1 Sam and Jony Ive chat that I hope will be published on YT sometime, because it was very insightful.

By far the best reason to go to this event in person is meeting folks and networking, both OpenAI employees, and AI Engineers who use their products. It’s 1 day a year, when OpenAI makes all their employees who attend, Developer Experience folks, as you can and are encouraged to, interact with them, ask questions, give feedback and it’s honestly great!

I really enjoy meeting folks at this event and consider this to be a very high signal network, and was honored to have quite a few ThursdAI listeners among the participants and OpenAI folk! If you’re reading this, thank you for your patronage 🫡

Launches and Ships

OpenAI also shipped, and shipped a LOT! Sam was up on Keynote with 3 main pillars, which we’ll break down 1 by 1. ChatGPT Apps, AgentKit (+ agent builder) and Codex/New APIs

Codex & New APIs

Codex has gotten General Availability, but we’ve been using it all this time so we don’t really care, what we do care about is the new slack integration and the new Codex SDK, which means you can now directly inject Codex agency into your app. This flew a bit over people’s heads, but Romain Huet, VP of DevEx at OpenAI demoed on stage how his mobile app now has a Codex tab, where he can ask Codex to make changes to the app at runtime! It was quite crazy!

ChatGPT Apps + AppsSDK

This was maybe the most visual and most surprising release, since they’ve tried to be an appstore before (plugins, customGPTs). But this time, it seems like, based on top of MCP, ChatGPT is going to become a full blown Appstore for 800+ million weekly active ChatGPT users as well.

Some of the examples they have showed included Spotify and Zillow, where just by typing in “Spotify” in chatGPT, you will have an interactive app with it’s own UI, right inside of ChatGPT. So you could ask it to create a playlist for you based on your history, or ask Zillow to find homes in an area under a certain $$ amount.

The most impressive thing, is that those are only launch partners, everyone can (technically) build a ChatGPT app with their AppsSDK that’s built on top of... the MCP (model context protocol) spec!

The main question remains about discoverability, this is where Plugins and CustomGPTs (previous attempts to create apps within ChatGPT have failed), and when I asked him about it, Sam basically said “we’ll iterate and get it right” (starting 01:17:00). So it remains to be seen if folks really need their ChatGPT as yet another Appstore.

AgentKit, AgentBuilder and ChatKit

2025 is the year of agents, and besides launching quite a few of their own, OpenAI will not let you, build and host smart agents that can use tools, on their platform. Supposedly, with AgentBuilder, building agents is just dragging a few nodes around, prompting and connecting them. They had a great demo on stage where with less than 8 minutes, they’ve build an agent to interact with the DevDay website.

It’s also great to see how greatly does OpenAI adapt the MCP spec, as this too, is powered by MCP, as in, any external connection you want to give your agent, must happen with an MCP server.

Agents for the masses is maybe not quite there yet

In reality though, things are not so easy. Agents require more than just a nice drag & drop interface, they require knowledge, iteration, constant evaluation (which they’ve also added, kudos!) and eventually, customized agents need code.

I spent an hour trying it out yesterday, building an agent to search the ThursdAI archives. The experience was a mixed bag. The AI-native features are incredibly cool. For instance, you can just describe the JSON schema you want as an output, and it generates it for you. The widget builder is also impressive, allowing you to create custom UI components for your agent’s responses.

However, I also ran into the harsh realities of agent building. My agent’s web browsing tool failed because Substack seems to be blocking OpenAI’s crawlers, forcing me to fall back on the old-school RAG approach of uploading our entire archive to a vector store. And while the built-in evaluation and tracing tools are a great idea, they were buggy and failed to help me debug the error. It’s a powerful tool, but it also highlights that building a reliable agent is an iterative, often frustrating process that a nice UI alone can’t solve. It’s not just about the infrastructure; it’s about wrestling with a stochastic machine until it behaves.

But to get started with something simple, they have definitely pushed the envelope on what is possible without coding.

OpenAI also dropped a few key API updates:

* GPT-5-Pro is now available via API. It’s incredibly powerful but also incredibly expensive. As Eric mentioned, you’re not going to be running agentic loops with it, but it’s perfect for a high-stakes initial planning step where you need an “expert opinion.”

* SORA 2 is also in the API, allowing developers to integrate their state-of-the-art video generation model into their own apps. The API can access the 15-second “Pro” model but doesn’t support the “Cameo” feature for now.

* Realtime-mini is a game-changer for voice AI. It’s a new, ultra-fast speech-to-speech model that’s 80% cheaper than the original Realtime API. This massive price drop removes one of the biggest barriers to building truly conversational, low-latency voice agents.

My Chat with Sam & Greg - On Power, Responsibility, and Energy

After the announcements, I’ve got to sit in a fireside chat with Sam Altman and Greg Brockman and ask some questions. Here’s what stood out:

When I asked about the energy requirements for their massive compute plans (remember the $500B Stargate deal?), Sam said they’d have announcements about Helion (his fusion investment) soon but weren’t ready to talk about it. Then someone from Semi Analysis told me most power will come from... generator trucks. 15-megawatt generator trucks that just drive up to data centers. We’re literally going to power AGI with diesel trucks!

On responsibility, when I brought up the glazing incident and asked how they deal with being in the lives of 800+ million people weekly, Sam’s response was sobering: “This is not the excitement of ‘oh we’re building something important.’ This is just the stress of the responsibility... The fact that 10% of the world is talking to kind of one brain is a strange thing and there’s a lot of responsibility.”

Greg added something profound: “AI is far more surprising than I anticipated... The deep nuance of how these problems contact reality is something that I think no one had anticipated.”

This Week’s Buzz: RL X-mas came early with Serverless RL! (X, Blog)

Big news from our side of the world! About a month ago, the incredible OpenPipe team joined us at Weights & Biases and CoreWeave. They are absolute wizards when it comes to fine-tuning and Reinforcement Learning (RL), and they wasted no time combining their expertise with CoreWeave’s massive infrastructure.

This week, they launched Serverless RL, a managed reinforcement learning service that completely abstracts away the infrastructure nightmare that usually comes with RL. It automatically scales your training and inference compute, integrates with W&B Inference for instant deployment, and simplifies the creation of reward functions and verifiers. RL is what turns a good model into a great model for a specific task, often with surprisingly little data. This new service massively lowers the barrier to entry, and I’m so excited to see what people build with it. We’ll be doing a deeper dive on this soon but please check out the Colab Notebook to get a taste of what AutoRL is like!

Open Source

While OpenAI was holding its big event, the open-source community was busy dropping bombshells of its own.

Samsung’s TRM: Is This 7M Parameter Model... Magic? (X, Blog, arXiv)

This was the release that had everyone’s jaws on the floor. A single researcher from the Samsung AI Lab in Montreal released a paper on a Tiny Recursive Model (TRM). Get this: it’s a 7 MILLION parameter model that is outperforming giants like DeepSeek-R1 and Gemini 2.5 Pro on complex reasoning benchmarks like ARC-AGI. I had to read that twice. 7 million, not billion.

How is this possible? Instead of relying on brute-force scale, TRM uses a recursive process. It generates a first draft of an answer, then repeatedly critiques and refines its own logic in a hidden “scratchpad” up to 16 times. As Yam pointed out, the paper is incredibly insightful, and it’s a groundbreaking piece of work from a single author, which is almost unheard of these days. Eric made a great point that because it’s so small, it opens the door for hobbyists and solo researchers to experiment with cutting-edge architectures on their home GPUs. This feels like a completely new direction for AI, and it’s incredibly exciting.

inclusionAI’s Ling-1T: Enter the Trillion Parameter Club (X, HF, Try it)

On the complete opposite end of the scale (about 3 OOM away), we have Ling-1Tfrom inclusionAI. This is a 1 TRILLION parameter Mixture-of-Experts (MoE) model. The key here is efficiency; while it has a trillion total parameters, it only uses about 37 billion active parameters per token.

The benchmarks are wild, showing it beating models like GPT-5-Main (in non-thinking mode) and Gemini 2.5 on a range of reasoning tasks. They claim to match Gemini’s performance using about half the compute. Of course, with any new model posting huge scores, there’s always the question of whether it was trained on the public test sets, but the results are undeniably impressive. It’s another example of the push towards maintaining top-tier performance while drastically reducing the computational cost of inference.

More Open Source Goodness: Microsoft, AI21, and IBM

It didn’t stop there.

* Microsoft released UserLM-8B, a fascinating Llama 3 finetune trained not to be an assistant, but to simulate the user in a conversation. As Yam explained from his own experience, this is a super useful technique for generating high-quality, multi-turn synthetic data to train more robust chatbot agents. (X, HF)

* Our friends at AI21 Labs are back with Jamba Reasoning 3B, a tiny but mighty 3-billion-parameter model. It uses a hybrid SSM-Transformer architecture, which makes it incredibly fast for its size, making it a great option for local inference on a laptop.

* IBM also released their Granite family of models, which also use a hybrid design. Their big focus is on enterprise-grade governance and trust, and it’s the first open model family to get an ISO certification for AI management systems.Big Company Moves: Grok Imagine Levels Up... And Leans In

Finally, let’s talk about the latest update to Grok Imagine. They’ve rolled out video generation with synchronized sound, and it’s fast—often faster than Sora. The quality has significantly improved, and it’s a powerful tool.

However, I have to talk about the other side of this. Grok is positioning itself as the “uncensored” alternative, and they are leaning into that hard. Their video generator has a “spicy” mode that explicitly generates 18+ content. But the thing that truly disturbed me was a new feature with their animated character, “Annie.” It’s a gamified engagement mechanic where you “make your connection better” by talking to her every day to unlock special rewards, like new outfits.

To be blunt, this is disgusting. We talk a lot on this show about the immense responsibility that comes with building these powerful AIs. I know from my conversations with folks at OpenAI and other labs that they think deeply about safety, preventing misuse, and the psychological impact these systems can have. This feature from Grok is the polar opposite. It leans into the worst fears about AI creating addictive, para-social relationships. It’s exploitative, and frankly, the team behind it should reconsider their choices IMO.

All righty, this is mostly the new for this week, it’s been a very busy week, and if you’d like to see our live coverage + DevDay keynote + interviews I’ve had with Simon Willison , Greg Kamradt, Jeffrey Huber, Allesio from Latent.Space, Matthew Berman and more impactful folks, our livestream can be found here:

I’m incredibly humbled and privileged to keep being invited to the Dev Day, and looking forward to cover more events, with exclusive interviews, on the ground reporting and insights. Please subscribe if you like this content to continue.

TL;DR and Show Notes

* Show Notes & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Guest: Kyle Corbitt - OpenPipe / CoreWeave (@corbtt)

* Guest: Eric Provencher - Repo Prompt (@pvncher)

* OpenAI Dev Day

* OpenAI AgentKit All-in-One Agent Builder (X, OpenAI)

* ChatGPT Apps & New APIs (GPT-5-pro, SORA, realtime-mini)

* Open Source LLMs

* Microsoft UserLM-8B Model Released (X, HF)

* Samsung Tiny Recursive Model (TRM) (X, Blog, arXiv)

* AI21 Labs releases Jamba Reasoning 3B (X, HF)

* inclusionAI debuts Ling-1T: Trillion-Scale Efficient Reasoner (X, HF, Try it)

* IBM Granite Models

* Evals

* Repo Bench by Repo Prompt (Web)

* Big CO LLMs + APIs

* Qwen 3 Omni & Realtime Models

* Google DeepMind unveils Gemini 2.5 Computer-Use model (X, Blog)

* Google Gemini Flash 2.5 (new)

* Grok Imagine updated with video and sound

* This weeks Buzz

* OpenPipe (part of Coreweave,W&B) launch Serverless RL (X, Blog, Notebook)

* Vision & Video

* Ovi: Open Source Video & Synchronized Audio Generation (X, HF)

* Voice & Audio

* GPT-realtime-mini: OpenAI’s ultra-fast speech-to-speech model API (OpenAI Blog, TechCrunch)

* AI Art & Diffusion & 3D

* Bagel.com: Paris – Decentralized Diffusion Model (X, HF, Blogpost)

The weeks are getting crazier, but what OpenAI pulled this week, with a whole new social media app attached to their latest AI breakthroughs is definitely breathtaking! Sora2 released and instantly became a viral sensation, shooting to the top 3 free iOS spot on AppStore, with millions of videos watched, and remixed.

On weeks like these, even huge releases like Claude 4.5 are taking the backseat, but we still covered them!

For listeners of the pod, the second half of the show was very visual heavy, so it may be worth it watching the YT video attached in a comment if you want to fully experience the Sora revolution with us! (and if you want a SORA invite but don’t have one yet, more on that below)

ThursdAI - if you find this valuable, please support us by subscribing!

Sora 2 - the AI video model that signifies a new era of social media

Look, you’ve probably already heard about the SORA-2 release, but in case you haven’t, OpenAI released a whole new model, but attached it to a new, AI powered social media experiment in the form of a very addictive TikTok style feed. Besides being hyper-realistic, and producing sounds and true to source voice-overs, Sora2 asks you to create your own “Cameo” by taking a quick video, and then allows you to be featured in your own (and your friends) videos.

This makes a significant break from the previously “slop” based meta Vibes, becuase, well, everyone loves seeing themselves as the stars of the show!

Cameos are a stroke of genius, and what’s more, one can allow everyone to use their Cameo, which is what Sam Altman did at launch, making everyone Cameo him, and turning him, almost instantly into one of the most meme-able (and approachable) people on the planet!

Sam sharing away his likeness like this for the sake of the app achieved a few things, it added trust in the safety features, made it instantly viral and showed folks they shouldn’t be afraid of adding their own likeness.

Vibes based feed and remixing

Sora 2 is also unique in that, it’s the first social media with UGC (user generated content) where content can ONLY be generated, and all SORA content is created within the app. It’s not possible to upload pictures that have people to create the posts, and you can only create posts with other folks if you have access to their Cameos, or by Remixing existing creations.

Remixing is also a way to let users “participate” in the creation process, by adding their own twist and vibes!

Speaking of Vibes, while the SORA app has an algorithmic For You page, they have a completely novel and new way to interact with the algorithm, by using their Pick a Mood feature, where you can describe which type of content you want to see, or not see, with natural language!

I believe that this feature will come to all social media platforms later, as it’s such a game changer. Want only content in a specific language? or content that doesn’t have Sam Altman in it? Just ask!

Content that makes you feel good

The most interesting thing is about the type of content is, there’s no sexualisation (because all content is moderated by OpenAI strong filters), and no gore etc. OpenAI has clearly been thinking about teenagers and have added parent controls, things like being able to turn of the For You page completely etc to the mix.

Additionally, SORA seems to be a very funny model, and I mean this literally. You can ask the video generation for a joke and you’ll often get a funny one. The scene setup, the dialogue, the things it does even unprompted are genuinely entertaining.

AI + Product = Profit?

OpenAI shows that they are one of the worlds best product labs in the world, not just a foundational AI lab. Most AI advancements are tied to products, and in this case, the whole experience is so polished, it’s hard to accept that it’s a brand new app from a company that didn’t do social before. There’s very little buggy behavior, videos are loaded up quick, there’s even DMs! I’m thoroughly impressed and am immersing myself in the SORA sphere. Please give me a follow there and feel free to use my Cameo by tagging @altryne in there. I love seeing how folks have used my Cameo, it makes me laugh 😂

The copyright question is.. wild

Remember last year when I asked Sam why Advanced Voice Mode couldn’t sing Happy Birthday? He said they didn’t have classifiers to detect IP violations. Well, apparently that’s not a concern anymore because SORA 2 will happily generate perfect South Park episodes, Rick and Morty scenes, and Pokemon battles. They’re not even pretending they didn’t train on this stuff. You can even generate videos with any dead famous person (I’ve had zoom meetings with Michael Jackson and 2Pac, JFK and Mister Rogers)

Our friend Ryan Carson already used it to create a YouTube short ad for his startup in two minutes. What would have cost $100K and three months now takes six generations and you’re done. This is the real game-changer for businesses.

Getting invited

EDIT: If you’re reading this on Friday, try the code `FRIYAY` and let me know in comments if it worked for you 🙏

I wish I would have invites for all of you, but all invited users have 4 other folks they can invite, so we shared a bunch of invites during the live show, and asked folks to come back and invite other listeners, this went on for half an hour so I bet we’ve got quite a few of you in! If you’re still looking for an invite, you can visit the thread on X and see who claimed and invite and ask them for one, tell them you’re also a ThursdAI listener, they hopefully will return the favor!

Alternatively, OpenAI employees often post codes with a huge invite ratio, so follow @GabrielPeterss4 who often posts codes and you can get in there fairly quick, and if you’re not in the US, I heard a VPN works well. Just don’t forget to follow me on there as well 😉

A Week with OpenAI Pulse: The Real Agentic Future is Here

Listen to me, this may be a hot take. I think OpenAI Pulse is a bigger news story than Sora. I’ve told you about Pulse last week, but today on the show I was able to share my weeks worth of experience, and honestly, it’s now the first thing I look at when I wake up in the morning after brushing my teeth!

While Sora is changing media, Pulse is changing how we interact with AI on a fundamental level. Released to Pro subscribers for now, Pulse is an agentic, personalized feed that works for you behind the scenes. Every morning, it delivers a briefing based on your interests, your past conversations, your calendar—everything. It’s the first asynchronous AI agent I’ve used that feels truly proactive.

You don’t have to trigger it. It just works. It knew I had a flight to Atlanta and gave me tips. I told it I was interested in Halloween ideas for my kids, and now it’s feeding me suggestions. Most impressively, this week it surfaced a new open-source video model, Kandinsky 5.0, that I hadn’t seen anywhere on X or my usual news feeds. An agent found something new and relevant for my show, without me even asking.

This is it. This is the life-changing-level of helpfulness we’ve all been waiting for from AI. Personalized, proactive agents are the future, and Pulse is the first taste of it that feels real. I cannot wait for my next Pulse every morning.

This Week’s Buzz: The AI Build-Out is NOT a Bubble

This show is powered by Weights & Biases from CoreWeave, and this week that’s more relevant than ever. I just got back from a company-wide offsite where we got a glimpse into the future of AI infrastructure, and folks, the scale is mind-boggling.

CoreWeave, our parent company, is one of the key players providing the GPU infrastructure that powers companies like OpenAI and Meta. And the commitments being made are astronomical. In the past few months, CoreWeave has locked in a $22.4B deal with OpenAI, a $14.2B pact with Meta, and a $6.3B “backstop” guarantee with NVIDIA that runs through 2032.

If you hear anyone talking about an “AI bubble,” show them these numbers. These are multi-year, multi-billion dollar commitments to build the foundational compute layer for the next decade of AI. The demand is real, and it’s accelerating. And the best part? As a Weights & Biases user, you have access to this same best-in-class infrastructure that runs OpenAI through our inference services. Try wandb.me/inference, and let me know if you need a bit of a credit boost!

Claude Sonnet 4.5: The New Coding King Has a Few Quirks

On any other week, Anthropic’s release of Claude Sonnet 4.5 would’ve been the headline news. They’re positioning it as the new best model for coding and complex agents, and the benchmarks are seriously impressive. It matches or beats their previous top-tier model, Opus 4.1, on many difficult evals, all while keeping the same affordable price as the previous Sonnet.

One of the most significant jumps is on the OS World benchmark, which tests an agent’s ability to use a computer—opening files, manipulating windows, and interacting with applications. Sonnet 4.5 scored a whopping 61.4%, a massive leap from Opus 4.1’s 44%. This clearly signals that Anthropic is doubling down on building agents that can act as real digital assistants.

However, the real-world experience has been a bit of a mixed bag. My co-host Ryan Carson, whose company Amp switched over to 4.5 right away, noted some regressions and strange errors, saying they’re even considering switching back to the previous version until the rough edges are smoothed out. Nisten also found it could be more susceptible to “slop catalysts” in prompting. It seems that while it’s incredibly powerful, it might require some re-prompting and adjustments to get the best, most stable results. The jury’s still out, but it’s a potent new tool in the developer’s arsenal.

Open Source LLMs: DeepSeek’s Attention Revolution

Despite the massive news from the big companies, open source still brought the heat this week, with one release in particular representing a fundamental breakthrough.

DeepSeek released V3.2 Experimental, and the big news is DSA, or DeepSeek Sparse Attention. For those who don’t know, one of the biggest bottlenecks in LLMs is the “quadratic attention problem”—as you double the context length, the computation and memory required quadruple. This makes very long contexts incredibly expensive. DeepSeek’s new architecture makes the cost curve nearly flat, allowing for massive context at a fraction of the cost, all while maintaining the same SOTA performance as their previous model.

This is one of those “unhobbling moments,” like the invention of RoPE or GRPO, that moves the entire field forward. Everyone will be able to implement this, making all open-source models faster and more efficient. It’s a huge deal.

We also saw major releases from Z.ai with GLM-4.6, an advanced agentic model with a 200K context window that’s getting incredibly close to Claude’s performance, and a surprise from ServiceNow SLAM Labs, who dropped Apriel-1.5-15B, a frontier-level multimodal model that’s fully open source. It’s amazing to see a huge enterprise company contributing to the open-source ecosystem at this level.

Multimodal Madness: Audio, Video, and Image Models updates

The torrent of releases continued across all modalities this week, a bit overshadowed by SORA but definitely still happened (all links in the TL;DR section)

In voice and audio, our friends at Hume AI launched Octave 2, their next-gen text-to-speech model that’s faster, cheaper, and now fluent in over 11 languages. We also saw LFM2-Audio from Liquid AI, an incredibly efficient 1.5B parameter end-to-end audio model with sub-100ms latency.

In video, the open-source community answered Sora 2 with Kandinsky 5.0, a new 2B parameter text-to-video model that is claiming the #1 spot in open source and looks incredibly promising. And as I mentioned on the show, I wouldn’t have even known about it if it weren’t for my new personal AI agent, Pulse!

Finally, in AI art, Tencent dropped a monster: HunyuanImage 3.0, a massive 80-billion-parameter open-source text-to-image model. The scale of these open-source releases is just breathtaking.

Agentic browsing for all is here

Just as I was wrapping up the show, Perplexity has decided to let everyone in to use their Comet Agentic browser. I strongly recommend it, as I switched to it lately and it’s great!

I’m using it right now to run some agents, it can click stuff, scroll through stuff, collect info across tabs, it’s really great. Give it a spin, really, it’s worth getting into the habit of agentic browsing!

Many of you were asking me for invites before, well, it’s free access now, download it here (not sponsored, I just really like it)

Phew, ok, this was a WILD week, and I’m itching to get back to creating and seeing all the folks who used my Cameo on SORA, which you can see too btw if you hit the Cameo button here (https://sora.chatgpt.com/profile/altryne)

Next week is OpenAI’s Dev Day, and for the third year in a row we’re going to cover it, so follow us on social media and tune in Monday 8:30am Pacific. We’ll be live streaming from the location and re-streaming the keynote with Sam so don’t miss it!

TL;DR and Show Notes

Hosts and Guests:

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson

Big CO LLMs + APIs:

* OpenAI releases SORA2 + a new social media app (X, Blog, App download)

* Anthropic releases Claude Sonnet 4.5 - same price as 4.1 - leading coding model (X)

* OpenAI launches Instant Checkout & Agentic Commerce Protocol (X, Protocol)

Open Source LLMs:

* DeepSeek V3.2 Exp: Sparse Attention, Cost Drop (X, Evals, HF)

* Apriel-1.5-15B-Thinker by ServiceNow SLAM Labs (X, HF, Arxiv)

* Z.ai GLM-4.6: advanced Agentic flagship model (X, Blog, HF)

This weeks Buzz:

* CoreWeave locks $22.4B OpenAI, a $6.3B NVIDIA “backstop”, and a $14.2B Meta compute pact (X)

Voice & Audio:

* Hume AI launches Octave 2 (X, Blog)

* LFM2-Audio: End-to-end audio foundation model (X, Blog, HF)

Vision & Video:

* Kandinsky 5.0 T2V Lite: #1 open-source text-to-video (Blog, GitHub, HF, Try It)

AI Art & Diffusion & 3D:

* HunyuanImage 3.0: 80B Open-Source Text-to-Image by Tencent (X, HF, Github)

Hola AI aficionados, it’s yet another ThursdAI, and yet another week FULL of AI news, spanning Open Source LLMs, Multimodal video and audio creation and more!

Shiptember as they call it does seem to deliver, and it was hard even for me to follow up on all the news, not to mention we had like 3-4 breaking news during the show today!

This week was yet another Qwen-mas, with Alibaba absolutely dominating across open source, but also NVIDIA promising to invest up to $100 Billion into OpenAI.

So let’s dive right in! As a reminder, all the show notes are posted at the end of the article for your convenience.

ThursdAI - Because weeks are getting denser, but we’re still here, weekly, sending you the top AI content! Don’t miss out

Table of Contents

* Open Source AI

* Qwen3-VL Announcement (Qwen3-VL-235B-A22B-Thinking):

* Qwen3-Omni-30B-A3B: end-to-end SOTA omni-modal AI unifying text, image, audio, and video

* DeepSeek V3.1 Terminus: a surgical bugfix that matters for agents

* Evals & Benchmarks: agents, deception, and code at scale

* Big Companies, Bigger Bets!

* OpenAI: ChatGPT Pulse: Proactive AI news cards for your day

* XAI Grok 4 fast - 2M context, 40% fewer thinking tokens, shockingly cheap

* Alibaba Qwen-Max and plans for scaling

* This Week’s Buzz: W&B Fully Connected is coming to London and Tokyo & Another hackathon in SF

* Vision & Video: Wan 2.2 Animate, Kling 2.5, and Wan 4.5 preview

* Moondream-3 Preview - Interview with co-founders Via & Jay

* Wan open sourced Wan 2.2 Animate (aka “Wan Animate”): motion transfer and lip sync

* Kling 2.5 Turbo: cinematic motion, cheaper and with audio

* Wan 4.5 preview: native multimodality, 1080p 10s, and lip-synced speech

* Voice & Audio

* ThursdAI - Sep 25, 2025 - TL;DR & Show notes

Open Source AI

This was a Qwen-and-friends week. I joked on stream that I should just count how many times “Alibaba” appears in our show notes. It’s a lot.

Qwen3-VL Announcement (Qwen3-VL-235B-A22B-Thinking): (X, HF, Blog, Demo)

Qwen 3 launched earlier as a text-only family; the vision-enabled variant just arrived, and it’s not timid. The “thinking” version is effectively a reasoner with eyes, built on a 235B-parameter backbone with around 22B active (their mixture-of-experts trick). What jumped out is the breadth of evaluation coverage: MMU, video understanding (Video-MME, LVBench), 2D/3D grounding, doc VQA, chart/table reasoning—pages of it. They’re showing wins against models like Gemini 2.5 Pro and GPT‑5 on some of those reports, and doc VQA is flirting with “nearly solved” territory in their numbers.

Two caveats. First, whenever scores get that high on imperfect benchmarks, you should expect healthy skepticism; known label issues can inflate numbers. Second, the model is big. Incredible for server-side grounding and long-form reasoning with vision (they’re talking about scaling context to 1M tokens for two-hour video and long PDFs), but not something you throw on a phone.

Still, if your workload smells like “reasoning + grounding + long context,” Qwen 3 VL looks like one of the strongest open-weight choices right now.

Qwen3-Omni-30B-A3B: end-to-end SOTA omni-modal AI unifying text, image, audio, and video (HF, GitHub, Qwen Chat, Demo, API)

Omni is their end-to-end multimodal chat model that unites text, image, and audio—and crucially, it streams audio responses in real time while thinking separately in the background. Architecturally, it’s a 30B MoE with around 3B active parameters at inference, which is the secret to why it feels snappy on consumer GPUs.

In practice, that means you can talk to Omni, have it see what you see, and get sub-250 ms replies in nine speaker languages while it quietly plans. It claims to understand 119 languages. When I pushed it in multilingual conversational settings it still code-switched unexpectedly (Chinese suddenly appeared mid-flow), and it occasionally suffered the classic “stuck in thought” behavior we’ve been seeing in agentic voice modes across labs. But the responsiveness is real, and the footprint is exciting for local speech streaming scenarios. I wouldn’t replace a top-tier text reasoner with this for hard problems, yet being able to keep speech native is a real UX upgrade.

Qwen Image Edit, Qwen TTS Flash, and Qwen‑Guard

Qwen’s image stack got a handy upgrade with multi-image reference editing for more consistent edits across shots—useful for brand assets and style-tight workflows. TTS Flash (API-only for now) is their fast speech synth line, and Q‑Guard is a new safety/moderation model from the same team. It’s notable because Qwen hasn’t really played in the moderation-model space before; historically Meta’s Llama Guard led that conversation.

DeepSeek V3.1 Terminus: a surgical bugfix that matters for agents (X, HF)

DeepSeek whale resurfaced to push a small 0.1 update to V3.1 that reads like a “quality and stability” release—but those matter if you’re building on top. It fixes a code-switching bug (the “sudden Chinese” syndrome you’ll also see in some Qwen variants), improves tool-use and browser execution, and—importantly—makes agentic flows less likely to overthink and stall. On the numbers, Humanities Last Exam jumped from 15 to 21.7, while LiveCodeBench dipped slightly. That’s the story here: they traded a few raw points on coding for more stable, less dithery behavior in end-to-end tasks. If you’ve invested in their tool harness, this may be a net win.

Liquid Nanos: small models that extract like they’re big (X, HF)

Liquid Foundation Models released “Liquid Nanos,” a set of open models from roughly 350M to 2.6B parameters, including “extract” variants that pull structure (JSON/XML/YAML) from messy documents. The pitch is cost-efficiency with surprisingly competitive performance on information extraction tasks versus models 10× their size. If you’re doing at-scale doc ingestion on CPUs or small GPUs, these look worth a try.

Tiny IBM OCR model that blew up the charts (HF)

We also saw a tiny IBM model (about 250M parameters) for image-to-text document parsing trending on Hugging Face. Run in 8-bit, it squeezes into roughly 250 MB, which means Raspberry Pi and “toaster” deployments suddenly get decent OCR/transcription against scanned docs. It’s the kind of tiny-but-useful release that tends to quietly power entire products.

Meta’s 32B Code World Model (CWM) released for agentic code reasoning (X, HF)

Nisten got really excited about this one, and once he explained it, I understood why. Meta released a 32B code world model that doesn’t just generate code - it understands code the way a compiler does. It’s thinking about state, types, and the actual execution context of your entire codebase.

This isn’t just another coding model - it’s a fundamentally different approach that could change how all future coding models are built. Instead of treating code as fancy text completion, it’s actually modeling the program from the ground up. If this works out, expect everyone to copy this approach.

Quick note, this one was released with a research license only!

Evals & Benchmarks: agents, deception, and code at scale

A big theme this week was “move beyond single-turn Q&A and test how these things behave in the wild.” with a bunch of new evals released. I wanted to cover them all in a separate segment.

OpenAI’s GDP Eval: “economically valuable tasks” as a bar (X, Blog)

OpenAI introduced GDP Eval to measure model performance against real-world, economically valuable work. The design is closer to how I think about “AGI as useful work”: 44 occupations across nine sectors, with tasks judged against what an industry professional would produce.

Two details stood out. First, OpenAI’s own models didn’t top the chart in their published screenshot—Anthropic’s Claude Opus 4.1 led with roughly a 47.6% win rate against human professionals, while GPT‑5-high clocked in around 38%. Releasing a benchmark where you’re not on top earns respect. Second, the tasks are legit. One example was a manufacturing engineer flow where the output required an overall design with an exploded view of components—the kind of deliverable a human would actually make.

What I like here isn’t the precise percent; it’s the direction. If we anchor progress to tasks an economy cares about, we move past “trivia with citations” and toward “did this thing actually help do the work?”

GAIA 2 (Meta Super Intelligence Labs + Hugging Face): agents that execute (X, HF)

MSL and HF refreshed GAIA, the agent benchmark, with a thousand new human-authored scenarios that test execution, search, ambiguity handling, temporal reasoning, and adaptability—plus a smartphone-like execution environment. GPT‑5-high led across execution and search; Kimi’s K2 was tops among open-weight entries. I like that GAIA 2 bakes in time and budget constraints and forces agents to chain steps, not just spew plans. We need more of these.

Scale AI’s “SWE-Bench Pro” for coding in the large (HF)

Scale dropped a stronger coding benchmark focused on multi-file edits, 100+ line changes, and large dependency graphs. On the public set, GPT‑5 (not Codex) and Claude Opus 4.1 took the top two slots; on a commercial set, Opus edged ahead. The broader takeaway: the action has clearly moved to test-time compute, persistent memory, and program-synthesis outer loops to get through larger codebases with fewer invalid edits. This aligns with what we’re seeing across ARC‑AGI and SWE‑bench Verified.

The “Among Us” deception test (X)

One more that’s fun but not frivolous: a group benchmarked models on the social deception game Among Us. OpenAI’s latest systems reportedly did the best job both lying convincingly and detecting others’ lies. This line of work matters because social inference and adversarial reasoning show up in real agent deployments—security, procurement, negotiations, even internal assistant safety.

Big Companies, Bigger Bets!

Nvidia’s $100B pledge to OpenAI for 10GW of compute

Let’s say that number again: one hundred billion dollars. Nvidia announced plans to invest up to $100B into OpenAI’s infrastructure build-out, targeting roughly 10 gigawatts of compute and power. Jensen called it the biggest infrastructure project in history. Pair that with OpenAI’s Stargate-related announcements—five new datacenters with Oracle and SoftBank and a flagship site in Abilene, Texas—and you get to wild territory fast.

Internal notes circulating say OpenAI started the year around 230MW and could exit 2025 north of 2GW operational, while aiming at 20GW in the near term and a staggering 250GW by 2033. Even if those numbers shift, the directional picture is clear: the GPU supply and power curves are going vertical.

Two reactions. First, yes, the “infinite money loop” memes wrote themselves—OpenAI spends on Nvidia GPUs, Nvidia invests in OpenAI, the market adds another $100B to Nvidia’s cap for good measure. But second, the underlying demand is real. If we need 1–8 GPUs per “full-time agent” and there are 3+ billion working adults, we are orders of magnitude away from compute saturation. The power story is the real constraint—and that’s now being tackled in parallel.

OpenAI: ChatGPT Pulse: Proactive AI news cards for your day (X, OpenAI Blog)

In a #BreakingNews segment, we got an update from OpenAI, that currently works only for Pro users but will come to everyone soon. Proactive AI, that learns from your chats, email and calendar and will show you a new “feed” of interesting things every morning based on your likes and feedback!

Pulse marks OpenAI’s first step toward an AI assistant that brings the right info before you ask, tuning itself with every thumbs-up, topic request, or app connection. I’ve tuned mine for today, we’ll see what tomorrow brings!

P.S - Huxe is a free app from the creators of NotebookLM (Ryza was on our podcast!) that does a similar thing, so if you don’t have pro, check out Huxe, they just launched!

XAI Grok 4 fast - 2M context, 40% fewer thinking tokens, shockingly cheap (X, Blog)

xAI launched Grok‑4 Fast, and the name fits. Think “top-left” on the speed-to-cost chart: up to 2 million tokens of context, a reported 40% reduction in reasoning token usage, and a price tag that’s roughly 1% of some frontier models on common workloads. On LiveCodeBench, Grok‑4 Fast even beat Grok‑4 itself. It’s not the most capable brain on earth, but as a high-throughput assistant that can fan out web searches and stitch answers in something close to real time, it’s compelling.

Alibaba Qwen-Max and plans for scaling (X, Blog, API)

Back in the Alibaba camp, they also released their flagship API model, Qwen 3 Max, and showed off their future roadmap.

Qwen-max is over 1T parameters, MoE that gets 69.6 on Swe-bench verified and outperforms GPT-5 on LMArena!

And their plan is simple: scale. They’re planning to go from 1 million to 100 million token context windows and scale their models into the terabytes of parameters. It culminated in a hilarious moment on the show where we all put on sunglasses to salute a slide from their presentation that literally said, “Scaling is all you need.” AGI is coming, and it looks like Alibaba is one of the labs determined to scale their way there. Their release schedule lately (as documented by Swyx from Latent.space) is insane.

This Week’s Buzz: W&B Fully Connected is coming to London and Tokyo & Another hackathon in SF

Weights & Biases (now part of the CoreWeave family) is bringing Fully Connected to London on Nov 4–5, with another event in Tokyo on Oct 31. If you’re in Europe or Japan and want two days of dense talks and hands-on conversations with teams actually shipping agents, evals, and production ML, come hang out. Readers got a code on stream; if you need help getting a seat, ping me directly.

Links: fullyconnected.com

We are also opening up registrations to our second WeaveHacks hackathon in SF, October 11-12, yours trully will be there, come hack with us on Self Improving agents! Register HERE

Vision & Video: Wan 2.2 Animate, Kling 2.5, and Wan 4.5 preview

This is the most exciting space in AI week-to-week for me right now. The progress is visible. Literally.

Moondream-3 Preview - Interview with co-founders Via & Jay

While I’ve already reported on Moondream-3 in the last weeks newsletter, this week we got the pleasure of hosting Vik Korrapati and Jay Allen the co-founders of MoonDream to tell us all about it. Tune in for that conversation on the pod starting at 00:33:00

Wan open sourced Wan 2.2 Animate (aka “Wan Animate”): motion transfer and lip sync

Tongyi’s Wan team shipped an open-source release that the community quickly dubbed “Wanimate.” It’s a character-swap/motion transfer system: provide a single image for a character and a reference video (your own motion), and it maps your movement onto the character with surprisingly strong hair/cloth dynamics and lip sync. If you’ve used runway’s Act One, you’ll recognize the vibe—except this is open, and the fidelity is rising fast.

The practical uses are broader than “make me a deepfake.” Think onboarding presenters with perfect backgrounds, branded avatars that reliably say what you need, or precise action blocking without guessing at how an AI will move your subject. You act it; it follows.

Kling 2.5 Turbo: cinematic motion, cheaper and with audio

Kling quietly rolled out a 2.5 Turbo tier that’s 30% cheaper and finally brings audio into the loop for more complete clips. Prompts adhere better, physics look more coherent (acrobatics stop breaking bones across frames), and the cinematic look has moved from “YouTube short” to “film-school final.” They seeded access to creators and re-shared the strongest results; the consistency is the headline. (Source X: @StevieMac03)

I’ve chatted with my kiddos today over facetime, and they were building minecraft creepers. I took a screenshot, sent to Nano Banana to make their creepers into actual minecraft ones, and then with Kling, Animated the explosions for them. They LOVED it! Animations were clear, while VEO refused for me to even upload their images, Kling didn’t care haha

Wan 4.5 preview: native multimodality, 1080p 10s, and lip-synced speech

Wan also teased a 4.5 preview that unifies understanding and generation across text, image, video, and audio. The eye-catching bit: generate a 1080p, 10-second clip with synced speech from just a script. Or supply your own audio and have it lip-sync the shot. I ran my usual “interview a polar bear dressed like me” test and got one of the better results I’ve seen from any model. We’re not at “dialogue scene” quality, but “talking character shot” is getting… good.

The generation of audio (not only text + lipsync) is one of the best ones besides VEO, it’s really great to see how strongly this improves, sad that this wasn’t open sourced! And apparently it supports “draw text to animate” (Source: X)

Voice & Audio

Suno V5: we’ve entered the “I can’t tell anymore” era

Suno calls V5 a redefinition of audio quality. I’ll be honest, I’m at the edge of my subjective hearing on this. I’ve caught myself listening to Suno streams instead of Spotify and forgetting anything is synthetic. The vocals feel more human, the mixes cleaner, and the remastering path (including upgrading V4 tracks) is useful. The last 10% to “you fooled a producer” is going to be long, but the distance between V4 and V5 already makes me feel like I should re-cut our ThursdAI opener.

MiMI Audio: a small omni-chat demo that hints at the floor

We tried a MiMI Audio demo live—a 7B-ish model with speech in/out. It was responsive but stumbled on singing and natural prosody. I’m leaving it in here because it’s a good reminder that the open floor for “real-time voice” is rising quickly even for small models. And the moment you pipe a stronger text brain behind a capable, native speech front-end, the UX leap is immediate.

Ok, another DENSE week that finishes up Shiptember, tons of open source, Qwen (Tongyi) shines, and video is getting so so good. This is all converging folks, and honestly, I’m just happy to be along for the ride!

This week was also Rosh Hashanah, which is the Jewish new year, and I’ve shared on the pod that I’ve found my X post from 3 years ago, using the state of the art AI models of the time. WHAT A DIFFERENCE 3 years make, just take a look, I had to scale down the 4K one from this year just to fit into the pic!

Shana Tova to everyone who’s reading this, and we’ll see you next week 🫡

ThursdAI - Sep 25, 2025 - TL;DR & Show notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @yampeleg @nisten @ldjconfirmed @ryancarson

* Guest - Vik Korrapathy (@vikhyatk) - Moondream

* Open Source AI (LLMs, VLMs, Papers & more)

* DeepSeek V3.1 Terminus: cleaner bilingual output, stronger agents, cheaper long-context (X, HF)

* Meta’s 32B Code World Model (CWM) released for agentic code reasoning (X, HF)

* Alibaba Tongyi Qwen on a release streak again:

What an absolute packed week this week, which started with yet another crazy model release from OpenAI, but they didn't stop there, they also announced GPT-5 winning the ICPC coding competitions with 12/12 questions answered which is apparently really really hard!

Meanwhile, Zuck took the Meta Connect 25' stage and announced a new set of Meta glasses with a display! On the open source front, we yet again got multiple tiny models doing DeepResearch and Image understanding better than much larger foundational models.

Also, today I interviewed Jeremy Berman, who topped the ArcAGI with a 79.6% score and some crazy Grok 4 prompts, a new image editing experience called Reve, a new world model and a BUNCH more! So let's dive in! As always, all the releases, links and resources at the end of the article.

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Table of Contents

* Codex comes full circle with GPT-5-Codex agentic finetune

* Meta Connect 25 - The new Meta Glasses with Display & a neural control interface

* Jeremy Berman: Beating frontier labs to SOTA score on ARC-AGI

* This Week’s Buzz: Weave inside W&B models—RL just got x-ray vision

* Open Source

* Perceptron Isaac 0.1 - 2B model that points better than GPT

* Tongyi DeepResearch: A3B open-source web agent claims parity with OpenAI Deep Research

* Reve launches a 4-in-1 AI visual platform taking on Nano 🍌 and Seedream

* Ray3: Luma’s “reasoning” video model with native HDR, Draft Mode, and Hi‑Fi mastering

* World models are getting closer - Worldlabs announced Marble

* Google puts Gemini in Chrome

Codex comes full circle with GPT-5-Codex agentic finetune (X, OpenAI Blog)

My personal highlight of the week was definitely the release of GPT-5-Codex. I feel like we've come full circle here. I remember when OpenAI first launched a separate, fine-tuned model for coding called Codex, way back in the GPT-3 days. Now, they've done it again, taking their flagship GPT-5 model and creating a specialized version for agentic coding, and the results are just staggering.

This isn't just a minor improvement. During their internal testing, OpenAI saw GPT-5-Codex work independently for more than seven hours at a time on large, complex tasks—iterating on its code, fixing test failures, and ultimately delivering a successful implementation. Seven hours! That's an agent that can take on a significant chunk of work while you're sleeping. It's also incredibly efficient, using 93% fewer tokens than the base GPT-5 on simpler tasks, while thinking for longer on the really difficult problems.

The model is now integrated everywhere - the Codex CLI (just npm install -g codex), VS Code extension, web playground, and yes, even your iPhone. At OpenAI, Codex now reviews the vast majority of their PRs, catching hundreds of issues daily before humans even look at them. Talk about eating your own dog food!

Other OpenAI updates from this week

While Codex was the highlight, OpenAI (and Google) also participated and obliterated one of the world’s hardest algorithmic competitions called ICPC. OpenAI used GPT-5 and an unreleased reasoning model to solve 12/12 questions in under 5 hours.

OpenAI and NBER also released an incredible report on how over 700M people use GPT on a weekly basis, with a lot of insights, that are summed up in this incredible graph:

Meta Connect 25 - The new Meta Glasses with Display & a neural control interface

Just when we thought the week couldn't get any crazier, Zuck took the stage for their annual Meta Connect conference and dropped a bombshell. They announced a new generation of their Ray-Ban smart glasses that include a built-in, high-resolution display you can't see from the outside. This isn't just an incremental update; this feels like the arrival of a new category of device. We've had the computer, then the mobile phone, and now we have smart glasses with a display.

The way you interact with them is just as futuristic. They come with a "neural band" worn on the wrist that reads myoelectric signals from your muscles, allowing you to control the interface silently just by moving your fingers. Zuck's live demo, where he walked from his trailer onto the stage while taking messages and playing music, was one hell of a way to introduce a product.

This is how Meta plans to bring its superintelligence into the physical world. You'll wear these glasses, talk to the AI, and see the output directly in your field of view. They showed off live translation with subtitles appearing under the person you're talking to and an agentic AI that can perform research tasks and notify you when it's done. It's an absolutely mind-blowing vision for the future, and at $799, shipping in a week, it's going to be accessible to a lot of people. I've already signed up for a demo.

Jeremy Berman: Beating frontier labs to SOTA score on ARC-AGI

We had the privilege of chatting with Jeremy Berman, who just achieved SOTA on the notoriously difficult ARC-AGI benchmark using checks notes... Grok 4! 🚀

He walked us through his innovative approach, which ditches Python scripts in favor of flexible "natural language programs" and uses a program-synthesis outer loop with test-time adaptation. Incredibly, his method achieved these top scores at 1/25th the cost of previous systems

This is huge because ARC-AGI tests for true general intelligence - solving problems the model has never seen before. The chat with Jeremy is very insightful, available on the podcast starting at 01:11:00 so don't miss it!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

This Week’s Buzz: Weave inside W&B models—RL just got x-ray vision

You know how every RL project produces a mountain of rollouts that you end up spelunking through with grep? We just banished that misery. Weave tracing now lives natively inside every W&B Workspace run. Wrap your training-step and rollout functions in @weave.op, call weave.init(), and your traces appear alongside loss curves in real time. I can click a spike, jump straight to the exact conversation that tanked the reward, and diagnose hallucinations without leaving the dashboard. If you’re doing any agentic RL, please go treat yourself. Docs: https://weave-docs.wandb.ai/guides/tools/weave-in-workspaces

Open Source

Open source did NOT disappoint this week as well, we've had multiple tiny models beating the giants at specific tasks!

Perceptron Isaac 0.1 - 2B model that points better than GPT ( X, HF, Blog )

One of the most impressive demos of the week came from a new lab, Perceptron AI. They released Isaac 0.1, a tiny 2 billion parameter "perceptive-language" model. This model is designed for visual grounding and localization, meaning you can ask it to find things in an image and it will point them out. During the show, we gave it a photo of my kid's Harry Potter alphabet poster and asked it to "find the spell that turns off the light."

Not only did it correctly identify "Nox," but it drew a box around it on the poster. This little 2B model is doing things that even huge models like GPT-4o and Claude Opus can't, and it's completely open source. Absolutely wild.

Moondream 3 preview - grounded vision reasoning 9B MoE (2B active) (X, HF)

Speaking of vision reasoning models, just a bit after the show concluded, our friend Vik released a demo of Moondream 3, a reasoning vision model 9B (A2B) that is also topping the charts! I didn't have tons of time to get into this, but the release thread shows this to be an exceptional open source visual reasoner also beating the giants!

Tongyi DeepResearch: A3B open-source web agent claims parity with OpenAI Deep Research ( X, HF )

Speaking of smaller models obliterating huge ones, Tongyi released a bunch of papers and a model this week that can do Deep Research on the level of OpenAI, even beating it, with a Qwen Finetune with only 3B active parameters!

With insane scores like 32.9 (38.3 in Heavy mode) on Humanity's Last Exam (OAI Deep Research gets 26%) and an insane 98.6% on SimpleQA, this innovative approach uses a lot of RL and synthetic data to train a Qwen model to find what you need. The paper is full of incredible insights into how to build automated RL environments to get to this level.

AI Art, Diffusion 3D and Video

This category of AI has been blowing up, we've seen SOTA week after week with Nano Banana then Seedream 4 and now a few more insane models.

Tencent's Hunyuan released SRPO (X, HF, Project, Comparison X)(Semantic Relative Preference Optimization) which is a new method to finetune diffusion models quickly without breaking the bank. Also released a very realistic looking finetune trained with SRPO. Some of the generated results are super realistic, but it's more than just a model, there's a whole new method of finetuning here!

Hunyuan also updated their 3D model and announced a full blown 3D studio that does everything from 3D object generation, meshing, texture editing & more.

Reve launches a 4-in-1 AI visual platform taking on Nano 🍌 and Seedream (X, Reve, Blog)

A newcomer, Reve has launched a comprehensive new AI visual platform bundling image creation, editing, remixing, creative assistant, and API integration, all aimed at making advanced editing as accessible, all using their own proprietary models.

What stood out to me though, is the image editing UI, which allows you to select on your image exactly what you want to edit, write a specific prompt for that thing (change color, objects, add text etc') and then hit generate and their model takes into account all those new queues! This is way better than just ... text prompting the other models!

Ray3: Luma’s “reasoning” video model with native HDR, Draft Mode, and Hi‑Fi mastering (X, Try It)

Luma released the third iteration of their video model called Ray, and this one does.. HDR! But it also has Draft Mode (for quick iteration), first/last frame interpolation, and they claim to be "production ready" with extreme prompt adherence.

The thing that struck me is the reasoning part, their video model is now reasoning, to let you create more complex scenes, while the model will ... evaluate itself and select the best generation for you! This is quite bonkers, can't wait to play with it!

World models are getting closer - Worldlabs announced Marble (Demo)

We've covered a whole host of world models, Genie3, Hunyuan 3D world models, Mirage and a bunch more!

Dr FeiFei's WorldLabs have been one of the first ones to tackle the world model concept and their recent release shows incredible progress (and finally lets us play with it!)

Marble takes images and creates Gussian Splats, that can be used in 3D environments. So now you can use any AI image generation and turn it into a walkable 3D world!

Google puts Gemini in Chrome (X, Blog)

This happened after the show today and while not fully rolled out yet, I've told you before when we covered Comet from PPXL and Dia from browser company, that Google will not be far behind!

So today they announced that Gemini is coming to Chrome, and will allow users to chat with a bunch of their tabs, summarize across tabs and soon do agentic tasks like clicking things and shopping for you? 😅

I wonder if this means that Google will offer this for free to the over 1B chrome users or introduce some sort of Gemini tier cross-over? Remains to be seen but very exciting to see AI browsers all over!

The best feature could be a hidden one, where the Gemini in Chrome will have knowledge about your surfing history and you'll be able to ask it about that one website you visited a while ago that had sharks!

Folks, I can go on and on today, literally there's a new innovative video model from ByteDance, a few more image models, but alas, I have to prioritize and give you only the top important news. So, I'll just remind that I put all the links in the TL;DR below and that you should absolutely check out the video version of our show on YT because a lot of visual things are happening and we're playing with all of them live!

Hey, just before you get to the “links”, consider subscribing to help me keep this going? 🙏

See you next week 🫡 Don't forget to subscribe (and if you already subbed, share this with a friend or two?)

TL;DR and show notes - September 18, 2025

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @ldjconfirmed @nisten

* Guest : Jeremy Berman (@jerber888) - SOTA on ARC- AGI

* Open Source

* Perceptron AI introduces Isaac 0.1: a 2B param perceptive-language model (X, HF, Blog)

* Tongyi DeepResearch: A3B open-source web agent claims parity with OpenAI Deep Research (X, HF)

* Mistral updates Magistral-Small-2509 (HF)

* Big CO LLMs + APIs

* GPT-5-Codex release: Agentic coding upgrade for Codex (X, OpenAI Blog)

* Meta Connect - New AI glasses with display, new AI mode (X Recap)

* NBER & OpenAI - How People Use ChatGPT: Growth, Demographics, and Scale (X, Blog, NBER Paper)

* ARC-AGI: New SOTA by Jeremy Berman and Eric Pang using Grok-4 (X, Blog)

* OpenAI’s reasoning system aces 2025 ICPC World Finals with a perfect 12/12 (X)

* OpenAI adds thinking budgets to ChatGPT app (X)

* Gemini in Chrome: AI assistant across tabs + smarter omnibox + safer browsing (X, Blog)

* Anthropic admits Claude bugs - Detailed analysis

* This weeks Buzz

* W&B Models + Weave! You can now log your RL runs in W&B Weave 👏 (X, W&B Link)

* W&B Fully Connected London - tickets are running out! Use FCLNTHURSAI for a free ticket on me! (Register Here)

* Vision & Video

* Moondream 3 (Preview): 9B MoE VLM with 2B active targets frontier-level visual reasoning (X, HF)

* Ray3: Luma’s “reasoning” video model with native HDR, Draft Mode, and Hi‑Fi mastering (X)

* HuMo: human‑centric, multimodal video gen from ByteDance/Tsinghua (X, HF)

* Voice & Audio

* Reka Speech: high-throughput multilingual ASR and speech translation for batch-scale pipelines (X, Blog)

* AI Art & Diffusion & 3D

* Hunyuan SRPO (Semantic Relative Preference Optimization) supercharges diffusion models (X, HF, Project, Comparison X)

* Hunyuan 3D 3.0 (X, Try it)

* FeiFei WorldLabs presents Marble (Demo)

* Reve launches 4-in-1 AI visual platform (X, Reve, Blog)

* Tools

* Chrome adds Gemini (Blog)

The main thing you need to know this week is likely the incredible Image model that ByteDance released, that overshoots the (incredible image model from last 2 weeks) nano 🍌. ByteDance really outdid themselves on this one!

But also, a video model with super fast generation, OpenAI rumor made Larry Ellison the richest man alive, ChatGPT gets MCP powers (under a flag you can enable) and much more!

This week we covered a lot of visual stuff, so while the podcast format is good enough, it's really worth tuning in to the video recording to really enjoy the full show.

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

AI Art and Diffusion

It's rare for me to start the newsletter not on Open Source AI news, but hey, at least this way you know that I'm writing it and not some AI right? 😉

ByteDance SeeDream 4 - 4K SOTA image generation and editing model with up to 6 reference images (Fal, Replicate)

The level of detail on ByteDance's new model, has really made all the hosts on ThursdAI stop and go... huh? is this AI? Bytedance really outdid themselves with this image model that not only generates images, it also is a fully functional image editing with natural language model. It's a diffusion transformer, able to generate 2K and 4K images, fast (under 5 seconds?) while enabling up to 6 reference images to be provided for the generation.

This is going to be incredible for all kinds of purposes, AI art, marketing etc'. The promt adherence is quite incredible, text is also crisp and sharp at those 2/4K resolutions. We created this image live on the show with it (using a prompt extended by another model)

I then provided my black and white headshot and the above image and asked to replace me as a cartoon character, and it did, super quick, and even got my bomber jacket and the W&B logo on it in there! Notable, nothing else was changed in the image, showing just how incredible this one is for image editing.

In you want enhanced realism, our friend FoFr from replicate, reminded us that using IMG_3984.CR2 in the prompt, will make the model show images that are closer to reality, even if they depict some incredibly unrealistic things, like a pack of lions forming his nickname

Additional uses for this model are just getting discovered, and one user already noted that given this model outputs 4K resolution, it can be used as a creative upscaler for other model outputs. Just shove your photo from another AI in Seedream and ask for an upscale. Just be ware that creative upscalers change some amount of details in the generated picture.

DecART AI Lucy 14B Redefines Video Generation speeds!

If Seedteam blew my mind with images, Decart's Lucy 14B absolutely shattered my expectations for video generation speed. We're talking about generating 5-second videos from images in 6.5 seconds. That's almost faster than watching the video itself!

This video model is not open source yet (despite them adding 14B to the name) but it's smaller 5B brother was open sourced. The speed to quality ratio is really insane here, and while Lucy will not generate or animate text or faces that well, it does produce some decent imagery, but SUPER fast. This is really great for iteration, as AI Video is like a roulette machine, you have to generate a lot of tries to see a good result.

This paired with Seedream (which is also really fast) are a game changer in the AI Art world! So stoked to see what folks will be creating with these!

Bonus Round: Decart's Real-Time Minecraft Mod for Oasis 2 (X)

The same team behind Lucy also dropped Oasis 2.0, a Minecraft mod that generates game environments in real-time using diffusion models. I got to play around with it live, and watching Minecraft transform into different themed worlds as I moved through them was surreal.

Want a steampunk village? Just type it in. Futuristic city? Done. The frame rate stayed impressively smooth, and the visual coherence as I moved through the world was remarkable. It's like having an AI art director that can completely reskin your game environment on demand. And while the current quality remains low res, if you consider where Stable Diffusion 1.4 was 3 years ago, and where Seedream 4 is now, and do the same extrapolation to Oasis, in 2-3 years we'll be reskinning whole games on the fly and every pixel will be generated (like Jensen loves to say!)

OpenAI adds full MCP to ChatGPT (under a flag)

This is huge, folks. I've been waiting for this for a while, and finally, OpenAI quietly added full MCP (Model Context Protocol) support to ChatGPT via a hidden "developer mode."

How to Enable MCP in ChatGPT

Here's the quick setup I showed during the stream:

* Go to ChatGPT settings → Connectors

* Scroll down to find "Developer Mode" and enable it

* Add MCP servers (I used Rube.ai from Composio)

* Use GPT-4o in developer mode to access your connectors

During the show, I literally had ChatGPT pull Nisten's last five tweets using the Twitter MCP connector. It worked flawlessly (though Nisten was a bit concerned about what tweets it might surface 😂).

The implications are massive - you can now connect ChatGPT to GitHub, databases, your local files, or chain multiple tools together for complex workflows. As Wolfram pointed out though, watch your context usage - each MCP connector eats into that 200K limit.

Big Moves: Investments and Infrastructure

Speaking of OpenAI, Let's talk money, because the stakes are getting astronomical. OpenAI reportedly has a $300 billion (!) deal with Oracle for compute infrastructure over five years, starting in 2027. That's not a typo - $60 billion per year for compute. Larry Ellison just became the world's richest person, and Oracle's stock shot up 40% on the news in just a few days! This has got to be one of the biggest compute deals the world has ever head of!

The scale is hard to comprehend. We're talking about potentially millions of H100 GPUs worth of compute power. When you consider that most AI companies are still figuring out how to profitably deploy thousands of GPUs, this deal represents infrastructure investment at a completely different magnitude.

Meanwhile, Mistral just became Europe's newest decacorn, valued at $13.8 billion after receiving $1.3 billion from ASML. For context, ASML makes the lithography machines that TSMC uses to manufacture chips for Nvidia. They're literally at the beginning of the AI chip supply chain, and now they're investing heavily in Europe's answer to OpenAI.

Wolfram made a great point - we're seeing the emergence of three major AI poles: American companies (OpenAI, Anthropic), Chinese labs (Qwen, Kimi, Ernie), and now European players like Mistral. Each is developing distinct approaches and capabilities, and the competition is driving incredible innovation.

Anthropic's Mea Culpa and Code Interpreter

After weeks of users complaining about Claude's degraded performance, Anthropic finally admitted there were bugs affecting both Claude Opus and Sonnet. Nisten, who tracks these things closely, speculated that the issues might be related to running different quantization schemes on different hardware during peak usage times. We already reported last week that they admitted that "something was affecting intelligence" but this week they said they pinpointed (and fixed) 2 bugs realted to inference!

They also launched a code interpreter feature that lets Claude create and edit files directly. It's essentially their answer to ChatGPT's code interpreter - giving Claude its own computer to work with. The demo showed it creating Excel files, PDFs, and documents with complex calculations. Having watched Claude struggle with file operations for months, this is a welcome addition.

🐝 This Week's Buzz: GLM 4.5 on W&B and We're on Open Router!

Over at Weights & Biases, we've got some exciting updates for you. First, we've added Zhipu AI's GLM 4.5 to W&B Inference! This 300B+ parameter model is an absolute beast for coding and tool use, ranking among the top open models on benchmarks like SWE-bench. We've heard from so many of you, including Nisten, about how great this model is, so we're thrilled to host it. You can try it out now and get $2 in free credits to start.

And for all you developers out there, you can use a proxy like LiteLLM to run GLM 4.5 from our inference endpoint inside Anthropic's Claude Code if you're looking for a powerful and cheap alternative!

Second, we're now on Open Router! You can find several of our hosted models, like GPT-4-OSS and DeepSeek Coder, on the platform. If you're already using Open Router to manage your model calls, you can now easily route traffic to our high-performance inference stack.

Open Source Continues to Shine

Open Source LLM models took a bit of a break this week, but there were still interesting models!

Baidu released ERNIE-4.5, a very efficient 21B parameter "thinking" MoE that only uses 3B active parameters per token. From the UAE, MBZUAI released K2-Think, a finetune of Qwen 2.5 that's showing some seriously impressive math scores. And Moonshot AI updated Kimi K2, doubling its context window to 256K and further improving its already excellent tool use and writing capabilities.

Tencent released an update to HunyuanImage 2.1, which is a bit slow, but also generates 2K images and is decent at text.

Qwen drops Qwen3-Next-80B-A3B (X, HF)

In breaking news post the show (we were expecting this on the show itself), Alibaba folks dropped a much more streamlined version of the next Qwen, 80B parametes with only 3B active! They call this an "Ultra Sparse MOE" and it beats Qwen3-32B in perf, rivals Qwen3-235B in reasoning & long-context.

This is quite unprecedented, as getting models as sparse as to work well takes a lot of effort and skill, but the Qwen folks delivered!

Tools

We wrapped with a quick shoutout to EBSynth, a nifty video editor that lets you draw or add elements to one frame and extrapolates to the rest—like Photoshop for motion. It's browser-based and fun for VFX tweaks; check the demo video on X. Simple but powerful for quick video hacks. Speaking of Photoshop, it was confirmed that Nano Banana is going to be embedded into Photoshop for image editing!

Summary & TL;DR

What a week—Seedream and Lucy alone have me rethinking how fast AI can iterate creatively, while MCP in ChatGPT feels like the dawn of truly accessible agents. With open-source keeping pace and big deals fueling the fire, AI's multimodal future is accelerating. Thanks for tuning in, folks; if you missed the live vibes, catch the podcast or hit sub.thursdai.news for all the links. See you next Thursday—what blew your mind this week? Drop a comment and share with a friend, it's the best way to support this endeavor!

TL;DR of all topics covered:

AI Models & APIs:

* ChatGPT adds full MCP support - Developer mode unlocks tool connectors for 400M+ users (Setup Guide)

* Sea Dream 4.0 - ByteDance's unified image generation/editing model creates 4K images in ~1.8 seconds (X, Try it)

* Lucy 14B - Decart's lightning-fast video model generates 5-second clips in 6.5 seconds (Demo, Page)

* Claude bug fixes - Anthropic admits to performance issues and releases code interpreter (Blog)

* Sonoma Dusk & Sky - Mystery models on OpenRouter with 2M context, rumored to be Grok (OpenRouter)

This Week's W&B Buzz:

* OpenRouter integration - Serving models to broader developer community (Try us)

* GLM 4.5 - 350B parameter coding model added to inference (X, Try It)

* W&B inference in Claude Code with LiteLLM (Olaf's Guide)

Open Source Releases:

* ERNIE 4.5 - Baidu open-sources 21B parameter thinking model with 3B active parameters (X, HF)

* K2-think - MBZUAI's Qwen 2.5 fine-tune with strong math performance (X)

* Kimi K2 update - Doubled context to 256K, improved tool use (X)

* HunyuanImage 2.1 - Tencent's 17B parameter open-source 2K image model (X, HF)

* Qwen-next-80B-A3B - Alibaba's next frontier MoE with 3B active param (X, HF)

Voice & Audio:

* Qwen3-ASR-Flash - 11-language speech recognition with singing support (X)

* Stable Audio 2.5 - Enterprise audio generator creating 3-minute tracks in <2 seconds (X, Blog, Try It)

* ElevenLabs Voice Remixing - Modify cloned voices for age, gender, accent (X)

Business & Investment:

* OpenAI-Oracle deal - $300B infrastructure agreement over 5 years

* Mistral funding - $1.3B investment from ASML at $13.8B valuation (Blog)

Tools:

* Oasis 2.0 - Real-time Minecraft world generation mod from Decart (Try It)

* EbSynth - Video editing tool for frame-by-frame manipulation (X)

Hosts:

* Alex Volkov (@altryne)

* Wolfram RavenWlf (@WolframRvnwlf)

* Yam Peleg (@yampeleg)

* Nisten (@nisten)

* LDJ (@ldjconfirmed)

I'm back from the desert (pic at the end) and what a great feeling it is to be back in the studio to talk about everything that happened in AI!

It's been a pretty full week (or two) in AI, with Coding agent space heating up, Grok entering the ring and taking over free tokens, Codex 10xing usage and Anthropic... well, we'll get to Anthropic.

Today on the show we had Roger and Bhavesh from Nous Research cover the awesome Hermes 4 release and the new PokerBots benchmark, then we had a returning favorite, Kwindla Hultman Kramer, to talk about the GA of RealTime voice from OpenAI.

Plus we got some massive funding news, some drama with model quality on Claude Code, and some very exciting news right here from CoreWeave aquiring OpenPipe! 👏 So grab your beverage of choice, settle in (or skip to the part that interests you) and let's take a look at the last week (or two) in AI!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source: Soulful Models and Poker-Playing Agents

This week did not disappoint as it comes to Open Source! Our friends at Nous Research released the 14B version of Hermes 4, after releasing the 405B and 70B versions last week. This company continues to excel in finetuning models for powerful, and sometimes just plain weird (in a good way) usecases.

Nous Hermes 4 (14B, 70B, 405B) and the Quest for a "Model Soul" (X, HF)

Roger and Bhavash from Nous came to announce the release of the smaller (14B) version of Hermes 4, and cover the last weeks releases of the larger 70B and 405B brothers. Hermes series of finetunes was always on our radar, as unique data mixes turned them into uncensored, valuable and creative models and unlocked a bunch of new use-cases.

But the wildest part? They told us they intentionally stopped training the model not when reasoning benchmarks plateaued, but when they felt it started to "lose its model soul." They monitor the entropy and chaos in the model's chain-of-thought, and when it became too sterile and predictable, they hit the brakes to preserve that creative spark. This focus on qualities beyond raw benchmark scores is why Hermes 4 is showing some really interesting generalization, performing exceptionally well on benchmarks like EQBench3, which tests emotional and interpersonal understanding. It's a model that's primed for RL not just in math and code, but in creative writing, role-play, and deeper, more "awaken" conversations. It’s a soulful model that's just fun to talk to.

Nous Husky Hold'em Bench: Can Your LLM Win at Poker? (Bench)

As if a soulful model wasn't enough, the Nous team also dropped one of the most creative new evals I've seen in a while: Husky Hold'em Bench. We had Bhavesh, one of its creators, join the show to explain. This isn't a benchmark where the LLM plays poker directly. Instead, the LLM has to write a Python poker botfrom scratch, under time and memory constraints, which then competes against bots written by other LLMs in a high-stakes tournament. Very interesting approach, and we love creative benchmarking here at ThursdAI!

This is a brilliant way to test for true strategic reasoning and planning, not just pattern matching. It's an "evergreen" benchmark that gets harder as the models get better. Early results are fascinating: Claude 4 Sonnet and Opus are currently leading the pack, but Hermes 4 is the top open-source model.

More Open Source Goodness

The hits just kept on coming this week. Tencent open-sourced Hunyuan-MT-7B, a translation model that swept the WMT2025 competition and rivals GPT-4.1 on some benchmarks. Having a small, powerful, specialized model like this is huge for anyone doing large-scale data translation for training or needing fast on-device capabilities.

From Switzerland, we got Apertus-8B and 70B, a set of fully open (Apache 2.0 license, open data, open training recipes!) multilingual models trained on a massive 15 trillion tokens across 1,800 languages. It’s fantastic to see this level of transparency and contribution from European institutions.

And Alibaba’s Tongyi Lab released WebWatcher, a powerful multimodal research agent that can plan steps, use a suite of tools (web search, OCR, code interpreter), and is setting new state-of-the-art results on tough visual-language benchmarks, often beating models like GPT-4o and Gemini.

All links are in the TL;DR at the end

BREAKING NEWS: Google Drops Embedding Gemma 308M (X, HF, Try It)

Just as we were live on the show, news broke from our friends at Google. They've released Embedding Gemma, a new family of open-source embedding models. This is a big deal because they are tiny—the smallest is only 300M parameters and takes just 200MB to run—but they are topping the MTEB leaderboard for models under 500M parameters. For anyone building RAG pipelines, especially for on-device or mobile-first applications, having a small, fast, SOTA embedding model like this is a game-changer.

It's so optimized for on device running that it can run fully in your browser on WebGPU, with this great example from our friend Xenova highlighted on the release blog!

Big Companies, Big Money, and Big Problems

It was a rollercoaster week for the big labs, with massive fundraising, major product releases, and a bit of a reality check on the reliability of their services.

OpenAI's GPT Real-Time Goes GA and gets an upgraded brain (X, Docs)

We had the perfect guest to break down OpenAI's latest voice offering: Kwindla Kramer, founder of Daily and maintainer of the open-source PipeCat framework. OpenAI has officially taken its Realtime API to General Availability (GA), centered around the new gpt-realtime model.

Kwindla explained that this is a true speech-to-speech model, not a pipeline of separate speech-to-text, LLM, and text-to-speech models. This reduces latency and preserves more nuance and prosody. The GA release comes with huge upgrades, including support for remote MCP servers, the ability to process image inputs during a conversation, and—critically for enterprise—native SIP integration for connecting directly to phone systems.

However, Kwindla also gave us a dose of reality. While this is the future, for many high-stakes enterprise use cases, the multi-model pipeline approach is still more reliable. Observability is a major issue with the single-model black box; it's hard to know exactly what the model "heard." And in terms of raw instruction-following and accuracy, a specialized pipeline can still outperform the speech-to-speech model. It’s a classic jagged frontier: for the lowest latency and most natural vibe, GPT Real-Time is amazing. For mission-critical reliability, the old way might still be the right way for now.

ChatGpt has branching!

Just as I was about to finish writing this up, ChatGPT announced a new feature, and this one I had to tell you about! Finally you can branch chats in their interface, which is a highly requested feature!

Branching seems to be live on the chat interface, and honestly, tiny but important UI changes like these are how OpenAI remains the best chat experience!

The Money Printer Goes Brrrr: Anthropic's $13B Raise

Let's talk about the money. Anthropic announced it has raised an absolutely staggering $13 billion in a Series F round, valuing the company at $183 billion. Their revenue growth is just off the charts, jumping from a run rate of around $1 billion at the start of the year to over $5 billion by August. This growth is heavily driven by enterprise adoption and the massive success of Claude Code. It's clear that the AI gold rush is far from over, and investors are betting big on the major players. In related news, OpenAI is also reportedly raising $10 billion at a valuation of around $500 billion, primarily to allow employees to sell shares—a huge moment for the folks who have been building there for years.

Oops... Did We Nerf Your AI? Anthropic's Apology

While Anthropic was celebrating its fundraise, it was also dealing with a self-inflicted wound. After days of users on X and other forums complaining that Claude Opus felt "dumber," the company finally issued a statement admitting that yes, for about three days, the model's quality was degraded due to a change in their infrastructure stack.

Honestly, this is not okay. We're at a point where hundreds of thousands of developers and businesses rely on these models as critical tools. To have the quality of that tool change under your feet without any warning is a huge problem. It messes with people's ability to do their jobs and trust the platform. While it was likely an honest mistake in pursuit of efficiency, it highlights a fundamental issue with closed, proprietary models. You're at the mercy of the provider. It's a powerful argument for the stability and control that comes with open-source and self-hosted models. These companies need to realize that they are no longer just providing experimental toys; they're providing essential infrastructure, and that comes with a responsibility for stability and transparency.

This Week's Buzz: CoreWeave Acquires OpenPipe! 🎉

Super exciting news from the Weights & Biases and CoreWeave family - we've acquired OpenPipe! Kyle and David Corbett and their team are joining us to help build out the complete AI infrastructure stack from metal to model.

OpenPipe has been doing incredible work on SFT and RL workflows with their open source ART framework. As Yam showed during the show, they demonstrated you can train a model to SOTA performance on deep research tasks for just $300 in a few hours - and it's all automated! The system can generate synthetic data, apply RLHF, and evaluate against any benchmark you specify.

This fits perfectly into our vision at CoreWeave - bare metal infrastructure, training and observability with Weights & Biases, fine-tuning and RL with OpenPipe's tools, evaluation with Weave, and inference to serve it all. We're building the complete platform, and I couldn't be more excited!

Vision & Speed: Apple's FastVLM (HF)

Just before Apple's event next week, they dropped FastVLM - a speed-first vision model that's 85x faster on time-to-first-token than comparable models. They released it in three sizes (7B, 1.5B, and 0.5B), all optimized for on-device use.

The demo that blew my mind was real-time video captioning running in WebGPU. HF CEO Clem showed it processing Apple's keynote video with maybe 250ms latency - the captions were describing what was happening almost in real-time. When someone complained it wasn't accurate because it described "an older man in headphones" when showing an F1 car, Clem pointed out that was actually the previous frame showing Tim Cook - the model was just slightly behind!

Tools Showdown: Codex vs Claude Code

To wrap up, we dove into the heated debate between Codex and Claude Code. Sam Altman reported that Codex usage is up 10x in the past two weeks (!) and improvements are coming. Yam gave us a live demo, and while Claude Code failed to even start up during the show (highlighting why people are switching), Codex with GPT-5 was smooth as butter.

The key advantages? Codex authenticates with your OpenAI account (no API key juggling), it has MCP support, and perhaps most importantly - it's not just a CLI tool. You can use it for PR reviews on GitHub, as a cloud-based agent, and integrated into Cursor and Windsurf. Though as Yam pointed out, OpenAI really needs to stop calling everything "Codex" - there are like five different products with that name now! 😅

If you're tried Codex (the CLI!) when it was released, and gave up, give it a try now, it's significantly upgraded!

Ok, phew, what a great episode we had, if you're only reading, I strongly recommend checking out the live recording or the edited podcast, and of course, if this newsletter is helpful to you, the best way you can do to support it is to subscribe, and share with friends 👏

P.S - Just came back after my first burning man, it was a challenging, all consuming experience, where I truly disconnected for the first time (first ThursdAI in over 2 years that I didn't know what's going on with AI). It was really fun but I'm happy to =be back! See you next week!

TL;DR and Show Notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

* Guests - Roger Jin - @rogershijin & Bhavesh Kumar @bha_ku21

* Kwindla Kramer - @kwindla

* Open Source LLMs

* Nous Hermes 4 — 14B launches: compact hybrid reasoning model with tool calling for local and cloud use (X, HF, Tech Report)

* Tencent open-sources Hunyuan-MT-7B translation model after sweeping WMT2025 (X, HF)

* Nous - Husky Hold'em Bench launches as an open-source pokerbot eval for LLM strategic play (X, Bench)

* WebWatcher: Alibaba's Tongyi Lab open-sources a vision-language deep research agent that sets new SOTA (X, HF)

* Apertus-8B and 70B launch as Switzerland's fully open, multilingual LLMs trained on 15T tokens across 1,800+ languages (X, HF)

* Google releases Embedding Gemma - 300M param SOTA embeddings model for RAG ([Breaking News])

* Big CO LLMs + APIs

* Mistral adds 20+ MCP-powered connectors and controllable Memories to Le Chat for enterprise workflows (X, Blog)

* Anthropic raises $13B Series F at a $183B post-money valuation (X, Blog)

* OpenAI fundraises $10B at ~$500B valuation - buyback for employees

* OpenAI ships gpt-realtime and takes Realtime API to GA with remote MCP tools, image input, and SIP phone calling (X)

* OpenAI releases projects for free users with larger file uploads and project-only memory controls

* OpenAI acquires Statsig & Alex for $1.1B+ to strengthen applications team

* Grok Code 1 - now taking 50% of coding traffic on OpenRouter

* Codex usage up 10x in 2 weeks per Sam Altman, with improvements coming

* Anthropic admits to Claude Opus quality degradation for 3 days due to infrastructure changes

* This weeks Buzz

* CoreWeave buys OpenPipe! 🎉 (Blog)

* Vision & Video

* Apple's FastVLM-7B lands with speed-first vision encoder—85x faster TTFT vs peers (X, HF)

* AI Art & Diffusion & 3D

* Nano Banana (Imagen 3) continues to dominate as Google's best image model (ai.studio/banana)

* Tools

* Codex vs Claude Code discussion → Codex now significantly better with GPT-5 engine, GitHub PR reviews, and cloud agents

This week looked quiet… until about 15 hours before we went live. Then the floodgates opened: DeepSeek dropped a hybrid V3.1 that beats their own R1 with fewer thinking tokens, ByteDance quietly shipped a 36B Apache-2.0 long-context family with a “thinking budget” knob, NVIDIA pushed a faster mixed-architecture 9B with open training data, and a stealth image editor dubbed “Nano Banana” started doing mind-bending scene edits that feel like a new tier of 3D-aware control.

On the big-co side, a mystery “Sonic” model appeared in Cursor and Cline (spoiler: the function call paths say a lot), and OpenAI introduced Agents.md to stop the config-file explosion in agentic dev tools. We also got a new open desktop-agent RL framework that 4x’d OSWorld SOTA, an IBM + NASA model for solar weather, and Qwen’s fully open 20B image editor that’s shockingly capable and runnable on your own GPU.

Our show today was one of the shortest yet, as I had to drop early to prepare for Burning Man 🔥🕺 Speaking of which, Wolfram and the team will host the next episode!

Ok, let's dive in!

DeepSeek V3.1: a faster hybrid that thinks less, scores more (X, HF)

DeepSeek does this thing where they let a base artifact “leak” onto Hugging Face, and the rumor mill goes into overdrive. Then, hours before we went live, the full V3.1 model card and an instruct variant dropped. The headline: it’s a hybrid reasoner that combines the strengths of their V3 (fast, non-thinking) and R1 (deep, RL-trained thinking), and on many tasks it hits R1-level scores with fewer thinking tokens. In human terms: you get similar or better quality, faster.

A few things I want to call out from the release and early testing:

* Hybrid reasoning mode done right. The model can plan with thinking tokens and then switch to non-thinking execution, so you don’t have to orchestrate two separate models. This alone simplifies agent frameworks: plan with thinking on, execute with thinking off.

* Thinking efficiency is real. DeepSeek shows curves where V3.1 reaches or surpasses R1 with significantly fewer thinking tokens. On AIME’25, for example, R1 clocks 87.5% with ~22k thinking tokens; V3.1 hits ~88.4 with ~15k. On GPQA Diamond, V3.1 basically matches R1 with roughly half the thinking budget.

* Tool-use and search-agent improvements. V3.1 puts tool calls inside the thinking process, instead of doing a monologue and only then calling tools. That’s the pattern you want for multi-turn research agents that iteratively query the web or your internal search.

* Long-context training was scaled up hard. DeepSeek says they increased the 32K extension phase to ~630B tokens, and the 128K phase to ~209B tokens. That’s a big bet on long-context quality at train time, not just inference-time RoPE tricks. The config shows a max position in the 160K range, with folks consistently running it in the 128K class.

* Benchmarks show the coding and terminal agent work got a big push. TerminalBench jumps from a painful 5.7 (R1) to 31 with V3.1. Codeforces ratings are up. On SweBench Verified (non-thinking), V3.1 posts 66 vs R1’s ~44. And you feel it: it’s faster to “get to it” without noodling forever.

* API parity you’ll actually use. Their API now supports the Anthropic-style interface as well, which means a bunch of editor integrations “just work” with minimal glue. If you’re in a Claude-first workflow, you won’t have to rewire the world to try V3.1.

* License and availability. This release is MIT-licensed, and you can grab the base model on Hugging Face. If you prefer hosted, keep an eye on our inference—we’re working to get V3.1 live so you can benchmark without burning your weekend assembling a serving stack.

Hugging Face: https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base

Quick personal note: I’m seeing a lot of small, pragmatic improvements add up here. If you’re building agents, the hybrid mode plus tighter tool integration is a gift. DeepSeek V3.1 is going to be deployed to W&B Inference service soon! Take a look here to see when it's ready wandb.me/inference

ByteDance Seed-OSS 36B: Apache-2.0, 512K context, and a “thinking budget” knob (X, HF, Github)

I didn’t see much chatter about this one, which is a shame because this seems like a serious release. ByteDance’s Seed team open-sourced a trio of 36B dense models—two Base variants (with and without synthetic data) and an Instruct model—under Apache-2.0, trained on 12T tokens and built for long-context and agentic use. The context window is a native half-million tokens, and they include a “thinking budget” control you can set in 512-token increments so you can trade depth for speed.

They report strong general performance, long-context RULER scores, and solid code/math numbers for a sub-40B model, with the Instruct variant posting very competitive MMLU/MMLU-Pro and LiveCodeBench results. The architecture is a straightforward dense stack (not MoE), and the models ship with Transformers/vLLM support and 4/8-bit quantization ready to go. If you’ve been hunting for a commercial-friendly, long-context 30-something‑B with an explicit reasoning-control dial, this should be on your shortlist.

A neat detail for the training nerds: two Base releases—one trained with synthetic data, one without—make for a rare apples-to-apples study in how synthetic data shapes base capability. Also worth noting: they previously shipped a Seed-Prover specialized for Lean; it looks like the team is interested in tight domain models and generalists.

NVIDIA Nemotron Nano 9B V2: mixed architecture, open data, and long-context throughput (X, Blog, HF, Dataset, Try It)

NVIDIA shipped a fully open release of Nemotron Nano 9B V2—base, base-before-alignment/pruning, and a realigned reasoning model—and, crucially, they published most of the pretraining dataset details (~6.6T tokens across premium web, math, code, and SFT). That level of data transparency is rare and makes this a great base for fine-tuners who want reproducibility.

Under the hood, this is a mixed Mamba+Transformer architecture. NVIDIA is claiming up to 6x higher throughput versus a pure-Transformer peer (they compare to Qwen3-8B) and specifically highlight that they pruned a 12B down to 9B while preserving quality. They also note a single A10 can handle 128K context after compression and distillation passes, which is the kind of practical systems work that matters when you’re running fleets.

A couple of caveats. The license is NVIDIA Open Model License—not Apache-2.0—so read it; it includes restrictions around illegal surveillance and safety bypasses and has revocation clauses. Personally, I appreciate the data openness and the long-context engineering; as always, just make sure the license fits your use case.

If you’re into longer-context math/coding with small models, the numbers (AIME’25, RULER-128K, GPQA) are impressive for 9B. And if you fine-tune: the availability of both pruned and pre-pruned bases plus the dataset recipe is a rare treat.

Cohere’s Command-A Reasoning: dense, multilingual, and research-only licensing (X, Blog, HF)

Cohere Dropped a new reasoning model focused on enterprise deployment patterns. It’s dense 111B model, supports a 256K context, and includes very strong multilingual coverage (23 languages is what they called out). What caught my eye: on the BFCL (Berkeley Function-Calling Leaderboard) they show 70%—above DeepSeek R1’s ~63% and GPT-OSS’s ~61%—and they plot the now-familiar test-time compute curves where more thinking tokens yield higher scores.

This release uses Cohere’s non-commercial research license; if you want commercial usage you’ll need to go through them. That said, for teams who need privately deployable, on-prem reasoning and can work under a research license for prototyping, it’s a serious option. A meta observation from the show: there’s accumulating evidence that more active parameters help multi-hop tool-use chains compared to very sparse MoE at similar effective capacity. This model nudges in that direction.

Desktop agents leap: ComputerRL hits 48% on OSWorld (Paper)

A new framework dubbed ComputerRL from Z.ai and folks at Tsingua Uni, unified API calls with GUI actions and scaled RL across fleets of virtual desktops, posting a 48.1% success rate on OSWorld versus ~12% for earlier open models. The training system spins up thousands of qemu-in-docker VMs via gRPC; the learning loop alternates RL with supervised fine-tuning and uses a clean step-level binary reward to simplify credit assignment. If you care about practical desktop automation across Ubuntu/Windows/macOS, this is a big jump.

IBM + NASA’s Surya: open model for solar weather (HF)

Scientists get some love: IBM and NASA open-sourced Surya, a transformer trained on nine years of multi-instrument observations (nearly 200 TB) to forecast solar dynamics and space weather—the stuff that can knock satellites and power grids sideways. It’s on Hugging Face, it’s actually runnable, and it’s a fantastic example of open models delivering real-world scientific utility.

Smaller but notable: InternLM and OpenCUA, plus Intel’s quants

Two quick flags from the “worth your time” pile. InternLM shipped S1 Mini, an 8B vision+language model (ViT on top) that’s multimodal and lightweight; if you need on-device omni-ish behavior on a laptop or tablet, give it a look. And OpenCUA 32B (Qwen-based) is a specialized computer-usage agent with strong scores; if you’re building automations that need native OS control, it’s worth benchmarking.

Also, if you’re running 4-bit: the Intel quantization work is excellent right now. Their 4-bit quants have been extremely high precision in my testing, especially for large coders and reasoners like DeepSeek V3.1. It’s an easy win if you’re trying to squeeze a 30B+ onto a workstation without hemorrhaging quality.

Big-co updates and platform shifts

Sonic appears in Cursor and Cline

If you open Cursor or fire up Cline, you may see a new “Sonic” model toggle. It’s labeled as a reasoning model and, when you poke the function-calling internals, the call paths include “xai/…” strings. Folks report it’s fast and solid for coding. No official docs yet, but I’d be surprised if this isn’t Grok Code in pre-release clothes.

Agents.md: one file to rule your agents

Agentic dev stacks have multiplied config files like gremlins: Cursor’s rules.json, Windsurf’s prompts, MCP server manifests, tool schemas, install scripts… and every tool wants a different filename and format. OpenAI’s Agents.md is a strong attempt at standardization. It’s just Markdown at repo root that says, “here’s how to set up, build, test, and run this project,” plus any agent-specific caveats. Tools then auto-detect and follow your instructions instead of guessing.

It’s already supported by OpenAI Codex, Amp, Jules, Cursor, RooCode, and more, with tens of thousands of public repos adopting the pattern. In monorepos, the nearest Agents.md wins, so you can override at the package level. And human chat instructions still override the file’s guidance, which is the right default.

GPT‑5 context truncation in the web UI (reports)

There’s been a wave of reports that GPT‑5 in the web UI is truncating long prompts even when you’re under the documented context limit. The folks at Repo Prompt reproduced this multiple times and got confirmation from OpenAI that it’s a bug (not a deliberate nerf). If you saw GPT‑5 suddenly forget the bottom half of your carefully structured system prompt in the web app, this likely explains it. The API doesn’t seem affected. Fingers crossed for a quick fix—GPT‑5 is still the best model I’ve used for 300k‑token “read the whole repo and propose a plan” tasks.

Image and 3D: Nano Banana and Qwen’s open image editor

Nano Banana: 3D-consistent scene editing from thin air

A stealth model nicknamed “Nano Banana” surfaced in a web demo and started doing the kind of edits you’d normally expect from a 3D suite with a modeler at the controls. Take two photos—your living room and a product shot—and it composites the object into the scene with shockingly consistent lighting and geometry. Ask for a 3D mesh “five inches off the skin,” and the mesh really does offset. Ask for a new camera angle on a single still, and it renders the scene from above with plausible structure. People have been calling it a game-changer and, for once, it doesn’t feel like hyperbole.

There’s a strong whiff of 3D world modeling under the hood—some volumetric representation or neural field that enables true view synthesis—and Logan Kilpatrick posted a banana emoji that set speculation on fire. We’ll see where it lands and under what license, but for now the demo has been doing the rounds and it is… wow.

If you’re wondering where to try it: LMarena is the currently only way to give it a try, but it's supossedly dropping soon!

Qwen Image Edit (20B): fully open and already practical (X, HF)

Qwen shipped a 20B image-editing model layered on their existing vision stack, and it’s properly open (permissive license) with strong bilingual text editing in Chinese and English. It handles high-level semantic edits (pose adjustments, rotations, style/IP creation) and low-level touch-ups (add/remove/insert). You can swap objects, expand aspect ratios, keep character identity consistent across panels, and do clean style transfer. It runs locally if you’ve got a decent GPU.

What I appreciate here is the precision. Product placement tasks like “put this book in this person’s hand at this angle,” or “make the shoes match the dress” come out with the kind of control that used to require hand masking and a dozen passes. And yes, the capybara mascot is back in the release materials, which made my day! 👏

If Nano Banana is the closed-world demo of what’s “beyond SOTA,” Qwen Image Edit is the open tool you can actually ship with today.

This week’s buzz from Weights & Biases

Two quick updates from our side. First, we’re working to bring DeepSeek V3.1 to our inference as fast as we can so you can run serious benchmarks without fussing over serving stacks. Keep an eye on our channels; we’ll shout when it’s live and we’ll have some credits for early feedback.

Second, our cofounder Chris Van Pelt released Catnip, a containerized multi‑agent coding workspace that runs multiple Claude Code sessions (or other agents) in isolated sandboxes, each with its own context and notification stream. If you’ve been juggling parallel coding agents that step on each other’s toes, this is catnip indeed.

Catnip Github: https://github.com/wandb/catnip

Closing thoughts

A year ago, “thinking tokens” weren't even a curiosity; We only got the first whiff of "reasoning" back in September, and now we’re watching hybrid models that do more with less thinking, tool calls woven inside the reasoning loop, and long-context training regimes scaled up by an order of magnitude. The agent stack is maturing fast—desktop RL is finally clearing real tasks; editor ecosystems are converging on a single config file; and even the stealth drops are clearly building toward world-model‑aware editing and control.

If you only try two things this week: run DeepSeek V3.1 in both modes (planning with thinking on, execution with thinking off) and throw a complex multi-step tool workflow at it; then take Qwen Image Edit for a spin on a real product-placement or character-consistency task. You’ll feel the future in your hands.

I’m off to the desert next week for a bit (no internet where I’m going), but Wolfram and the crew will keep the ship on course. If you’re at Burning Man, DM me—would love to say hi out there. As always, thank you for tuning in and nerding out with us every week.

— Alex

TL;DR and show notes

ThursdAI - Aug 21, 2025 - TL;DR

ThursdAI - Aug 21, 2024 - TL;DR

TL;DR of all topics covered:

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases

* Co Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Open Source LLMs // Papers

* ByteDance Seed-OSS - 36B open-source LLM family (X, HF, GitHub)

* DeepSeek V3.1 - Updated Hybrid model (HF)

* Cohere CMD-a Reasoning - (X, Blog, HF)

* Zai/Tsinghua ComputerRL - Framework for desktop agents (X, Paper, Benchmark)

* IBM & NASA Surya - Solar weather prediction (HF)

* NVIDIA Nemotron Nano 9B V2 - (X, Blog, HF, Dataset, Try It)

* Alibaba Quark Med

* Big CO LLMs + APIs

* Sonic Stealth Model - Likely Grok Code

* OpenAI agents.md - Unified agent files (agents.md)

* GPT-5 Nerf

* AI Art & Diffusion & 3D

* Nano Banana - Image model (rumored Google)

* Qwen-Image-Edit - 20B Image editing (X, HF)

* This weeks Buzz

* Catnip - Containerized AI agent runner (GitHub)

Last week, I tried to test GPT-5 and got really surprisingly bad results, but it turns out, as you'll see below, it's partly because they had a bug in the router, and partly because ... well, the router itself! See below for an introduction, written by GPT-5, it's actually not bad?

Last week was a whirlwind. We live‑streamed GPT‑5’s “birthday,” ran long, and then promptly spent the next seven days poking every corner of the new router‑driven universe.

This week looked quieter on the surface, but it actually delivered a ton: two open‑source world models you can drive in real time, a lean vision‑language model built for edge devices, a 4B local search assistant that tops Perplexity Pro on SimpleQA, a base model “extraction” from GPT‑OSS that reverses alignment, fresh memory features landing across the big labs, and a practical prompting guide to unlock GPT‑5’s reasoning reliably.

We also had Alan Dao join to talk about Jan‑v1 and what it takes to train a small model that consistently finds the right answers on the open web—locally.

Not bad eh? Much better than last time 👏 Ok let's dive in, a lot to talk about in this "chill" AI week (show notes at the end as always) first open source, and then GPT-5 reactions and then... world models!

00:00 Introduction and Welcome

00:33 Host Introductions and Health Updates

01:26 Recap of Last Week's AI News

01:46 Discussion on GPT-5 and Prompt Techniques

03:03 World Models and Genie 3

03:28 Interview with Alan Dow from Jan

04:59 Open Source AI Releases

06:55 Big Companies and APIs

10:14 New Features and Tools

14:09 Liquid Vision Language Model

26:18 Focusing on the Task at Hand

26:18 Reinforcement Learning and Reward Functions

26:35 Offline AI and Privacy

27:13 Web Retrieval and API Integration

30:34 Breaking News: New AI Models

30:41 Google's New Model: Gemma 3

33:53 Meta's Dino E3: Advancements in Computer Vision

38:50 Open Source Model Updates

45:56 Weights & Biases: New Features and Updates

51:32 GPT-5: A Week in Review

55:12 Community Outcry Over AI Model Changes

56:06 OpenAI's Response to User Feedback

56:38 Emotional Attachment to AI Models

57:52 GPT-5's Performance in Coding and Writing

59:55 Challenges with GPT-5's Custom Instructions

01:01:45 New Prompting Techniques for GPT-5

01:04:10 Evaluating GPT-5's Reasoning Capabilities

01:20:01 Open Source World Models and Video Generation

01:27:54 Conclusion and Future Expectations

Open Source AI

We've had quite a lot of Open Source this week on the show, including a breaking news from the Gemma team!

Liquid AI's drops LFM2-VL (X, blog, HF)

Let's kick things off with our friends at Liquid AI who released LFM2-VL - their new vision-language models coming in at a tiny 440M and 1.6B parameters.

Liquid folks continue to surprise with speedy, mobile device ready models, that run 2X faster vs top VLM peers. With a native 512x512 resolution (which breaks any image size into 512 smart tiles) and an OCRBench of 74, this tiny model beats SmolVLM2 while being half the size. We've chatted with Maxime from liquid about LFM2 back in july, and it's great to see they are making them multimodal as well with the same efficiency gains!

Zhipu (z.ai) unleashes GLM-4.5V - 106B VLM (X, Hugging Face)

In another "previous good model that now has eyes" release, the fine folks from Zhipu continued training thier recently released (and excelled) GLM 4.5-air with a vision encoder, resulting in probably one of the top vision models in the open source!

It's an MoE with only 12B active parameters (106B total) and gets SOTA across 42 public vision-language benches + has a "thinking mode" that reasons about what it sees.

Given that GLM-4.5Air is really a strong model, this is de fact the best visual intelligence in the open source, able to rebuild websites from a picture for example and identify statues and locations!

Jan V1 - a tiny (4B) local search assistant QwenFinetune (X, Hugging Face)

This one release got a lot of attention, as the folks at Menlo Research (Alan Dao who came to chat with us about Jan on the pod today) released an Apache 2 finetune of Qwen3-4B-thinking, that's focused on SimpleQA.

They showed that their tiny model is beating Perplexity Pro on SimpleQA.

Alan told us on the pod that Jan (the open source Jan app) is born to be an open source alternative to searching with local models!

The trick is, you have to enable some source of search data (Exa, Serper, Tavily) via MCP and then enable tools in Jan, and then.. you have a tiny, completely local perplexity clone with a 4B model!

Google drops Gemma 3 270M (blog)

In some #breakingNews, Google open sourced a tiny (270M) parameters, "good at instruction following" Gemma variant. This joins models like SmolLM and LFM2 in the "smol models" arena, being only 300MB, you can run this.. on a toaster. This one apparently also fine-tunes very well while being very energy efficient!

Big Companies (AKA OpenAI corner this past 2 weeks)

Ok ok, we're finally here, a week with GPT-5! After watching the live stream and getting access to GPT-5, my first reactions were not great. Apparently, so have other peoples, and many folks outcried and complained about the model, some even yelling "AGI is cancelled".

What apparently happened is (and since, been fixed by OpenAI) is that GPT-5 wasn't just a model that launched, it was a "smart" router between a few models, and not only did they have a routing bug, the basic GPT-5 model, the one without thinking, is... not great.

But the thinking GPT-5, the one that the router refused to send me to, is really good (as confirmed independently by multiple evals at this point)

For one, it's the most accurate function calling model on OpenRouter

It's also one of the best on this new FormulaOne benchmark that was just launched

You're prompting it wrong!

Apparently, not only is GPT-5 more intelligent, it's also significantly "surgical" in instruction following, and so, for many folks, just replacing GPT-5 into their tools or prompts didn't just "work", as this model, more than before, is sensitive to conflicting things in the prompt.

OpenAI has released a guide for prompting the model, mostly aimed at developers (as users shouldn't be learning to prompt as models get more intelligent) + they also released a prompt optimizer! Just dump your long and complex prompts in there, and you'll get an updated prompt with explanations of why they changed what they changed!

Model Picker (and legacy models) are back!

So, OpenAI tried and super quickly reversed course on removing the "model picker". At first, it was only GPT-5 there, but many people complained about the abrupt removal of 4o, their .. favorite models. At first, OpenAI added back the models via a hidden setting, and later, they have added 4o back to everyone by default, while increasing the reasoning quota to 3000 messages per week!

Generally, my thoughts are, if you've tried GPT-5 and weren't impressed, give it another go! (especially now that it's connected to Gmail in chats!)

Other notable Big Company updates

In other news, Claude has extended the context window of Sonnet to 1M in the API, and apparently both Claude and Gemini have been adding memory features!

Grok video has been catching up and is now free for a while to all users

This Week's Buzz: Weave DX improvements

Quick update from my day job at Weights & Biases - we've rolled out some quality-of-life improvements to Weave, our LLM observability platform. We now have a unified assets tab where you can manage all your prompts, models, and datasets with full versioning support.

Prompts are being version tracked, so if you use that GPT-5 prompt optimizer, we'll store all the previous revisions for ya!

The coolest addition? Threads! Perfect for tracking agent executions or grouping related API calls. You just add a thread_id to your traces and Weave handles the rest. If you're building AI applications and not tracking everything, you're flying blind - give Weave a try at wandb.me/weave!

World models are getting... open sourced!

I still think that Google's Genie-3 release from last week was maybe the more important one, though we didn't really get to play with it yet!

And while getting excited by world models, I was thinking that it's goig to take a while for Open Source to catch up. But this week, not 1, but two world models were open sourced, making me think that we'll get to generated worlds quicker than I expected and the race has begun!

Skywork's Matrix-Game 2.0 (project, HF)

Matrix-game 2 is a auto-regressive diffusion model, that was trained on 1200 hours of Unreal Engine and GTA-5 environments that runs at 25 frames per second!

It works by creating an "action injection module" that embeds the mouse/keyboard inputs into the generation, enabling frame-level controls.

Hunyuan open-sources GameCraft for real-time, high-dynamic game video generation (X, Hugging Face)

Two world-models (well, game models) in the same week? Tencent (who had Hunyuan video before) have trained a game engine on top of their excellent HY-video and have shown the same examples, of building a full world based on a few images.

Their pipeline trained on 1M game play clips from AAA titles, and they also map W/A/S/D and mouse signals into continuous camera/action embeddings, allowing for control and angle creation.

The cool thing? A quantized 13B version supposedly can run on a RTX 4090!

Funnily, they already had Matrix-Game (the one that came out a few days before) benchmarked and beat on the release today!

Genie 3 is not messing around

While all the open source is impressive, I was… absolutely blown away by this video from an artist who got the Genie3 team to extend a video of his. Just look at the collision of the plane with the sphere, out of nowhere, Genie3 adds a shadow, and then collision mechanics, the plane bouncing off, and even the jet trails subside and then resume! It really really is crazy to imagine that no prompting was given and the model just.. knew how to do this!

Phew, that was a lot! Much more as always on the actual show, despite it being a "quiet" week by summer of 2025 standards, there was a LOT of open source releases and GPT-5 situation shows that even OpenAI can stumble on new tech releases!

Keep the feedback coming - find me on Twitter/X at @altryne or email the show. And remember, if you want to catch all the technical details and demos, the video version on YouTube has everything the podcast can't show.

Until next week, keep tinkering, keep questioning, and keep pushing the boundaries of what's possible with AI!

See you next ThursdAI 🚀

TL;DR - All Topics Covered

Hosts and Guests

* Alex Volkov - AI Evangelist @ Weights & Biases (@altryne)

* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Guest: Alan Dao from Jan (@jandotai)

Open Source LLMs

* Liquid AI LFM2-VL: 440M & 1.6B vision-language models with 2x GPU speedup (Blog, HF)

* Jan V1: 4B parameter search assistant beating Perplexity Pro on SimpleQA (X, HF)

* GPT-OSS Base: Reverse-engineered base model from Jack Morris (X Thread)

* Gemma 3: Google's 270M parameter model with strong instruction following (HF)

* Meta Dino v3: Self-supervised vision model for segmentation (Blog)

Big Companies & APIs

* Mistral Medium 3.1: New model on Mistral platform

* Gemini & Claude: Added memory/personalization features

* GPT-5 Updates: Router fixes, model selector returned, prompting guide released (Guide, Optimizer)

* Claude Sonnet 4: 1M token context window (Announcement)

This Week's Buzz

* Weave updates: Unified assets tab and threads for agent tracking

* New features for LLM observability and evaluation

Vision & Video

* Hunyuan Large Vision: 1B vision encoder + 389B MoE language model (Project)

* GLM-4.5V: 106B open source VLM from Zhipu AI (X, HF)

World Models

* Matrix Game 2.0: Real-time interactive world model, 25fps generation (Project, HF)

* Hunyuan GameCraft: Game video generation with physics understanding (X, HF)

Tools

* Grok: Now includes video generation for all users

* Jan Desktop App: Local AI with MCP support and search capabilities

Vacation or not, ThursdAI is keeping you up to date (for 32 months straight, which is also the time since the original GPT-4 release which gave this show its name!)

So, what did we have today on the stream? Well, we started as usual, talking about the AI releases of the week, as if OpenAI dropping OSS models (apache 2) 120B and 20B is "usual". We then covered incredible releases like Google's World model Genie3 (more on this next week!) and Qwen-image + a few small Qwens.

We then were VERY excited to tune in, and watch the (very long) announcement stream from OpenAI, in which they spent an hour to tell us about GPT-5.

This was our longest stream by far (3.5 hours, 1hr was just OpenAI live stream) and I'm putting this here mostly unedited, but chapters are up so feel free to skip to the parts that are interesting to you the most.

00:00 Introduction and Special Guests

00:56 Twitter Space and Live Streaming Plans

02:12 Open Source AI Models Overview

03:44 Qwen and Other New AI Models

08:59 Community Interaction and Comments

10:01 Technical Deep Dive into AI Models

25:06 OpenAI's New Releases and Benchmarks

38:49 Expectations and Use Cases for AI Models

40:03 Tool Use vs. Deep Knowledge in AI

41:02 Evaluating GPT OSS and OpenAI Critique

42:29 Historical and Medical Knowledge in AI

51:16 Opus 4.1 and Coding Models

55:38 Google's Genie 3: A New World Model

01:00:43 Kitten TTS: A Lightweight Text-to-Speech Model

01:02:07 11 Labs' Music Generation AI

01:08:51 OpenAI's GPT-5 Launch Event

01:24:33 Building a French Learning Web App

01:26:22 Exploring the Web App Features

01:29:19 Introducing Enhanced Voice Features

01:30:02 Voice Model Demonstrations

01:32:32 Personalizing Chat GPT

01:33:23 Memory and Scheduling Features

01:35:06 Safety and Training Enhancements

01:39:17 Health Applications of GPT-5

01:45:07 Coding with GPT-5

01:46:57 Advanced Coding Capabilities

01:52:59 Real-World Coding Demonstrations

02:10:26 Enterprise Applications of GPT-5

02:11:49 Amgen's Use of GPT-5 in Drug Design

02:12:09 BBVA's Financial Analysis with GPT-5

02:12:33 Healthcare Applications of GPT-5

02:12:52 Government Adoption of GPT-5

02:13:22 Pricing and Availability of GPT-5

02:13:51 Closing Remarks by Chief Scientist Yakob

02:16:03 Live Reactions and Discussions

02:16:41 Technical Demonstrations and Comparisons

02:33:53 Healthcare and Scientific Advancements with GPT-5

02:47:09 Final Thoughts and Wrap-Up

---

My first reactions to GPT-5

Look, I gotta keep it real with you, my first gut reaction was, hey, I'm on vacation, I don't have time to edit and write the newsletter (EU timezone) so let's see how ChatGPT-5 handles this task. After all, OpenAI has removed all other models from the dropdown, it's all GPT-5 now. (pricing from the incredible writeup by Simon Willison available here)

And to tell you the truth, I was really disappointed! GPT seems to be incredible at coding benchmarks, with 400K tokens and incredible pricing (just $1.25/$10 compared to Opus $15/$75) this model, per the many friends who got to test it early, is a beast at coding! Readily beating opus on affordability per token, switching from thinking to less thinking when it needs to, it definitely seems like a great improvement for coding and agentic tasks.

But for my, very much honed prompt of "hey, help me with ThursdAI drafts, here's previous drafts that I wrote myself, mimic my tone" it failed.. spectacularly!

Here's just a funny example, after me replying that it did a bad job:

It literally wrote "I'm Alex, I build the mind, not the vibe" 🤦‍♂️ What.. the actual...

For comparison, here's o3, with the same prompt, with a fairly true to tone draft:

High taste testers take on GPT-5

But hey, I have tons of previous speakers in our group chats, and many of them who got early access (I didn't... OpenAI, I can be trusted lol) rave about this model. They are saying that this is a huge jump in intelligence.

Folks like Dr Derya Unutmaz, who jumped on the live show and described how GPT5 does incredible things with less hallucinations, folks like Swyx from Latent.Space who had early access and even got invited to give first reactions to the OpenAI office, and Pietro Schirano who also showed up in an OpenAI video.

So definitely, definitely check out their vibes, as we all try to wrap our heads around this new intelligence king we got!

Other GPT5 updates

OpenAI definitely cooked, don't get me wrong, with this model plugging into everything else in their platform like memory, voice (that was upgraded and works in custom GPTs now, yay!), canvas and study mode, this will definitely be an upgrade for many folks using the models.

They have now also opened access to GPT-5 to free users, just in time for schools to reopen, including a very interesting Quiz mode (that just showed up for me without asking for it), and connection to Gmail, all those will now work with GPT5.

It now has 400K context, way less hallucinations but fewer refusals also, and the developer upgrades like a new verbosity setting and a new "minimal" reasoning setting are all very welcome!

OpenAI finally launches gpt-oss (120B / 20B) apache 2 licensed models (model card, HF)

It was really funny, on the stream Nisten talked about the open source models OpenAI dropped, and said "when we covered it last week", while it was just two days ago! It really does feel like this world is moving really fast.

OpenAI's long promised open source models are here, and they got a fairly mixed bag of reviews from folks. Many folks are celebrating that the western world is now back in the game, releasing incredible local models, with an open license!

Though, after the initial excitement, the vibes are split on these models. Folks are saying that maybe these were trained with only synthetic data, because, like Phi, they seem to be very good at benchmarks, and on the specific tasks they were optimized for (code, math) but really bad at creative writing (Sam Paech from EQBench was not impressed), they are also not multilingual, though OpenAI did release a cookbook on finetuning with HuggingFace!

Overall, these models are trained for agentic workflows—supporting function calling, web search, Python execution, configurable reasoning effort, and full raw chain-of-thought access, which we will never get from GPT5.

I particularly love the new approach, where a reasoning effort can be defined directly via the system prompt, by just adding "reasoning: high" to the system prompt, this model will reason for way longer! Can't wait to get back and bench these and share with you.

Overall, the fine-tuning and open source community is split for now, but it's been only a few days, so we'll keep you up to date on how well these models land, regardless, this was a historic week for OpenAI!

Speaking of open models, did you have a chance to try our W&B Inference? The team worked hard to bring these new models to you in record time and incredible pricing (just $.05 for 20B and $.15 for 120B!), these models are definitely worth giving a try!

Plus, if you comment "OSS Power" on our announcement post, we'll likely give you a few credits to try it out and let us know what you think!

World models "holy crap" moment - Google Genie3

The other very important release this week was.... not a release at all, but an announcement from Deepmind, with Genie3.

This World Model takes a single image or text prompt and creates a fully interactive, controllable 3D environment that runs in real-time at 24fps. An environment you as a user can control, walk (or fly) in, move around the camera view. It's really mindblowing stuff.

We've covered world models like Mirage on previous episodes, but what Google released is a MAJOR step up in coherency, temporal consistency and just overall quality!

The key breakthrough here is consistency and memory. In one demo, a user could "paint" a virtual wall, turn away, and when they turned back, the paint was still there. This is a massive step towards generalist agents that can train, plan, and reason in entirely simulated worlds, with huge implications for robotics and gaming.

We’re hoping to have the Genie 3 team on the show next week to dive even deeper into this incredible technology!!

Other AI news this week

This week, the "other" news could have filled a full show 2 years ago, we got Qwen keeping the third week of releases with 2 new tiny models + a new diffusion model called Qwen-image (Blog, HF)

Anthropic decided to pre-empt the GPT5 release, and upgraded Opus 4 and gave us Opus 4.1 with a slight bump in specs.

ElevenLabs released a music API called ElevenMusic, which sounds very very good (this on top of last weeks Riffusion + Producer.ai news, that I'm still raving about)

Also in voice an audio, a SUPER TINY TTS model called KittenTTS released, with just 15M parameters and a model that's 25MB, it's surprisingly decent at generating voice (X)

And to cap it off with breaking news, the Cursor team, who showed up on the OpenAI stream today (marking quite the change in direction from OpenAI + Windsurf previous friendship), dropped their own CLI version of cursor, reminiscent of Claude Code!

PHEW, wow ok this was a LOT to process. Not only did we tune in for the full GPT-5 release, we did a live stream when gpt-oss dropped as well.

On a personal note, I was very humbled when Sam Altman said it was 32 months since GPT-4 release, because it means this was 32 months of ThursdAI, as many of you know, we started live streaming on March 13, 2023, when GPT-4 was released.

I'm very proud of the incredible community we've built (50K views total across all streams this week!), the incredible co-hosts I have, who step up when I'm on vacation and the awesome guests we have on the show, to keep you up to date every week!

So, a little favor to ask, if you find our content valuable, entertaining, the best way to support this pod is upgrade to a paid sub, and share ThursdAI with a friend or two! 👏 See you next week 🫡

Woohoo, we're almost done with July (my favorite month) and the Open Source AI decided to go out with some fireworks 🎉

Hey everyone, Alex here, writing this without my own personal superintelligence (more: later) and this week has been VERY BUSY with many new open source releases.

Just 1 hour before the show we already had 4 breaking news releases, a tiny Qwen3-coder, Cohere and StepFun both dropped multimodal SOTAs and our friends from Krea dropped a combined model with BFL called Flux[Krea] 👏

This is on top of a very very busy week, with Runway adding conversation to their video model Alpha, Zucks' superintelligence vision and a new SOTA open video model Wan 2.2. So let's dive straight into this (as always, all show notes and links are in the end)

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source LLMs & VLMs

Tons of new stuff here, I'll try to be brief but each one of these releases deserves a deeper dive for sure.

Alibaba is on 🔥 with 3 new Qwen models this week

Yes, this is very similar to last week, where they have also dropped 3 new SOTA models in a week, but, these are additional ones.

It seems that someone in Alibaba figured out that after splitting away from the hybrid models, they can now release each model separately and get a lot of attention per model!

Here's the timeline:

* Friday (just after our show): Qwen3-235B-Thinking-2507 drops (235B total, 22B active, HF)

* Tuesday: Qwen3-30B-Thinking-2507 (30B total, 3B active, HF)

* Today: Qwen3-Coder-Flash-2507 lands (30B total, 3B active for coding, HF)

Lets start with the SOTA reasoner, the 235B(A22B)-2507 is absolutely the best reasoner among the open source models.

We've put the model on our inference service (at crazy prices $.10/$.10) and it's performing absolutely incredible on reasoning tasks.

It also jumped to the top OSS model on Artificial Analysis scores, EQBench, Long Context and more evals. It a really really good reasoning model!

Smaller Qwens for local use

Just a week ago, we've asked Junyang on our show, about smaller models that folks can run on their devices, and he avoided by saying "we're focusing on the larger models" and this week, they delivered not 1 but 2 smaller versions of the bigger models (perfect for Speculative Decoding if you can host the larger ones that is)

The most interesting one is the Qwen3-Coder-flash, which came out today, with very very impressive stats - and the ability to run locally with almost 80 tok/s on a macbook!

So for the last two weeks, we now have 3 Qwens (Instruct, Thinking, Coder) and 2 sizes for each (all three have a 30B/A3B version now for local use) 👏

Z.ai GLM and StepFun Step3

As we've said previously, Chinese companies completely dominate the open source AI field right now, and this week as saw yet another crazy testament to how stark the difference is!

We've seen a rebranded Zhipu (Z.ai previously THUDM) release their new GLM 4.5 - which gives Qwen3-thinking a run for it's money. Not quite at that level, but definitely very close. I personally didn't love the release esthetics, showing a blended eval score, which nobody can replicate feels a bit off.

We also talked about how StepFun has stepped in (sorry for the pun) with a new SOTA in multimodality, called Step3. It's a 321B MoE (with a huge 38B active param count) that achieves very significant multi modal scores (The benchmarks look incredible: 74% on MMMU, 64% on MathVision)

Big Companies APIs & LLMs

Well, we were definitely thinking we'll get GPT-5 or the Open Source AI model from OpenAI this week, but alas, the tea leaves readers were misled (or were being misleading). We 100% know that gpt-5 is coming as multiple screenshots were blurred and then deleted showing companies already testing it.

But it looks like August is going to be even hotter than July, with multiple sightings of anonymous testing models on Web Dev arena, like Zenith, Summit, Lobster and a new mystery model on OpenRouter called Zenith - that some claim are the different thinking modes of GPT-5 and the open source model?

Zuck shares vision for personalized superintelligence (Meta)

In a very "Nat Fridman" like post, Mark Zuckerberg finally shared the vision behind his latest push to assemble the most cracked AI engineers.

In his vision, Meta is the right place to provide each one with personalized superintelligence, enhancing individual abilities with user agency according to their own values. (as opposed to a centralized model, which feels like his shot across the bow for the other frontier labs)

A few highlights: Zuck leans heavily into the rise of personal devices on top of which humans will interact with this superintelligence, including AR glasses and a departure from a complete "let's open source everything" dogman of the past, now there will be a more deliberate considerations of what to open source.

This Week's Buzz: Putting Open Source to Work with W&B

With all these incredible new models, the biggest question is: how can you actually use them? I'm incredibly proud to say that the team at Weights & Biases had all three of the big new Qwen models—Thinking, Instruct, and Coder—live on W&B Inference on day one (link)

And our pricing is just unbeatable. Wolfram did a benchmark run that would have cost him $150 using Claude Opus. On W&B Inference with the Qwen3-Thinking model, it cost him 22 cents. That's not a typo. It's a game-changer for developers and researchers.

To make it even easier, a listener of the show, Olaf Geibig, posted a fantastic tutorial on how you can use our free credits and W&B Inference to power tools like Claude Code and VS Code using LiteLLM. It takes less than five minutes to set up and gives you access to state-of-the-art models for pennies. All you need to do is add this config to vllm and run claude (or vscode) through it!

Give our inference service a try here and follow our main account @weights_biases a follow as we often drop ways to get additional free credits when new models release

Vision & Video models

Wan2.2: Open-Source MoE Video Generation Model Launches (X, HF)

This is likely the best open source video model, but definitely the first MoE video model! It came out with text2video, image2video and a combined version.

With 5 second 720p videos, that can even be generator at home on a single 4090, this is definitely a step up in the quality of video models that are fully open source.

Runway changes the game again - Gen-3 Aleph model for AI video editing / transformation (X, X)

Look, there's simply no denying this, AI video has had an incredible year, from open source like Wan, to proprietary models with sounds like VEO3. And it's not surprising that we're seeing this trend, but it's definitely very exciting when we see an approach like Runway has, to editing.

This adds a chat to the model, and your ability to edit.. anything in the scene. Remove / Add people and environmental effects, see the same scene from a different angle and a lot more!

Expect personalized entertainment very soon!

AI Art & Diffusion & 3D

FLUX.1 Krea [dev] launches as a state-of-the-art open-weights text-to-image model (X, HuggingFace)

Black Forest Labs teamed with Krea AI for Flux.1 Krea [dev], an open-weights text-to-image model ditching the "AI gloss" for natural, distinctive vibes—think DALL-E 2's quirky grain without the saturation. It outperforms open peers and rivals pros in prefs, fully Flux-compatible for LoRAs/tools. Yam and I geeked over the aesthetics frontier; it's a flexible base for fine-tunes, available on Hugging Face with commercial options via FAL/Replicate. If you're tired of cookie-cutter outputs, this breathes fresh life into generations.

Ideogram Character launches: one-shot character consistency for everyone (X)

Ideogram's Characters feature lets you upload one pic for instant, consistent variants—free for all, with inpainting to swap into memes/art. My tests nailed expressions/scenes (me in cyberpunk? Spot-on), though not always photoreal. Wolfram praised the accuracy; it's a meme-maker's dream! and they give like 10 free ones so give it a go

Tencent Hunyuan3D World Model 1.0 launches as the first open-source, explorable 3D world generator (X, HF)

Tencent's Hunyuan3D World Model 1.0 is the first open-source generator of explorable 3D worlds from text/image—360° immersive, exportable meshes for games/modeling. ~33GB VRAM on complex scenes, but Wolfram called it a metaverse step; I wandered a demo scene, loving the potential despite edges. Integrate into CG pipelines? Game-changer for VR/creators.

Voice & Audio

Look I wasn't even mentioning this on the show, but it came across my feed just as I was about to wrap up ThursdAI, and it's really something. Riffusion joined forces producer and using FUZZ-2 they now have a fully Chatable studio producer, you can ask for.. anything you would ask in a studio!

Here's my first reaction, and it's really fun, I think they still are open with the invite code 'STUDIO'... I'm not afiliated with them at all!

Tools

Ok I promised some folks we'll add this in, Nisten went super viral last week with him using a new open source tool called Crush from CharmBracelet, which is an open version of VSCode and it looks awesome!

He gave a demo live on the show, including how to set it up to work, with subagents etc. If you're into vibe coding, and using the open source models, def. give Crush a try it's really flying and looks cool!

Phew, ok, we somehow were able to cover ALLL these releases this week, and we didn’t even have an interview!

Here’s the TL;DR and links to the folks who subscribed (I’m trying a new thing to promote subs on this newsletter) and see you in two weeks (next week is Wolframs turn again as I’m somewhere in Europe!)

ThursdAI - July 31st, 2025 - TL;DR

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldj

* Open Source LLMs

* Zhipu drops GLM-4.5 355B (A32B) AI model (X, HF)

* ARCEE AFM‑4.5B and AFM‑4.5B‑Base weights released (X, HF)

* Qwen is on 🔥 - 3 new models:

Was a pleasure of mine to host Junyang Lin from the team at Alibaba to come and chat with us about their incredible release with, with not 1 but three new models!

Then, we had a great chat with Joseph Nelson from Roboflow, who not only dropped additional SOTA models, but was also in Washington at the annocement of the new AI Action plan from the WhiteHouse.

Great conversations this week, as always, TL;DR in the end, tune in!

Open Source AI - QwenMass in July

This week, the open-source world belonged to our friends at Alibaba Qwen. They didn't just release one model; they went on an absolute tear, dropping bomb after bomb on the community and resetting the state-of-the-art multiple times.

A "Small" Update with Massive Impact: Qwen3-235B-A22B-Instruct-2507

Alibaba called this a minor refresh of their 235B parameter mixture-of-experts.

Sure—if you consider +13 points on GPQA, 256K context window minor. The 2507 drops hybrid thinking. Instead, Qwen now ships separate instruct and chain-of-thought models, avoiding token bloat when you just want a quick answer. Benchmarks? 81 % MMLU-Redux, 70 % LiveCodeBench, new SOTA on BFCL function-calling. All with 22 B active params.

Our friend of the pod, and head of development at Alibaba Qwen, Junyang Lin, join the pod, and talked to us about their decision to uncouple this model from the hybrid reasoner Qwen3.

"After talking with the community and thinking it through," he said, "we decided to stop using hybrid thinking mode. Instead, we'll train instruct and thinking models separately so we can get the best quality possible."

The community felt the hybrid model sometimes had conflicts and didn't always perform at its best. So, Qwen delivered a pure non-reasoning instruct model, and the results are staggering. Even without explicit reasoning, it's crushing benchmarks. Wolfram tested it on his MMLU-Pro benchmark and it got the top score of all open-weights models he's ever tested. Nisten saw the same thing on medical benchmarks, where it scored the highest on MedMCQA. This thing is a beast, getting a massive 77.5 on GPQA (up from 62.9) and 51.8 on LiveCodeBench (up from 32). This is a huge leap forward, and it proves that a powerful, well-trained instruct model can still push the boundaries of reasoning.

The New (open) King of Code: Qwen3-Coder-480B (X, Try It, HF)

Just as we were catching our breath, they dropped the main event: Qwen3-Coder. This is a 480-billion-parameter coding-specific behemoth (35B active) trained on a staggering 7.5 trillion tokens, with a 70% code ratio, that gets a new SOTA on SWE-bench verified with 69.6% (just a week after Kimi got SOTA with 65% and 2 weeks after Devstral's SOTA of 53% 😮)

To get this model to SOTA, Junyang explained they used reinforcement learning with over 20,000 parallel sandbox environments. This allows the model to interact with the environment, write code, see the output, get the reward, and learn from it in a continuous loop. The results speak for themselves.

With long context abilities 256K with up to 1M extended with YaRN, this coding beast tops the charts, and is achieving Sonnet level performance for significantly less cost!

Both models supported day-1 on W&B Inference (X, Get Started)

I'm very very proud to announce that both these incredible models get Day-1 support on our W&B inference (and that yours truly is now part of the decision of which models we host!)

With unbeatable prices ($0.10/$0.10 input/output 1M for A22B, $1/$1.5 for Qwen3 Coder) and speed, we are hosting these models at full precision to give you the maximum possible intelligence and the best bang for your buck!

Nisten has setup our (OpenAI compatible) endpoint with his Cline coding assistant and has built a 3D Tetris game live on the show, and it absolutely went flying.

This demo perfectly captures the convergence of everything we're excited about: a state-of-the-art open-source model, running on a blazing-fast inference service, integrated into a powerful open-source tool, creating something complex and interactive in seconds.

If you want to try this yourself, we're giving away credits for W&B Inference. Just find our announcement tweet for the Qwen models on the @weights_biases X account and reply with "coding capybara" (a nod to Qwen's old mascot!). Add "ThursdAI" and I'll personally make sure you get bumped up the list!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Big Companies & APIs

America’s AI Action Plan: A New Space Race for AI Dominance (ai.gov)

Switching gears to policy, I’m was excited to cover the White House’s newly unveiled “America’s AI Action Plan.” This 25-page strategy, dropped this week, frames AI as a national priority on par with the space race or Cold War, aiming to secure U.S. dominance with 90 policy proposals. I was thrilled to have Joseph Nelson from RoboFlow join us fresh from the announcement event in Washington, sharing the room’s energy and insights. The plan pushes for deregulation, massive data center buildouts, workforce training, and—most exciting for us—explicit support for open-source and open-weight models. It’s a bold move to counter global competition, especially from China, while fast-tracking infrastructure like chip fabrication and energy grids.

Joseph broke down the vibe at the event, including a surreal moment where the President riffed on Nvidia’s market dominance right in front of Jensen Huang. But beyond the anecdotes, what strikes me is the plan’s call for startups and innovation—think grants and investments via the Department of Defense and Small Business Administration. It’s like a request for new AI companies to step up. As someone who’s railed against past moratorium fears on this show, seeing this pro-innovation stance is a huge relief.

🔊 Voice & Audio – Higgs Audio v2 Levels Up (X)

Boson AI fused a 3B-param Llama 3.2 with a 2.2B audio Dual-FFN and trained on ten million hours of speech + music. Result: Higgs Audio v2 beats GPT-4o-mini and ElevenLabs v2 on prosody, does zero-shot multi-speaker dialog, and even hums melodies. The demo runs on a single A100 and sounds pretty-good.

The first demo I played was not super impressive, but the laugh track made up for it!

🤖 A Week with ChatGPT Agent

Last week, OpenAI dropped the ChatGPT Agent on us during our stream, and now we've had a full week to play with it. It's a combination of their browser-operating agent and their deeper research agent, and the experience is pretty wild.

Yam had it watching YouTube videos and scouring Reddit comments to create a comparison of different CLI tools. He was blown away, seeing the cursor move around and navigate complex sites right on his phone.

I put it through its paces as well. I tried to get it to order flowers for my girlfriend (it got all the way to checkout!), and it successfully found and filled out the forms for a travel insurance policy I needed. My ultimate test (live stream here), however, was asking it to prepare the show notes for ThursdAI, a complex task involving summarizing dozens of my X bookmarks. It did a decent job (a solid C/B), but still needed my intervention. It's not quite a "fire-and-forget" tool for complex, multi-step tasks yet, but it's a huge leap forward. As Yam put it, "This is the worst that agents are going to be." And that's an exciting thought.

What a week. From open-source models that rival the best closed-source giants to governments getting serious about AI innovation, the pace is just relentless. It's moments like Nisten's live demo that remind me why we do this show—to witness and share these incredible leaps forward as they happen. We're living in an amazing time.

Thank you for being a ThursdAI subscriber. As always, here's the TL;DR and show notes for everything that happened in AI this week.

Thanks for reading ThursdAI - Recaps of the most high signal AI weekly spaces! This post is public so feel free to share it.

TL;DR and Show Notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Junyang Lin - Qwen Team, Alibaba (@JustinLin610)

* Joseph Nelson - Co-founder & CEO, Roboflow (@josephnelson)

* Open Source LLMs

* Sapient Intelligence releases Hierarchical Reasoning Model (HRM), a tiny 27M param model with impressive reasoning on specific tasks (X, arXiv).

* Qwen drops a "little" update: Qwen3-235B-A22B-Instruct-2507, a powerful non-reasoning model (X, HF Model).

* Qwen releases the new SOTA coding agent model: Qwen3-Coder-480B-A35B-Instruct (X, HF Model).

* Hermes-Reasoning Tool-Use dataset with 51k tool-calling examples is released (X, HF Dataset).

* NVIDIA releases updates to their Nemotron reasoning models.

* Big CO LLMs + APIs

* The White House unveils "America’s AI Action Plan" to "win the AI race" (X, White House PDF).

* Both OpenAI (X) and Google DeepMind win Gold at the International Math Olympiad (IMO), with ByteDance's Seed-Prover taking Silver (GitHub).

* The AI math breakthrough has a "gut punch" effect on the math community (Dave White on X).

* Google now processes over 980 trillion tokens per month across its services.

* A week with ChatGPT Agent: testing its capabilities on real-world tasks.

* This Week's Buzz

* Day 0 support for both new Qwen models on W&B Inference (Try it, Colab). Reply to our tweet with "coding capybara ThursdAI" for credits!

* Live on-stream demo of Qwen3-Coder building a 3D Tetris game using kline.

* Interesting Research

* Researchers discover subliminal learning in LLMs, where traits are passed through seemingly innocuous data (X, arXiv).

* Apple proposes multi-token prediction, speeding up LLMs by up to 5x without quality loss (X, arXiv).

* Voice & Audio

* Boson AI open-sources Higgs Audio v2, a unified TTS model that beats GPT-4o-mini and ElevenLabs (X, HF Model).

* AI Art & Diffusion & 3D

* Decart AI Releases MirageLSD, a real-time live-stream diffusion model for instant video transformation (X Post).

* Tools

* Qwen releases qwen-code, a CLI tool and agent for their new coder models. (Github)

* GitHub Spark, a new AI-powered feature from GitHub (Simon Willison on X).

This was one of those weeks where I kept checking if I was dreaming. Remember when DeepSeek dropped and we all lost our minds? Well, buckle up because Moonshot's Kimi K2 just made that look like a warm-up act. And that's not even the wildest part of this week!

As always, all the show notes and links are at the bottom, here's our liveshow (which included the full OAI ChatGPT agents watch party) - Let's get into it!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

🚀 Open Source LLMs: The Kimi K2 Revolution

The New Open Source King Has Arrived

Folks, I need you to understand something - just a little after we finished streaming last week celebrating Grok 4, a company called Moonshot decided to casually drop what might be the most significant open source release since... well, maybe ever?

Kimi K2 is a 1 trillion parameter model. Yes, you read that right - TRILLION. Not billion. And before you ask "but can my GPU run it?" - this is an MOE (Mixture of Experts) with only 32B active parameters, which means it's actually usable while being absolutely massive.

Let me give you the numbers that made my jaw drop:

* 65.8% on SWE-bench Verified - This non-reasoning model beats Claude Sonnet (and almost everything else)

* 384 experts in the mixture (the scale here is bonkers)

* 128K context window standard, with rumors of 2M+ capability

* Trained on 15.5 trillion tokens with the new Muon optimizer

The main thing about the SWE-bench score is not even just the incredible performance, it's the performance without thinking/reasoning + price!

The Muon Magic

Here's where it gets really interesting for the ML nerds among us. These folks didn't use AdamW - they used a new optimizer called Muon (with their own Muon Clip variant). Why does this matter? They trained to 15.5 trillion tokens with ZERO loss spikes. That beautiful loss curve had everyone in our community slack channels going absolutely wild.

As Yam explained during the show, claiming you have a better optimizer than AdamW is like saying you've cured cancer - everyone says it, nobody delivers. Well, Moonshot just delivered at 1 trillion parameter scale.

Why This Changes Everything

This isn't just another model release. This is "Sonnet at home" if you have the hardware. But more importantly:

* Modified MIT license (actually open!)

* 5x cheaper than proprietary alternatives

* Base model released (the first time we get a base model this powerful)

* Already has Anthropic-compatible API (they knew what they were doing)

The vibes are OFF THE CHARTS. Every high-taste model tester I know is saying this is the best open source model they've ever used. It doesn't have that "open source smell" - it feels like a frontier model because it IS a frontier model.

Not only a math genius

Importantly, this model is great at multiple things, as folks called out it's personality or writing style specifically! Our Friend Sam Paech, creator of EQBench, has noted that this is maybe the first time an open source model writes this well, and is in fact SOTA on his Creative Writing benchmark and EQBench!

Quick Shoutouts

Before we dive deeper, huge props to:

* Teknium for dropping the Hermes 3 dataset (nearly 1M high-quality entries!) (X)

* LG (yes, the fridge company) for EXAONE 4.0 - their 32B model getting 81.8% on MMLU Pro is no joke (X)

🎉 This Week's Buzz: W&B Inference Goes Live with Kimi-K2! (X)

Ok, but what if you want to try Kimi-K2 but don't have the ability to run 1T models willy nilly? Well, Folks, I've been waiting TWO AND A HALF YEARS to say this: We're no longer GPU poor!

Weights & Biases + CoreWeave = Your new inference playground. We launched Kimi K2 on our infrastructure within 3 days of release!

Sitting behind the scenes on this launch was surreal - as I've been covering all the other inference service launches, I knew exactly what we all want, fast inference, full non-quantized weights, OpenAI API compatibility, great playground to test it out, function calling and tool use. And we've gotten almost all of these, while the super cracked CoreWeave and W&B Weave teams worked their ass off over the weekend to get this shipped in just a few days!

And here’s the kicker: I’m giving away $50 in inference credits to 20 of you to try Kimi K2 on our platform. Just reply “K2-Koolaid-ThursdAI” to our X launch post here and we'll pick up to 20 winners with $50 worth of credits! 🫡

It’s live now at api.inference.wandb.ai/v1 (model ID: moonshotai/Kimi-K2-Instruct), fully integrated with Weave for tracing and evaluation. We’re just getting started, and I want your feedback to make this even better. More on W&B Inference Docs - oh and everyone gets $2 free even without me, which is like 500K tokens to test it out.

Big CO LLMs + APIs

The big players didn't sleep this week either—funding flew like confetti, Grok went full anime, and OpenAI dropped agents mid-stream (we reacted live!). Amazon snuck in with dev tools, and Gemini embeddings claimed the throne. Let's get through some of these openers before we get to the "main course" which of course came from OpenAI

Grok Gets... Waifus?

I can't believe I'm writing this in a serious AI newsletter, but here we are. XAI added animated 3D characters to Grok, including "Annie" - and let's just say she's very... interactive. XAI partnered with a company that does real time animated 3d avatars and these are powered by Grok so... they are a bit unhinged!

The same Elon who's worried about birth rates just created nuclear-grade digital companions. The Grok app shot to #1 in the Japanese App Store immediately. Make of that what you will. 😅

They even posted a job for "Full Stack Waifu Engineer" - we truly live in the strangest timeline.

XAI also this week addressed the concerns we all had with "mechahitler" and the Grok4 issues post launch (where it used it's web search to see "what does Elon think" when it was asked about a few topics)

Credit for finding the prompt change: Simon Willison

Other Quick Hits from Big Tech

* Gemini Embedding Model: New SOTA on MTEB leaderboards (68.32 score) (dev blog)

* Amazon S3 Vectors: Native vector storage in S3 (huge for RAG applications) (X)

* Amazon Kiro: Their VS Code fork with spec-driven development (think PM-first coding) (X)

🔥 OpenAI Agents: ChatGPT Levels Up to Do-It-All Sidekick

We timed it perfectly—OpenAI's live stream hit mid-show, and we reacted with 30,000+ of you! And while we didn't get the rumored Open Source model from OAI, we did get... ChatGPT Agent (codename Odyssey) which merges Deep Research's fast-reading text browser with Operator's clicky visual browser and terminal access, all RL-tuned to pick tools smartly. It browses, codes, calls APIs (Google Drive, GitHub, etc., if you connect), generates images, and builds spreadsheets/slides—handling interruptions, clarifications, and takeovers for collaboration. SOTA jumps: 41.6% on Humanities Last Exam (double O3), 27.4% on FrontierMath, 45.5% on SpreadsheetBench, 68.9% on BrowseComp.

These are insane jumps in capabilities folks, just... mindblowing that we can now have agents that are SO good!

The team demoed wedding planning (outfits, hotels, gifts with weather/venue checks), sticker design/ordering, and an MLB itinerary spreadsheet—wild to watch it chain thoughts on recordings.

Wolfram called it the official start of agent year; Yam hyped the product polish (mobile control!); Nisten noted it's packaged perfection over DIY. I refreshed ChatGPT obsessively—mind-blown at turning my phone into a task master. Available now for Pro/Plus/Team (400/40 queries/month), Enterprise soon. This is the "feel the AGI" moment Sam mentioned—game over for tedious tasks (OpenAI announcement: https://openai.com/index/introducing-chatgpt-agent/).

I've yet to get access to it, but I'm very much looking forward to testing it out and letting you guys know how it works!

Combining the two browser modes (visual that has my cookies and textual that can scan tons of websites super quick) + CLI + deep research abilities + RL for the right kind of tool use all sounds incredibly intriguing!

Vision & Video

Runway’s Act-Two: Motion Capture Gets a Major Upgrade (X, YouTube)

Runway’s latest drop, Act-Two, is a next-gen motion capture model that’s got creatives buzzing. It tracks head, face, body, and hands with insane fidelity, animating any character from a single performance video. It’s a huge leap from Act-One, already in use for film, VFX, and gaming, and available now to enterprise and creative customers with a full rollout soon.

Voice & Audio

Mistral’s Voxtral: Open Speech Recognition Champ (X, HF)

Mistral AI is killing it with Voxtral, a state-of-the-art open speech recognition model. With Voxtral Small at 24B for production and Mini at 3B for edge devices, it outperforms OpenAI’s Whisper large-v3 across English and multilingual tasks like French, Spanish, Hindi, and German. Supporting up to 32K token context (about 30-40 minutes of audio), it offers summarization and Q&A features, all under an Apache 2.0 license. At just $0.001 per minute via API, it’s a steal for real-time or batch transcription.

Tools

Liquid AI’s LEAP and Apollo: On-Device AI for All

Liquid AI is bringing AI to your pocket with LEAP, a developer platform for building on-device models, and Apollo, a lightweight iOS app to run small LLMs locally. We’re talking 50-300MB models optimized for minimal battery drain and instant inference, no cloud needed. It’s privacy-focused and plug-and-play, perfect for offline workflows on Android and iOS. Developers, this is your prototyping dream—join the community via X.

Amazon Kiro: Your Spec-Driven Coding Buddy

I’ve already touched on Amazon’s Kiro, but let me reiterate—this spec-driven AI IDE is a standout. It structures your dev process around requirements, letting you define projects in plain language or diagrams before coding starts. It automates docs, testing, and more, feeling like a technical PM guiding you from concept to production. Early users are hooked on its PRD mode, and it’s free during preview. Give it a spin—details on X.

Wrapping Up: An Unforgettable AI Birthday Bash

What a week, folks! From Kimi K2 redefining open-source power to OpenAI’s ChatGPT Agent ushering in a new era of task automation, this has been a whirlwind of innovation. Throw in Grok’s quirky waifus and our own W&B Inference launch, and I’m left speechless on my birthday. Sharing this with over 30,000 of you during our live stream was the ultimate gift—AI is moving at a pace I couldn’t have dreamed of when I started ThursdAI. Here’s to more breakthroughs, and I can’t wait to see what you build with Kimi K2 credits. Let’s keep pushing the boundaries together!

P.S - If you'd like to support this podcast/newsletter and give me a birthday present, the best way is to tell your friends about it and the second best way is to subscribe 👏

TL;DR and Show Notes

Here’s everything we covered this week on ThursdAI for July 17, 2025, packed with links and key highlights for you to dive deeper:

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Open Source LLMs

* Moonshot launches Kimi K2 - a 1T param MoE crushing SWE Bench Verified at 65.8% (X post, HuggingFace, API & docs, GitHub)

* Teknium drops Hermes 3 dataset - nearly 1M samples for training agentic models (X)

* LGAI EXAONE-4.0 - hybrid attention, 32B & 1.2B models with 131K+ context (X, HuggingFace)

* Big CO LLMs + APIs

* OpenAI’s ChatGPT Agent - unified agentic AI for real-world tasks, scoring 41.6% on HLE (Announcement)

* Grok 4 waifus - XAI adds animated characters, topping Japan’s App Store

* Mira Murati’s Thinking Machines Lab - $2B funding for open AI science (X)

* Gemini Embedding Model - #1 on MTEB with 68.32 score (X, Dev Blog)

* Amazon S3 Vectors - preview for vector storage, up to 90% cost savings (X)

* This Week’s Buzz

* Kimi K2 on W&B Inference - open, scalable production access, $50 credits with “K2KoolAid” (X, Docs)

* Wolfram’s Evaluation of W&B service (X)

* Vision & Video

* Runway’s Act-Two - next-gen motion capture for head, face, body, hands (X, YouTube)

* Voice & Audio

* Mistral’s Voxtral - open SOTA speech recognition, beats Whisper v3 (X, HuggingFace)

* AI Art & Diffusion & 3D

* OpenAI image service API adds high-quality mode (X)

* Tools

* Liquid AI’s LEAP & Apollo - on-device AI for mobile, privacy-first (X)

* Amazon’s Kiro - spec-driven AI IDE, free in preview (X)

Don't you just love "new top LLM" drop weeks? I sure do! This week, we had a watch party for Grok-4, with over 20K tuning in to watch together, as the folks at XAI unveiled their newest and best model around. Two models in fact, Grok-4 and Grok-4 Heavy.

We also had a very big open source week, we had the pleasure to chat with the creators of 3 open source models on the show, first with Elie from HuggingFace who just released SmoLM3, then with our friend Maxime Labonne who together with Liquid released a beautiful series of tiny on device models.

Finally we had a chat with folks from Reka AI, and as they were on stage, someone in their org published a new open source Reka Flash model 👏 Talk about Breaking News right on the show!

It was a very fun week and a great episode, so grab your favorite beverage and let me update you on everything that's going on in AI (as always, show notes at the end of the article)

Open Source LLMs

As always, even on big weeks like this, we open the show with Open Source models first and this week, the western world caught up to the Chinese open source models we saw last week!

HuggingFace SmolLM3 - SOTA fully open 3B with dual reasoning and long-context (𝕏, HF)

We had Eli Bakouch from Hugging Face on the show and you could feel the pride radiating through the webcam. SmolLM 3 isn’t just “another tiny model”; it’s an 11-trillion-token monster masquerading inside a 3-billion-parameter body. It reasons, it follows instructions, and it does both “think step-by-step” and “give me the answer straight” on demand. Hugging Face open-sourced every checkpoint, every dataset recipe, every graph in W&B – so if you ever wanted a fully reproducible, multi-lingual pocket assistant that fits on a single GPU, this is it.

They achieved the long context (128 K today, 256 K in internal tests) with a NoPE + YaRN recipe and salvaged the performance drop by literally merging two fine-tunes at 2 a.m. the night before release. Science by duct-tape, but it works: SmolLM 3 edges out Llama-3.2-3B, challenges Qwen-3, and stays within arm’s reach of Gemma-3-4B – all while loading faster than you can say “model soup.” 🤯

Liquid AI’s LFM2: Blazing-Fast Models for the Edge (𝕏, Hugging Face)

We started the show and I immediately got to hit the #BREAKINGNEWS button, as Liquid AI dropped LFM2, a new series of tiny (350M-1.2B) models focused on Edge devices.

We then had the pleasure to host our friend Maxime Labonne, head of Post Training at Liquid AI, to come and tell us all about this incredible effort!

Maxime, a legend in the model merging community, explained that LFM2 was designed from the ground up for efficiency. They’re not just scaled-down big models; they feature a novel hybrid architecture with convolution and attention layers specifically optimized for running on CPUs and devices like the Samsung Galaxy S24.

Maxime pointed out that Out of the box, they won't replace ChatGPT, but when you fine-tune them for a specific task like translation, they can match models 60 times their size. This is a game-changer for creating powerful, specialized agents that run locally. Definitely a great release and on ThursdAI of all days!

Mistrals updated Devstral 1.1 Smashes Coding Benchmarks (𝕏, HF)

Mistral didn't want to be left behind on this Open Source bonanza week, and also, today, dropped an update to their excellent coding model Devstral.

With 2 versions, an open weights Small and API-only Medium model, they have claimed an amazing 61.6% score on Swe Bench and the open source Small gets a SOTA 53%, the highest among the open source models! 10 points higher than the excellent DeepSwe we covered just last week!

The thing to watch here is the incredible price performance, with this model beating Gemini 2.5 Pro and Claude 3.7 Sonnet while being 8x cheaper to run!

DevStral small comes to us with an Apache 2.0 license, which we always welcome from the great folks at Mistral!

Big Companies LLMs and APIs

There's only 1 winner this week, it seems that other foundational labs were very quiet to see what XAI is going to release.

XAI releases Grok-4 and Grok-4 heavy - the world leading reasoning model (𝕏, Try It)

Wow, what a show! Space uncle Elon together with the XAI crew, came fashionably late to their own stream, and unveiled the youngest but smartest brother of the Grok family, Grok 4 plus a multiple agents swarm they call Grok Heavy. We had a watch party with over 25K viewers across all streams who joined and watched together, this, fairly historic event!

Why historic? Well, for one, they have scaled RL (Reinforcement Learning) for this model significantly more than any other lab did so far, which resulted in an incredible reasoner, able to solve HLE (Humanity's Last Exam) benchmark at an unprecedented 50% (while using tools)

The other very much unprecedented result, is on the ArcAGI benchmark, specifically V2, which is designed to be very easy for humans and very hard for LLMs, Grok-4 got an incredible 15.9%, almost 2x better than Opus 4 the best performing model before it! (ArcAGI president Greg Kamradt says it Grok-4 shows signs of Fluid Intelligence!)

Real World benchmarks

Of course, academic benchmarks don't tell the full story, and while it's great to see that Grok-4 gets a perfect 100% on AIME25 and a very high 88.9% on GPQA Diamond, the most interesting benchmark they've showed was the Vending-Bench. This is a very interesting new benchmark from AndonLabs, where they simulate a vending machine, and let an LLM manage it, take orders, restock and basically count how much money a model can make while operating a "real" business.

Grok scored a very significant $4K profit, selling 4569 items, 4x more than Opus, which shows a real impact on real world tasks!

Not without controversy

Grok-4 release comes just 1 day after Grok-3 over at X, started calling itself MechaHitler and started spewing Nazi Antisemitic propaganda, which was a very bad episode. We've covered the previous "misalignment" from Grok, and this seemed even worse. Many examples (which XAI folks deleted) or Grok talking about Antisemitic tropes, blaming people with Jewish surnames for multiple things and generally acting jailbroken and up to no good.

Xai have addressed the last episode by a token excuse, supposedly open sourcing their prompts, which were updated all of 4 times in the last 2 month, while addressing this episode with a "we noticed, and we'll add guardrails to prevent this from happening"

IMO this isn't enough, Grok is consistently (this is the 3rd time on my count) breaking alignment, way more than other foundational LLMs, and we must ask for more transparency for a model as significant and as widely used as this! And to my (lack of) surprise

First principles thinking == Elon's thoughts?

Adding insult to injury, while Grok-4 was just launched, some folks asked it thoughts on the Israel-Palestine conflict and instead of coming up with an answer on its own, Grok-4 did a X search to see what Elon Musk things on this topic to form its opinion. It's so so wrong to claim a model is great at "first principles" and have the first few tests from folks, show that Grok defaults to see "what Elon thinks"

Look, I'm all for "moving fast" and of course I love AI progress, but we need to ask more from the foundational labs, especially given the incredible amount of people who count on these models more and more!

This weeks Buzz

We're well over 300 registrations to our hackathon at the Weights & Biases SF officess this weekend (July 12-13) and I'm packing my suitcase after writing this, as I'm excited to see all the amazing projets folks will build to try and win over $15K in prizes including an awesome ROBODOG

Not to late to come and hack with us, register at lu.ma/weavehacks

Tools – Browsers grow brains

Perplexity’s Comet landed on my Mac and within ten minutes it was triaging my LinkedIn invites by itself. This isn’t a Chrome extension; it’s a Chromium fork where natural-language commands are first-class citizens. Tell it “find my oldest unread Stripe invoice and download the PDF” and watch the mouse move. The Gmail connector lets you ask, “what flights do I still need to expense?” and get a draft report. Think Cursor, but for every tab.

I benchmarked Comet against OpenAI Operator on my “scroll Alex’s 200 tweet bookmarks, extract the juicy links, drop them into Notion” task—Operator died halfway, Comet almost finished. Almost. The AI browser war has begun; Chrome’s Mariner project and OpenAI’s rumored Chromium team better move fast.

Comet is available to Perplexity MAX subscribers now, and will come to pro subscribers with invites soon, as soon as I'll have them I'll tell you how to get one!

Vision & Video

Reka dropped in with a double-whammy of announcements. First, they showcased Reka Vision, an agentic platform that can search, analyze, and even edit your video library using natural language. The demo of it automatically generating short-form social media reels from long videos was super impressive.

Then, in a surprise live reveal, they dropped Reka Flash 3.1, a new 21B parameter open-source multimodal model! It boasts great performance on coding and math benchmarks, including a 65% on AIME24. It was awesome to see them drop this right on the show.

We also saw LTX Video release three new open-source LoRAs for precise video control (Pose, Depth, and Canny), and Moonvalley launched Marey, a video model for filmmakers that's built exclusively on licensed, commercially-safe data—a first for the industry.

Veo3 making talking pets

Google have released an update to VEO 3, allowing you to upload an image and have the characters in the image say what you want! It’s really cool for human like generations, but it’s way more fun to animate… your pets! Here’s two of the best doggos in Colorado presenting themselves!

The full prompt to create your own after you upload an image was:

Two dogs presenting themselves, the left one barking first and then saying "Hey, I'm George Washington Fox" and the right dog following up with a woof and then says "and I'm his younger brother, Dr Emmet Brown".

Then both are saying "we're good boys" and barking

Both should sound exiting with an american accent and a dog accent

Phew, what a week! From open source Breaking News from the folks who trained the models right on the podcast, to watch parties and Nazi LLMs, this has been one hell of a ride!

Next week, there are already rumors of a potential Gemini 3 release, the OpenAI open source model is rumored to be dropping, and I'm sure we'll get all kinds of incredible things lined up + it's going to be my birthday on Thursday so, looking forward!

See you next week 🫡

Show notes and Links

TL;DR of all topics covered:

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed) @ryancarson

* Guests

* Elie Bakouch - Training at Hugging Face (@eliebakouch)

* Maxime Labonne - Head of postrainig and Liquid AI (@maximelabonne) author of LLM-Course

* Mattia Atzeni - Member of Technical Staff @ Reka

* Meenal Nalwaya - Head of Product, Reka Al

* Open Source LLMs

* HuggingFace - SmolLM3 SOTA, fully open-source 3B dual-mode reasoning and long-context support (X, HF)

* Liquid AI launches LFM2: the fastest, most efficient open-source edge LLMs yet (X, HF)

* Reachy Mini: Hugging Face and Pollen Robotics launch a $299 open-source desktop robot (X, HF)

* NextCoder-32B: Microsoft’s new code-editing LLM rivals GPT-4o on complex code tasks (Microsoft Research, HF)

* Mistral AI updates Devstral Small 1.1 and Devstral Medium, setting new open-source coding agent benchmarks (X, HF, Blog)

* Reka updates RekaFlash 1.1 (HF)

* Big CO LLMs + APIs

* 👑 Grok 4 Release: A Historic Leap from XAI - Grok 4 and Grok 4 heavy X

* Grok 3 is going nazi racing on X - MeinPrompt gate (X)

* Gemini API Batch Mode launches with 50% cost savings for large-scale AI jobs (X, Google Blog)

* This weeks Buzz

* W&B Hackathon is nearing capacity - Robodog is ready to be given out (lu.ma/weavehacks)

* Vision & Video

* Reka Vision: Multimodal Agent for Visual Understanding and Search (Reka on X, Vision app)

* LTX Video launches 3 open-source LoRAs for video control: Pose, Depth, Canny (LTX Studio on X, GitHub, HF model)

* Marey by Moonvalley: the first professional, licensed AI video tool built for creative control (Moonvalley on X, Product page)

* Tools

* Perplexity Launches Comet: The AI-Powered Browser for Modern Productivity (X, HF)

Welcome back to another mind-blowing week on ThursdAI! We’re diving into the first show of the second half of 2025, and let me tell you, AI is not slowing down. This week, we’ve got a massive wave of open-source models from Chinese giants like Baidu and Tencent that are shaking up the game, Meta’s jaw-dropping hiring spree with Zuck assembling an AI dream team, and Microsoft’s medical AI outperforming doctors on the toughest cases. Plus, a real-time AI game engine that had me geeking out on stream. Buckle up, folks, because we’ve got a lot to unpack!

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

We had incredible guests like Michael Luo from Agentica, dropping knowledge on RL coding agents, and Ivan Burazin from Daytona, revealing the infrastructure powering the agent era. We had an incredible episode this week, with over 8,000 views for the live show (as always, Links and Show notes in the end, and the YT live video is here for your convienience if you'd prefer watching)

Open Source AI & LLMs: The Chinese Powerhouse Wave

Man, if there’s one takeaway from this week, it’s that Chinese companies are absolutely dominating the open-source LLM scene. Let’s break down the heavy hitters that dropped this week and why they’ve got everyone talking.

Baidu’s ERNIE 4.5: A Suite of 10 Models to Rule Them All

Baidu, a giant in the Chinese tech space, just flipped the script by open-sourcing their ERNIE 4.5 series. We’re talking 10 distinct models ranging from a whopping 424 billion parameters down to a tiny 0.3 billion. With an Apache 2.0 license, 128K context window, and multimodal capabilities handling image, video, and text input, this is a massive drop. Their biggest Mixture-of-Experts (MoE) model, with 47B active parameters, even outshines OpenAI’s o1 on visual knowledge tasks like DocVQA, scoring 93% compared to o1’s 81%!

What’s wild to me is Baidu’s shift. They’ve been running ERNIE in production for years—think chatbots and more across their ecosystem—but they weren’t always open-source fans. Now, they’re not just joining the party, they’re hosting it. If you’re into tinkering, this is your playground—check it out on Hugging Face (HF) or dive into their technical paper (Paper).

Tencent’s Hunyuan-A13B-Instruct: WizardLM Team Strikes Again

Next up, Tencent dropped Hunyuan-A13B-Instruct, and oh boy, does it have a backstory. This 80B parameter MoE model (13B active at inference) comes from the legendary WizardLM team, poached from Microsoft after a messy saga where their killer models got yanked from the internet over “safety concerns.” I remember the frustration—we were all hyped, then bam, gone. Now, under Tencent’s wing, they’ve cooked up a model with a 256K context window, hybrid fast-and-slow reasoning modes, and benchmarks that rival DeepSeek R1 and OpenAI o1 on agentic tasks. It scores an impressive 87% on AIME 2024, though it dips to 76% on 2025, hinting at some overfitting quirks. Though for a 12B active parameters model this all is still VERY impressive.

Here’s the catch—the license. It excludes commercial use in the EU, UK, and South Korea, and bans usage if you’ve got over 100M active users. So, not as open as we’d like, but for its size, it’s a beast that fits on a single machine, making it a practical choice for many. They’ve also released two datasets, ArtifactsBench and C3-Bench, for code and agent evaluation. I’m not sold on the name—Hunyuan doesn’t roll off the tongue for Western markets—but the WizardLM pedigree means it’s worth a look. Try it out on Hugging Face (HF) or test it directly (Try It).

Huawei’s Pangu Pro MoE: Sidestepping Sanctions with Ascend NPUs

Huawei entered the fray with Pangu Pro MoE, a 72B parameter model with 16B active per token, and here’s what got me hyped—it’s trained entirely on their own Ascend NPUs, not Nvidia or AMD hardware. This is a bold move to bypass US sanctions, using 4,000 of these chips to preprocess 13 trillion tokens. The result? Up to 1,528 tokens per second per card with speculative decoding, outpacing dense models in speed and cost-efficiency. Performance-wise, it’s close to DeepSeek and Qwen, making it a contender for those outside the Nvidia ecosystem.

I’m intrigued by the geopolitical angle here. Huawei’s proving you don’t need Western tech to build frontier models, and while we don’t know who’s got access to these Ascend NPUs, it’s likely a game-changer for Chinese firms. Licensing isn’t as permissive as MIT or Apache, but it’s still open-weight. Peek at it on Hugging Face (HF) for more details.

DeepSWE-Preview: RL Coding Agent Hits 59% on SWE-Bench

Switching gears, I was blown away chatting with Michael Luo from Agentica about DeepSWE-Preview, an open-source coding agent trained with reinforcement learning (RL) on Qwen3-32B. This thing scored a stellar 59% on SWE-Bench-Verified (42.2% Pass@1, 71% Pass@16), one of the top open-weight results out there. What’s cool is they did this without distilling from proprietary giants like Claude—just pure RL over six days on 64 H100 GPUs. Michael shared how RL is surging because pre-training hits data limits, and DeepSWE learned emergent behaviors like paranoia, double-checking edge cases to avoid shaky fixes.

This underdog story of academic researchers breaking benchmarks with limited resources is inspiring. They’ve open-sourced everything—code, data, logs—making it a goldmine for the community. I’m rooting for them to get more compute to push past even higher scores. Dive into the details on their blog (Notion) or check the model on Hugging Face (HF Model).

This Week’s Buzz from Weights & Biases: come Hack with Us! 🔥

As always, I’ve got some exciting news from Weights & Biases to share. We’re hosting the first of our Weavehacks hackathons in San Francisco on July 12-13. It’s all about agent protocols like MCP and A2A, and I’m stoked to you guys in person—come say hi for a high-five! We’ve got cool prizes, including a custom W&B RoboDog that’s been a conference hit, plus $13-14K in cash. Spots are filling fast, so register now and we'll let you in (Sign Up).

We’re also rolling out Online Evaluations in Weave, letting you monitor LLM apps live with judge agents on production data—super handy for catching hiccups. And our inference service via CoreWeave GPUs offers free credits for open-source model testing. Want in or curious about Weave’s tracing tools? Reach out to me anywhere, and I’ll hook you up. Can’t wait to demo this next week!

Big Companies & APIs: AI’s NBA Draft and Medical Marvels

Shifting to the big players, this week felt like an AI sports season with blockbuster hires and game-changing releases. From Meta’s talent poaching to Microsoft’s medical breakthroughs, let’s unpack the drama and innovation.

Meta Superintelligence Labs: Zuck’s Dream Team Draft

Imagine an AI NBA draft—that’s what Meta’s up to with their new Superintelligence Labs (MSL). Led by Alex Wang (formerly of Scale AI) and Nat Friedman (ex-GitHub CEO), MSL is Zuck’s power move after Llama 4’s lukewarm reception. They’ve poached up to 10 key researchers from OpenAI, including folks behind GPT-4’s image generation and o1’s foundations, with comp packages rumored at $100M for the first year and up to $300M over four years. That’s more than many Meta execs or even Tim Cook’s salary! They’ve also snagged talent from Google DeepMind and even tried to acquire Ilya Sutskever’s SSI outright (to which he said he's flattered but no)

This is brute force at its finest, and I’m joking that I didn’t get a $100M offer myself—ThursdAI’s still waiting for that email, Zuck! OpenAI’s Sam Altman fired back with “missionaries beat mercenaries,” hinting at a culture clash, while Mark Chen felt like Meta “broke into their house and took something” It’s war, folks, and I’m hyped to see if MSL delivers a Llama that crushes it. With FAIR and GenAI folding under this new crack team of 50, plus Meta’s GPU arsenal, the stakes are sky-high.

If you're like to see the list of "mercenaries" worth over 100M, you can see who they are and their achievements here

Cursor’s Killer Hires and Web Expansion

Speaking of talent wars, Cursor (built by AnySphere) just pulled off a stunner by hiring Boris Cherny and Cat Wu, key creators of Claude Code, as Chief Architect and Head of Product. This skyrockets Cursor’s cred in code generation, and I’m not surprised—Claude Code was a side project that exploded, and now Cursor’s got the brains behind it. On top of that, they’ve rolled out AI coding agents to web and mobile, even integrating with Slack. No more being tied to your desktop—launch, monitor, and collab on code tasks anywhere.

The lines between native and web tools are blurring fast, and Cursor’s leading the charge. I haven’t tested the Slack bit yet, but if you have, hit me up in the comments. This, plus their recent $20M raise, shows they’re playing to win. Learn more at (Cursor).

Microsoft MAI-DxO: AI Diagnoses Better Than Doctors

Now, onto something that hits close to home for me—Microsoft’s MAI-DxO, an AI system that’s outdiagnosing doctors on open-ended medical cases. On 304 of the toughest New England Journal of Medicine cases, it scored 85.5% accuracy, over four times the 20% rate of experienced physicians. I’ve had my share of frustrating medical waits, and seeing AI step in as a tool for doctors—not a replacement—gets me excited for the future.

It’s an orchestration of models simulating a virtual clinician panel, asking follow-up questions, ordering tests, and even factoring in cost controls for diagnostics. This isn’t just acing multiple-choice; it handles real-world ambiguity. My co-host Yam and I stressed—don’t skip your doctor for ChatGPT, but expect your doc to be AI-superpowered soon. Read more on Microsoft’s blog (Blog).

ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Cloudflare’s One-Click AI Bot Block: Protecting the Internet

Cloudflare made waves with a one-click feature to block AI bots and scrapers, available to all customers, even free-tier ones. With bots like Bytespider and GPTBot hitting nearly 40% of top sites, but only 3% blocking them, this addresses a huge shift. I’m with the CEO here—the old internet deal was Google scraping for traffic; now, AI summaries keep users from clicking through, breaking monetization for creators. Yam suggested a global license for training data with royalties, and I’m curious if that’s the future. For now, Cloudflare’s ML detects even sneaky bots spoofing as browsers. Big move—check their announcement (X) and the cool website goodaibots.com

Cypher Alpha: Mystery 1M Context Model on OpenRouter

Lastly, a mysterious 1M context model, Cypher Alpha, popped up on OpenRouter for free testing (with data logging). It’s fast at 70 tokens/sec, low latency, but not a reasoning model—refusals on basic queries stumped me. Speculation points to Amazon Titan, which would be a surprise entry. I’m intrigued by who’s behind this—Gemini, OpenAI, and Qwen hit 1M context, but Amazon? Let’s see. Try it yourself (Link).

Vision & Video: Mirage’s AI-Native Game Engine Blows Minds 🤯

Okay, folks, I’ve gotta geek out here. Dynamics Lab unveiled the world’s first AI-native user-generated content (UGC) game engine, live with playable demos like a GTA-style “Urban Chaos” and a racing “Coastal Drift.” Running at 16 frames per second, it generates photorealistic worlds in real-time via natural language or controller input. You can jump, run, fight, or drive, and even upload an image to spawn a new game environment on the fly.

What’s nuts is there’s no pre-built game behind this—it’s infinite, custom content created as you play. I was floored showing this on stream; it’s obviously not perfect with clipping and delays, but we’re witnessing the dawn of personalized gaming. You gotta try this—head to their site for the demos (Playable Demo).

This brings us even more closer to the "every pixel will be generated" dream of Jensen Huang.

Voice & Audio: TTS Gets Real with Kyutai and Qwen

This week brought fresh text-to-speech (TTS) updates that hint at smarter conversational AI down the line. Kyutai TTS, from the French team behind Moshi, dropped with ultra-low latency (220ms first-token) and high speaker similarity (77.1% English, 78.7% French), plus a word error rate of just 2.82% in English. It’s production-ready with a Rust server and voice cloning from a 10-second clip—perfect for LLM-integrated apps. Check it out (X Announcement, HF Model).

Qwen-TTS from Alibaba also launched, focusing on Chinese dialects like Pekingese and Shanghainese, but with English support too. It’s got human-level naturalness via API, though less relevant for our English audience. Still, it’s a solid step—see more (X Post). Both are pieces of the puzzle for richer virtual interactions, and I’m pumped to see where this goes.

Infrastructure for Agents: Daytona’s Sandbox Revolution

I’m thrilled to have chatted with Ivan Burazin from Daytona, a cloud provider delivering agent-native runtimes—or sandboxes—that give agents their own computers for tasks like code execution or data analysis. They’ve hit over $1M in annualized run rate just two months post-launch, with 15,000 signups and 1,500 credit cards on file. That’s insane growth for infrastructure, which usually ramps slowly due to integration delays.

Why’s this hot? 2025 is the year of agents, and as Ivan shared, even OpenAI and Anthropic recently redefined agents as needing runtimes. From YC’s latest batch (37% building agents) to Cursor’s web move, every task may soon spin up a sandbox. Daytona’s “stateful serverless” tech spins fast, lasts long, and scales across regions like the US, UK, Germany, and India, addressing latency and GDPR needs. If you’re building agents, this is your unsung hero—explore it at (Daytona IO) and grab $200 in credits, or up to $50K for startups (Startups).

Wrapping Up: AI’s Relentless Pace

What a week, folks! From Chinese open-source titans like ERNIE 4.5 and Hunyuan-A13B redefining accessibility, to Meta’s blockbuster hires signaling an AI arms race, and Microsoft’s MAI-DxO paving the way for smarter healthcare, we’re witnessing AI’s relentless acceleration. Mirage’s game engine and Daytona’s sandboxes remind us that creativity and infrastructure are just as critical as models themselves. I’m buzzing with anticipation for what’s next—will Meta’s dream team deliver? Will agents redefine every app? Stick with ThursdAI to find out. See you next week for more!

TL;DR and Show Notes

Here’s the quick rundown of everything we covered this week, packed with links to dive deeper:

* Show Notes & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Guests - Ivan Burazin (Daytona), Michael Luo (Agentica)

* Open Source LLMs

* Baidu’s ERNIE 4.5 Series - 10 models, 424B to 0.3B, multimodal, beats o1 on DocVQA (X, HF, Paper)

* Tencent’s Hunyuan-A13B-Instruct - 80B total, 13B active, 256K context, WizardLM legacy (X, HF, Try It)

* Huawei’s Pangu Pro MoE - 72B, trained on Ascend NPUs, 1,528 tokens/sec (X, HF)

* DeepSWE-Preview - RL agent, 59% SWE-Bench-Verified on Qwen3-32B (Notion, HF Model)

* This Week’s Buzz

* Weights & Biases Weavehacks Hackathon - SF, July 12-13, agent protocols focus (Sign Up)

* Big CO LLMs + APIs

* Meta Superintelligence Labs (MSL) - Zuck hires dream team, up to $300M comp packages from OpenAI talent (list)

* Cursor - Hires Claude Code creators, web/mobile agents with Slack (Cursor, HF)

* Microsoft MAI-DxO - 85.5% accuracy on NEJM cases vs. 20% for doctors (X, Blog)

* Cloudflare - One-click AI bot blocking, tackles scraping economics (X)

* Cypher Alpha - Mystery 1M context model, possibly Amazon Titan (Link)

* Gemini Pro 2.5 - Returned to Google’s free tier

* Vision & Video

* Mirage - AI-native UGC game engine, real-time photorealistic demos (Playable Demo)

* Workflow - Restyle videos with Flux Kontext and Luma Modify (X)

* Voice & Audio

* Kyutai TTS - Low-latency, high similarity in EN/FR (X, HF)

* Qwen-TTS - Bilingual Chinese/English, human-level naturalness (X, HF)

* Infrastructure

* Daytona - Agent-native sandboxes, $1M run rate in 2 months (GitHub, Startups)

* Tools

* Chai Discovery’s Chai-2 - Zero-shot antibody design (Chai Discovery)

Thanks for reading all the way through ThursdAI, folks! Share this with friends to spread the AI love, and I’ll catch you next week for more!

So... no long blogpost with analysis this week, but I'll def. recommend tuning in to the show that the folks ran, they had a few guests on, and even got some breaking news (new Flux Kontext that's open source)

Of course many of you are readers and are here for the links, so I'm including the raw TL;DR + speaker notes as prepared by the folks for the show!

P.S - our (rescheduled) hackathon is coming up in San Francisco, on July 12-13 called WeaveHacks, if you're interested at a chance to win a RoboDog, welcome to join us and give it a try. Register HERE

Ok, that's it for this week, please enjoy the show and see you next week!

ThursdAI - June 26th, 2025 - TL;DR

* Hosts and Guests

* WolframRvnwlf - Host (@WolframRvnwlf)

* Co-Hosts - @yampeleg, @nisten, @ldjconfirmed

* Guest - Jason Kneen (@jasonkneen) - Discussing MCPs, coding tools, and agents

* Guest - Hrishioa (@hrishioa) - Discussing agentic coding and spec-driven development

* Open Source LLMs

* Mistral Small 3.2 released with improved instruction following, reduced repetition & better function calling (X)

* Unsloth AI releases dynamic GGUFs with fixed chat templates (X)

* Kimi-VL-A3B-Thinking-2506 multimodal model updated for better video reasoning and higher resolution (Blog)

* Chinese Academy of Science releases Stream-Omni, a new Any-to-Any model for unified multimodal input (HF, Paper)

* Prime Intellect launches SYNTHETIC-2, an open reasoning dataset and synthetic data generation platform (X)

* Big CO LLMs + APIs

* Google

* Gemini CLI, a new open-source AI agent, brings Gemini 2.5 Pro to your terminal (Blog, GitHub)

* Google reduces free tier API limits for previous generation Gemini Flash models (X)

* Search Live with voice conversation is now rolling out in AI Mode in the US (Blog, X)

* Gemini API is now faster for video and PDF processing with improved caching (Docs)

* Anthropic

* Claude introduces an "artifacts" space for building, hosting, and sharing AI-powered apps (X)

* Federal judge rules Anthropic's use of books for training Claude qualifies as fair use (X)

* xAI

* Elon Musk announces the successful launch of Tesla's Robotaxi (X)

* Microsoft

* Introduces Mu, a new language model powering the agent in Windows Settings (Blog)

* Meta

* Report: Meta pursued acquiring Ilya Sutskever's SSI, now hires co-founders Nat Friedman and Daniel Gross (X)

* OpenAI

* OpenAI removes mentions of its acquisition of Jony Ive's startup 'io' amid a trademark dispute (X)

* OpenAI announces the release of DeepResearch in API + Webhook support (X)

* This weeks Buzz

* Alex is on vacation; WolframRvnwlf is attending AI Tinkerers Munich on July 25 (Event)

* Join W&B Hackathon happening in 2 weeks in San Francisco - grand prize is a RoboDog! (Register for Free)

* Vision & Video

* MeiGen-MultiTalk code and checkpoints for multi-person talking head generation are released (GitHub, HF)

* Google releases VideoPrism for generating adaptable video embeddings for various tasks (HF, Paper, GitHub)

* Voice & Audio

* ElevenLabs launches 11.ai, a voice-first personal assistant with MCP support (Sign Up, X)

* Google Magenta releases Magenta RealTime, an open weights model for real-time music generation (Colab, Blog)

* ElevenLabs launches a mobile app for iOS and Android for on-the-go voice generation (X)

* AI Art & Diffusion & 3D

* Google rolls out Imagen 4 and Imagen 4 Ultra in the Gemini API and Google AI Studio (Blog)

* OmniGen 2 open weights model for enhanced image generation and editing is released (Project Page, Demo, Paper)

* Tools

* OpenMemory Chrome Extension provides shared memory across ChatGPT, Claude, Gemini and more (X)

* LM Studio adds MCP support to connect local LLMs with your favorite servers (Blog)

* Cursor is now available as a Slack integration (Dashboard)

* All Hands AI releases the OpenHands CLI, a model-agnostic, open-source coding agent (Blog, Docs)

* Warp 2.0 launches as an Agentic Development Environment with multi-threading (X)

* Studies and Others

* The /r/LocalLLaMA subreddit is back online after a brief moderation issue (Reddit, News)

* Andrej Karpathy's talk "Software 3.0" discusses the future of programming in the age of AI (YouTube, Summary)

Thank you, see you next week!

This week, while not the busiest week in releases (we can't get a SOTA LLM every week now can we), was full of interesting open source releases, and feature updates such as the chatGPT meetings recorder (which we live tested on the show, the limit is 2 hours!)

It was also a day after our annual W&B conference called FullyConnected, and so I had a few goodies to share with you, like answering the main question, when will W&B have some use of those GPUs from CoreWeave, the answer is... now! (We launched a brand new preview of an inference service with open source models)

And finally, we had a great chat with Pankaj Gupta, co-founder and CEO of Yupp, a new service that lets users chat with the top AIs for free, while turning their votes into leaderboards for everyone else to understand which Gen AI model is best for which task/topic. It was a great conversation, and he even shared an invite code with all of us (I'll attach to the TL;DR and show notes, let's dive in!)

00:00 Introduction and Welcome

01:04 Show Overview and Audience Interaction

01:49 Special Guest Announcement and Experiment

03:05 Wolfram's Background and Upcoming Hosting

04:42 TLDR: This Week's Highlights

15:38 Open Source AI Releases

32:34 Big Companies and APIs

32:45 Google's Gemini Updates

42:25 OpenAI's Latest Features

54:30 Exciting Updates from Weights & Biases

56:42 Introduction to Weights & Biases Inference Service

57:41 Exploring the New Inference Playground

58:44 User Questions and Model Recommendations

59:44 Deep Dive into Model Evaluations

01:05:55 Announcing Online Evaluations via Weave

01:09:05 Introducing Pankaj Gupta from YUP.AI

01:10:23 YUP.AI: A New Platform for Model Evaluations

01:13:05 Discussion on Crowdsourced Evaluations

01:27:11 New Developments in Video Models

01:36:23 OpenAI's New Transcription Service

01:39:48 Show Wrap-Up and Future Plans

Here's the TL;DR and show notes links

ThursdAI - June 19th, 2025 - TL;DR

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

* Guest - @pankaj - co-founder of Yupp.ai

* Open Source LLMs

* Moonshot AI open-sourced Kimi-Dev-72B (Github, HF)

* MiniMax-M1 456B (45B Active) - reasoning model (Paper, HF, Try It, Github)

* Big CO LLMs + APIs

* Google drops Gemini 2.5 Pro/Flash GA, 2.5 Flash-Lite in Preview ( Blog, Tech report, Tweet)

* Google launches Search Live: Talk, listen and explore in real time with AI Mode (Blog)

* OpenAI adds MCP support to Deep Research in chatGPT (X, Docs)

* OpenAI launches their meetings recorder in mac App (docs)

* Zuck update: Considering bringing Nat Friedman and Daniel Gross to Meta (information)

* This weeks Buzz

* NEW! W&B Inference provides a unified interface to access and run top open-source AI models (inference, docs)

* NEW! W&B Weave Online Evaluations delivers real-time production insights and continuous evaluation for AI agents across any cloud. (X)

* The new platform offers "metal-to-token" observability, linking hardware performance directly to application-level metrics.

* Vision & Video

* ByteDance new video model beats VEO3 - Seedance.1.0 mini (Site, FAL)

* MiniMax Hailuo 02 - 1080p native, SOTA instruction following (X, FAL)

* Midjourney video is also here - great visuals (X)

* Voice & Audio

* Kyutai launches open-source, high-throughput streaming Speech-To-Text models for real-time applications (X, website)

* Studies and Others

* LLMs Flunk Real-World Coding Contests, Exposing a Major Skill Gap (Arxiv)

* MIT Study: ChatGPT Use Causes Sharp Cognitive Decline (Arxiv)

* Andrej Karpathy's "Software 3.0": The Dawn of English as a Programming Language (youtube, deck)

* Tools

* Yupp launches with 500+ AI models, a new leaderboard, and a user-powered feedback economy - use thursdai link* to get 50% extra credits

* BrowserBase announces director.ai - an agent to run things on the web

* Universal system prompt for reduction of hallucination (from Reddit)

*Disclosure: while this isn't a paid promotion, I do think that yupp has a great value, I do get a bit more credits on their platform if you click my link and so do you. You can go to yupp.ai and register with no affiliation if you wish.

This week was full of crazy AI news, both model related but also shifts in the AI landscape and big companies, with Zuck going all in on scale & execu-hiring Alex Wang for a crazy $14B dollars.

OpenAI meanwhile, maybe received a new shipment of GPUs? Otherwise, it’s hard to explain how they have dropped the o3 price by 80%, while also shipping o3-pro (in chat and API).

Apple was also featured in today’s episode, but more so for the lack of AI news, completely delaying the “very personalized private Siri powered by Apple Intelligence” during WWDC25 this week.

We had 2 guests on the show this week, Stefania Druga and Eric Provencher (who builds RepoPrompt). Stefania helped me cover the AI Engineer conference we all went to last week, and shared some cool Science CoPilot stuff she’s working on, while Eric is the GOTO guy for O3-pro helped us understand what this model is great for!

As always, TL;DR and show notes at the bottom, video for those who prefer watching is attached below, let’s dive in!

Big Companies LLMs & APIs

Let’s start with big companies, because the landscape has shifted, new top reasoner models dropped and some huge companies didn’t deliver this week!

Zuck goes all in on SuperIntelligence - Meta’s $14B stake in ScaleAI and Alex Wang

This may be the most consequential piece of AI news today. Fresh from the dissapointing results of LLama 4, reports of top researchers leaving the Llama team, many have decided to exclude Meta from the AI race. We have a saying at ThursdAI, don’t bet against Zuck!

Zuck decided to spend a lot of money (nearly 20% of their reported $65B investment in AI infrastructure) to get a 49% stake in Scale AI and bring Alex Wang it’s (now former) CEO to lead the new Superintelligence team at Meta.

For folks who are not familiar with Scale, it’s a massive company in providing human annotated data services to all the big AI labs, Google, OpenAI, Microsoft, Anthropic.. all of them really. Alex Wang, is the youngest self made billionaire because of it, and now Zuck not only has access to all their expertise, but also to a very impressive AI persona, who could help revive the excitement about Meta’s AI efforts, help recruit the best researchers, and lead the way inside Meta.

Wang is also an outspoken China hawk who spends as much time in congressional hearings as in Slack, so the geopolitics here are … spicy. Meta just stapled itself to the biggest annotation funnel on Earth, hired away Google’s Jack Rae (who was on the pod just last week, shipping for Google!) for brainy model alignment, and started waving seven-to-nine-figure comp packages at every researcher with “Transformer” in their citation list. Whatever disappointment you felt over Llama-4’s muted debut, Zuck clearly felt it too—and responded like a founder who still controls every voting share.

OpenAI’s Game-Changer: o3 Price Slash & o3-pro launches to top the intelligence leaderboards!

Meanwhile OpenAI dropping not one, but two mind-blowing updates. First, they’ve slashed the price of o3—their premium reasoning model—by a staggering 80%. We’re talking from $40/$10 per million tokens down to just $8/$2. That’s right, folks, it’s now in the same league as Claude Sonnet cost-wise, making top-tier intelligence dirt cheap. I remember when a price drop of 80% after a year got us excited; now it’s 80% in just four months with zero quality loss. They’ve confirmed it’s the full o3 model—no distillation or quantization here. How are they pulling this off? I’m guessing someone got a shipment of shiny new H200s from Jensen!

And just when you thought it couldn’t get better, OpenAI rolled out o3-pro, their highest intelligence offering yet. Available for pro and team accounts, and via API (87% cheaper than o1-pro, by the way), this model—or consortium of models—is a beast. It’s topping charts on Artificial Analysis, barely edging out Gemini 2.5 as the new king. Benchmarks are insane: 93% on AIME 2024 (state-of-the-art territory), 84% on GPQA Diamond, and nearing a 3000 ELO score on competition coding. Human preference tests show 64-66% of folks prefer o3-pro for clarity and comprehensiveness across tasks like scientific analysis and personal writing.

I’ve been playing with it myself, and the way o3-pro handles long context and tough problems is unreal. As my friend Eric Provencher (creator of RepoPrompt) shared on the show, it’s surgical—perfect for big refactors and bug diagnosis in coding. It’s got all the tools o3 has—web search, image analysis, memory personalization—and you can run it in background mode via API for async tasks. Sure, it’s slower due to deep reasoning (no streaming thought tokens), but the consistency and depth? Worth it.

Oh, and funny story—I was prepping a talk for Hamel Hussain’s evals course, with a slide saying “don’t use large reasoning models if budget’s tight.” The day before, this price drop hits, and I’m scrambling to update everything. That’s AI pace for ya!

Apple WWDC: Where’s the Smarter Siri?

Oh Apple. Sweet, sweet Apple. Remember all those Bella Ramsey ads promising a personalized Siri that knows everything about you? Well, Craig Federighi opened WWDC by basically saying "Yeah, about that smart Siri... she's not coming. Don't wait up."

Instead, we got:

* AI that can combine emojis (revolutionary! 🙄)

* Live translation (actually cool)

* Direct API access to on-device models (very cool for developers)

* Liquid glass UI (pretty but... where's the intelligence?)

The kicker? Apple released a paper called "The Illusion of Thinking" right before WWDC, basically arguing that AI reasoning models hit hard complexity ceilings. Some saw this as Apple making excuses for why they can't ship competitive AI. The timing was... interesting.

During our recording, Nisten's Siri literally woke up randomly when we were complaining about how dumb it still is. After a decade, it's the same Siri. That moment was pure comedy gold.

This Week's Buzz

Our premium conference Fully Connected is happening June 17-18 in San Francisco! Use promo code WBTHURSAI to register for free. We'll have updates on the CoreWeave acquisition, product announcements, and it's the perfect chance to give feedback directly to the people building the tools you use.

Also, my talk on Large Reasoning Models as LLM judges is now up on YouTube. Had to update it live because of the O3 price drop - such is life in AI!

Open Source LLMs: Mistral Goes Reasoning Mode

Mistral Drops Magistral - Their First Reasoning Model

The French champagne of LLMs is back! Mistral released Magistral, their first reasoning model, in two flavors: a 24B parameter open-source Small version and a closed API-only Medium version. And honestly? The naming continues to be chef's kiss - Mistral really has the branding game locked down.

Now, here's where it gets spicy. Mistral's benchmarks notably don't include comparisons to Chinese models like Qwen or DeepSeek. Dylan Patel from SemiAnalysis called them out on this, and when he ran the comparisons himself, well... let's just say Magistral Medium barely keeps up with Qwen's tiny 4B parameter model on math benchmarks. Ouch.

But here's the thing - and Nisten really drove this home during our discussion - benchmarks don't tell the whole story. He's been using Magistral Small for his workflows and swears by it. "It's almost at the point where I don't want to tell people about it," he said, which is the highest praise from someone who runs models locally all day. The 24B Small version apparently hits that sweet spot for local deployment while being genuinely useful for real work.

The model runs on a single RTX 4090 or a 32GB MacBook after quantization, has a 128K context window (though they recommend capping at 40K), and uses a transparent mode that shows its reasoning process. It's Apache 2.0 licensed, multilingual, and available through their Le Chat interface with "Flash Answers" for real-time reasoning.

SakanaAI's Text2Lora: The Future is Self-Adapting Models

This one blew my mind. SakanaAI (co-founded by one of the Transformer paper authors) released Text2Lora - a method for adapting LLMs to new tasks using ONLY text descriptions. No training data needed!

Think about this: instead of fine-tuning a model with thousands of examples to make it better at math, you just... tell it to be better at math. And it works! On Llama 3.1 8B, Text2Lora reaches 77% average accuracy, outperforming all baseline methods.

What this means is we're approaching a world where models can essentially customize themselves on-the-fly for whatever task you throw at them. As Nisten put it, "This is revolutionary. The model is actually learning, actually changing its own weights." We're just seeing the first glimpses of this capability, but in 6-12 months?

🎥 Multimedia & Tools: Video, Voice, and Browser Breakthroughs

Let’s zip through some multimedia and tool updates that caught my eye this week. Google’s VEO3-fast is a creator’s dream—2x faster 720p video generation, 80% cheaper, and now with audio support. I’ve seen clips on social media (like an NBA ad) that are unreal, though Wolfram noted it’s not fully rolled out in Europe yet. You can access it via APIs like Fail or Replicate, and I’m itching to make a full movie if I had the budget!

Midjourney’s gearing up for a video product with their signature style, but they’re also facing heat—Disney and Universal are suing them for copyright infringement over Star Wars and Avengers-like outputs. It’s Hollywood’s first major strike against AI, and while I get the IP concern, it’s odd they picked the smaller player when OpenAI and Google are out there too. This lawsuit could drag on, so stay tuned.

OpenAI’s new advanced voice mode dropped, aiming for a natural cadence with better multilingual support (Russian and Hebrew sound great now). But honestly? I’m not loving the breathing and laughing they added—it’s uncanny valley for me. Some folks on X are raving, though, and LDJ noted it’s closing the gap to Sesame’s Maya. I just wish they’d let me pick between old and new voices instead of switching under my feet. If OpenAI’s listening, transparency please!

On the tools side, Yutori’s Scouts got my timeline buzzing—AI agents that monitor the web for any topic (like “next ThursdAI release”) and notify you of updates. I saw a demo catching leadership changes at xAI, and it’s the future of web interaction. Couldn’t log in live on the show (email login woes—give me passwords, folks!), but it’s beta on yutori.com. Also, Browser Company finally launched DIA, an AI-native browser in beta. Chatting with open tabs, rewriting text, and instant answers? I’ve been using it to prep for ThursdAI, and it’s pretty slick. Try it at diabrowser.com.

Wrapping Up: AI’s Breakneck Pace

What a week, folks! From OpenAI democratizing intelligence with o3-pro and price cuts to Meta’s bold superintelligence play with ScaleAI, we’re witnessing history unfold at lightning speed. Apple’s stumble at WWDC stings, but open-source gems and new tools keep the excitement alive. I’m still riding the high from AI Engineer last week—your high-fives and feedback mean the world. Next week, don’t miss Weights & Biases’ Fully Connected conference in SF on June 18-19. I won’t be there physically, but I’m cheering from afar—grab your spot at fullyconnected.com with promo code WBTHURSAI for a sweet deal.

Thanks for being part of the ThursdAI crew. Here’s the full TL;DR and show notes to catch anything you missed. See you next week!

TL;DR of all topics covered:

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

* Guests -

* Stefania Druga @stefania_druga (Independent, Former Research Scientist Google DeepMind),Creator of scratch copilot, and AI Engineer education summit.

* Eric Provencher - @pvncher (Building RepoPrompt)

* Chit Chat - AI Engineer conference vibes, meeting fans, Jack Rae’s move to Meta.

* Open Source LLMs

* Mistral Magistral - 24B reasoning model (X, HF, Blog)

* HuggingFace Screensuite - GUI agents evaluation framework (HF)

* SakanaAI Text2Lora - Instant, Task-Specific LLM Adaptation (Github)

* Big CO LLMs + APIs

* OpenAI drops o3 price by 90% (Blog)

* OpenAI launches o3-pro - highest intelligence model (X)

* Meta buys 49% stake in ScaleAI, Alex Wang heads superintelligence team (Blog, Axios)

* Apple WWDC updates - pause on Apple Intelligence in iOS26, live translation, on-device APIs

* Apple paper on reasoning as illusion (Paper, Rebuttal)

* This Week’s Buzz

* Fully Connected: W&B’s 2-day conference, June 17-18 in SF (fullyconnected.com) - Promo Code WBTHURSAI

* Alex’s talk on LRM as LLM judges on Hamel’s course (YT)

* Vision & Video

* VEO3-fast - 2x faster 720p generations, 80% cheaper

* Midjourney to launch video product (X)

* Topaz Astra - creative 4K video upscaler (X, Site)

* Voice & Audio

* OpenAI’s new advanced voice mode - mixed responses, better multilingual support

* Cartesia Ink-Whisper - optimized for real-time chat (Blog)

* AI Art & Diffusion & 3D

* Disney & Universal sue Midjourney - first Hollywood vs AI lawsuit (NBC)

* Krea releases KREA-1 - custom image gen model (X)

* AI Tools

* Yutori Scouts - AI agents for web monitoring (Blog)

* BrowserCompany DIA - AI-native browser in beta (Link)

What an incredible episode this week, we recorded live from floor 30th at the Marriott in SF, while Yam was doing live correspondence from the floor of the AI Engineer event, all while Swyx, the cohost of Latent Space podcast, and the creator of AI Engineer (both the conference and the concept itself) joined us for the whole stream - here’s the edited version, please take a look.

We've had around 6500 people tune in, and at some point we got 2 surprise guests, straight from the keynote stage, Logan Kilpatrick (PM for AI Studio and lead cheerleader for Gemini) and Jack Rae (principal scientist working on reasoning) joined us for a great chat about Gemini! Mind was absolutely blown!

They have just launched the new Gemini 2.5 Pro and I though it would only be fitting to let their new model cover this podcast this week (so below is fully AI generated ... non slop I hope). The show notes and TL;DR is as always in the end.

Okay, enough preamble… let's dive into the madness!

🤯 Google Day at AI Engineer: New Gemini 2.5 Pro and a Look Inside the Machine's Mind

For the first year of this podcast, a recurring theme was us asking, "Where's Google?" Well, it's safe to say that question has been answered with a firehose of innovation. We were lucky enough to be joined by Google DeepMind's Logan Kilpatrick and Jack Rae, the tech lead for "thinking" within Gemini, literally moments after they left the main stage.

Surprise! A New Gemini 2.5 Pro Drops Live

Logan kicked things off with a bang, officially announcing a brand new, updated Gemini 2.5 Pro model right there during his keynote. He called it "hopefully the final update to 2.5 Pro," and it comes with a bunch of performance increases, closing the gap on feedback from previous versions and hitting SOTA on benchmarks like Aider.

It's clear that the organizational shift to bring the research and product teams together under the DeepMind umbrella is paying massive dividends. Logan pointed out that Google has seen a 50x increase in AI inference over the past year. The flywheel is spinning, and it's spinning fast.

How Gemini "Thinks"

Then things got even more interesting. Jack Rae gave us an incredible deep dive into what "thinking" actually means for a language model. This was one of the most insightful parts of the conference for me.

For years, the bottleneck for LLMs has been test-time compute. Models were trained to respond immediately, applying a fixed amount of computation to go from a prompt to an answer, no matter how hard the question. The only way to get a "smarter" response was to use a bigger model.

Jack explained that "Thinking" shatters this limitation. Mechanically, Gemini now has a "thinking stage" where it can generate its own internal text—hypothesizing, testing, correcting, and reasoning—before committing to a final answer. It's an iterative loop of computation that the model can dynamically control, using more compute for harder problems. It learns how to think using reinforcement learning, getting a simple "correct" or "incorrect" signal and backpropagating that to shape its reasoning strategies.

We're already seeing the results of this. Jack showed a clear trend: as models get better at reasoning, they're also using more test-time compute. This paradigm also gives developers a "thinking budget" slider in the API for Gemini 2.5 Flash and Pro, allowing a continuous trade-off between cost and performance.

The future of this is even wilder. They're working on DeepThink, a high-budget mode for extremely hard problems that uses much deeper, parallel chains of thought. On the tough USA Math Olympiad, where the SOTA was negligible in January, 2.5 Pro reached the 50th percentile of human participants. DeepThink pushes that to the 65th percentile.

Jack’s ultimate vision is inspired by the mathematician Ramanujan, who derived incredible theorems from a single textbook by just thinking deeply. The goal is for models to do the same—contemplate a small set of knowledge so deeply that they can push the frontiers of human understanding. Absolutely mind-bending stuff.

🤖 MorphLabs and the Audacious Quest for Verified Superintelligence

Just when I thought my mind couldn't be bent any further, we were joined by Jesse Han, the founder and CEO of MorphLabs. Fresh off his keynote, he laid out one of the most ambitious visions I've heard: building the infrastructure for the Singularity and developing "verified superintelligence."

The big news was that Christian Szegedy is joining MorphLabs as Chief Scientist. For those who don't know, Christian is a legend—he invented batch norm and adversarial examples, co-founded XAI, and led code reasoning for Grok. That's a serious hire.

Jesse’s talk was framed around a fascinating question: "What does it mean to have empathy for the machine?" He argues that as AI develops personhood, we need to think about what it wants. And what it wants, according to Morph, is a new kind of cloud infrastructure.

This is MorphCloud, built on a new virtualization stack called Infinibranch. Here’s the key unlock: it allows agents to instantaneously snapshot, branch, and replicate their entire VM state. Imagine an agent reaching a decision point. Instead of choosing one path, it can branch its entire existence—all its processes, memory, and state—to explore every option in parallel. It can create save states, roll back to previous checkpoints, and even merge its work back together.

This is a monumental step for agentic AI. It moves beyond agents that are just a series of API calls to agents that are truly embodied in complex software environments. It unlocks the potential for recursive self-improvement and large-scale reinforcement learning in a way that's currently impossible. It’s a bold, sci-fi vision, but they're building the infrastructure to make it a reality today.

🔥 The Agent Conversation: OpenAI, MCP, and Magic Moments

The undeniable buzz on the conference floor was all about agents. You couldn't walk ten feet without hearing someone talking about agents, tools, and MCP.

OpenAI is leaning in here too. This week, they made their Codex coding agent available to all ChatGPT Plus users and announced that ChatGPT will soon be able to listen in on your Zoom meetings. This is all part of a broader push to make AI more active and integrated into our workflows.

The MCP (Model-Context-Protocol) track at the conference was packed, with lines going down the hall. (Alex here, I had a blast talking during that track about MCP observability, you can catch our talk here on the live stream of AI Engineer)

Logan Kilpatrick offered a grounded perspective, suggesting the hype might be a bit overblown but acknowledging the critical need for an open standard for tool use, a void left when OpenAI didn't formalize ChatML.

I have to share my own jaw-dropping MCP moment from this week. I was coding an agent using an IDE that supports MCP. My agent, which was trying to debug itself, used an MCP tool to check its own observability traces on the Weights & Biases platform. While doing so, it discovered a new tool that our team had just added to the MCP server—a support bot. Without any prompting from me, my coding agent formulated a question, "chatted" with the support agent to get the answer, came back, fixed its own code, and then re-checked its work. Agent-to-agent communication, happening automatically to solve a problem. My jaw was on the floor. That's the magic of open standards.

This Week's Buzz from Weights & Biases

Speaking of verification and agents, the buzz from our side is all about it! At our booth here at AI Engineer, we have a Robodog running around, connected to our LLM evaluation platform, W&B Weave. As Jesse from MorphLabs discussed, verifying what these complex agentic systems are doing is critical. Whether it's superintelligence or your production application, you need to be able to evaluate, trace, and understand its behavior. We're building the tools to do just that.

And if you're in San Francisco, don't forget our own conference, Fully Connected, is happening on June 18th and 19th! It's going to be another amazing gathering of builders and researchers. Fullyconnected.com get in FREE with the promo code WBTHURSAI

What a show. The energy, the announcements, the sheer brainpower in one place was something to behold. We’re at a point where the conversation has shifted from theory to practice, from hype to real, tangible engineering. The tracks on agents and enterprise adoption were overflowing because people are building, right now. It was an honor and a privilege to bring this special episode to you all.

Thank you for tuning in. We'll be back to our regular programming next week! (and Alex will be back to writing his own newsletter, not send direct AI output!)

AI News TL;DR and show notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @swyx @yampeleg @romechenko

* Guests - @officialLoganK, @jack_w_rae

* Open Source LLMs

* ByteDance / ContentV-8B - (HF)

* Big CO LLMs + APIs

* Gemini Pro 2.5 updated Jun 5th (X)

* SOTA on HLE, Aider, and GPQA

* Now supports thinking budgets

* Same cost, on pareto frontier

* Closes gap on 03-25 regressions

* OAI AVM injects ads and stopped singing (X)

* OpenAI Codex is now available to plus members and has internet access (X)

* ~24,000 NEW PRs overnight from Codex after @OpenAI expands access to free users.

* OpenAI will record meetings and released connectors like (X)

* TestingCatalog News 🗞@testingcatalogJun 4, 2025

OpenAI released loads of connectors for Team accounts! Most of these connectors can be used for Deep Research, while Google Drive, SharePoint, Dropbox and Box could be used in all chats. https://t.co/oBEmYGKguE

* Anthropic cuts windsurf access for Windsurf (X)

* Without warning, Anthropic cuts off Windsurf from official Claude 3 and 4 APIs

* This weeks Buzz

* FULLY - CONNECTED - Fully Connected: W&B's 2-day conference, June 18-19 in SF fullyconnected.com - Promo Code WBTHURSAI

* Vision & Video

* VEO3 is now available via API on FAL (X)

* Captions launches Mirage Studio - talking avatars competition to HeyGen/Hedra (X)

* Voice & Audio

* ElevenLabs model V3 - supports emotion tags and is "inflection point" (X)

* Supporting 70+ languages, multi-speaker dialogue, and audio tags such as [excited], [sighs], [laughing], and [whispers].

* Tools

* Cursor Launched V1 - Bug Bot reviews PRs, iPython notebooks and one clickMCP

* 24,000 NEW PRs overnight from Codex after @OpenAI expands access to plus users (X)

Welcome back to another absolutely wild week in AI! I'm coming to you live from the Fontainebleau Hotel in Vegas at the Imagine AI conference, and wow, what a perfect setting to discuss how AI is literally reimagining our world. After last week's absolute explosion of releases (Claude Opus 4, Google I/O madness, OpenAI Codex and Jony colab), this week gave us a chance to breathe... sort of. Because even in a "quiet" week, we still got a new DeepSeek model that's pushing boundaries, and the entire internet discovered that we might all just be prompts. Yeah, it's been that kind of week!

Before we dive in, quick shoutout to everyone who joined us live - we had some technical hiccups with the Twitter Spaces audio (sorry about that!), but the YouTube stream was fire. And speaking of fire, we had two incredible guests join us: Charlie Holtz from Chorus (the multi-model chat app that's changing how we interact with AI) and Linus Eckenstam, who's been traveling the AI conference circuit and bringing us insights from the frontlines of the generative AI revolution.

Open Source AI & LLMs: DeepSeek Whales & Mind-Bending Papers

DeepSeek dropped R1-0528 out of nowhere, an update to their reasoning beast with some serious jumps in performance. We’re talking AIME at 91 (beating previous scores by a mile), LiveCodeBench at 73, and SWE verified at 57.6. It’s edging closer to heavyweights like o3, and folks on X are already calling it “clearer thinking.” There was hype it might’ve been R2, but the impact didn’t quite crash the stock exchange like past releases. Still, it’s likely among the best open-weight models out there.

So what's new? Early reports and some of my own poking around suggest this model "thinks clearer now." Nisten mentioned that while previous DeepSeek models sometimes liked to "vibe around" and explore the latent space before settling on an answer, this one feels a bit more direct.

And here’s the kicker—they also released an 8B distilled version based on Qwen3, runnable on your laptop. Yam called it potentially the best 8B model to date, and you can try it on Ollama right now. No need for a monster rig!

The Mind-Bending "Learning to Reason Without External Rewards" Paper

Okay, this paper result broke my brain, and apparently everyone else's too. This paper shows that models can improve through reinforcement learning with its own intuition of whether or not it's correct. 😮

It's like the placebo effect for AI! The researchers trained models without telling them what was good or bad, but rather, utilized a new framework called Intuitor, where the reward was based on how the "self certainty".

The thing that took my whole timeline by storm is, it works! GRPO (Group Policy Optimization) - the framework that DeepSeek gave to the world with R1 is based on external rewards (human optimize) and Intuitor seems to be mathcing or even exceeding some of GRPO results when Qwen2.5 3B was used to finetune. Incredible incredible stuff

Big Companies LLMs & APIs

Claude Opus 4: A Week Later – The Dev Darling?

Claude Opus 4, whose launch we celebrated live on the show, has had a week to make its mark. Charlie Holtz, who's building Chorus (more on that amazing app in a bit!), shared that while it's sometimes "astrology" to judge the vibes of a new model, Opus 4 feels like a step change, especially in coding. He mentioned that Claude Code, powered by Opus 4 (and Sonnet 4 for implementation), is now tackling GitHub issues that were too complex just weeks ago. He even had a coworker who "vibe coded three websites in a weekend" with it – that's a tangible productivity boost!

Linus Eckenstam highlighted how Lovable.dev saw their syntax error rates plummet by nearly 50% after integrating Claude 4. That’s quantifiable proof of improvement! It's clear Anthropic is leaning heavily into the developer/coding space. Claude Opus is now #1 on the LMArena WebDev arena, further cementing its reputation.

I had my own magical moment with Opus 4 this week. I was working on an MCP observability talk for the AI Engineer conference and trying to integrate Weave (our observability and evals framework at Weights & Biases) into a project. Using Windsurf's Cascade agent (which now lets you bring your own Opus 4 key, by the way – good move, Windsurf!), Opus 4 not only tried to implement Weave into my agent but, when it got stuck, it figured out it had access to the Weights & Biases support bot via our MCP tool. It then formulated a question to the support bot (which is also AI-powered!), got an answer, and used that to fix the implementation. It then went back and checked if the Weave trace appeared in the dashboard! Agents talking to agents to solve a problem, all while I just watched – my jaw was on the floor. Absolutely mind-blowing.

Quick Hits: Voice Updates from OpenAI & Anthropic

OpenAI’s Advanced Voice Mode finally sings—yes, I’ve been waiting for this! It can belt out tunes like Mariah Carey, which is just fun. Anthropic also rolled out voice mode on mobile, keeping up in the conversational race. Both are cool steps, but I’m more hyped for what’s next in voice AI—stay tuned below (OpenAI X, Anthropic X).

🐝 This Week's Buzz: Weights & Biases Updates!

Alright, time for a quick update from the world of Weights & Biases!

* Fully Connected is Coming! Our flagship 2-day conference, Fully Connected, is happening on June 18th and 19th in San Francisco. It's going to be packed with amazing speakers and insights into the world of AI development. You can still grab tickets, and as a ThursdAI listener, use the promo code WBTHURSAI for a 100% off ticket! I hustled to get yall this discount! (Register here)

* AI Engineer World's Fair Next Week! I'm super excited for the AI Engineer conference in San Francisco next week. Yam Peleg and I will be there, and we're planning another live ThursdAI show from the event! If you want to join the livestream or snag a last-minute ticket, use the coupon code THANKSTHURSDAI for 30% off (Get it HERE)

Vision & Video: Reality is Optional Now

VEO3 and the Prompt Theory Phenomenon

Google's VEO3 has completely taken over TikTok with the "Prompt Theory" videos. If you haven't seen these yet, stop reading and watch ☝️. The concept is brilliant - AI-generated characters discussing whether they're "made of prompts," creating this meta-commentary on consciousness and reality.

The technical achievement here is staggering. We're not just talking about good visuals - VEO3 nails temporal consistency, character emotions, situational awareness (characters look at whoever's speaking), perfect lip sync, and contextually appropriate sound effects.

Linus made a profound point - if not for the audio, VEO3 might not have been as explosive. The combination of visuals AND audio together is what's making people question reality. We're seeing people post actual human videos claiming they're AI-generated because the uncanny valley has been crossed so thoroughly.

Odyssey's Interactive Worlds: The Holodeck Prototype

Odyssey dropped their interactive video demo, and folks... we're literally walking through AI-generated worlds in real-time. This isn't a game engine rendering 3D models - this is a world model generating each frame as you move through it with WASD controls.

Yes, it's blurry. Yes, I got stuck in a doorway. But remember Will Smith eating spaghetti from two years ago? The pace of progress is absolutely insane. As Linus pointed out, we're at the "GAN era" of world models. Combine VEO3's quality with Odyssey's interactivity, and we're looking at completely personalized, infinite entertainment experiences.

The implications that Yam laid out still have me shook - imagine Netflix shows completely customized to you, with your context and preferences, generated on the fly. Not just choosing from a catalog, but creating entirely new content just for you. We're not ready for this, but it's coming fast.

Hunyuan's Open Source Avatar Revolution

While the big companies are keeping their video models closed, Tencent dropped two incredible open source releases: HunyuanPortrait and HunyuanAvatar. These are legitimate competitors to Hedra and HeyGen, but completely open source.

HunyuanPortrait does high-fidelity portrait animation from a single image plus video. HunyuanAvatar goes further with 1 image + audio, and lipsync, body animation, multi-character support, and emotion control.

Wolfram tested these extensively and confirmed they're "state of the art for open source." The portrait model is basically perfect for deepfakes (use responsibly, people!), while the avatar model opens up possibilities for AI assistants with consistent visual presence.

🖼️ AI Art & Diffusion

Black Forest Labs drops Flux Kontext - SOTA image editing!

This came as massive breaking news during the show (thought we didn't catch it live!) - Black Forest Labs, creators of Flux, dropped an incredible Image Editing model called Kontext (really, 3 models, Pro, Max and 12B open source Dev in private preview). The are consistent, context aware text and image editing! Just see the below example

If you used GPT-image to Ghiblify yourself, or VEO, you know that those are not image editing models, your face will look different every generation. These images model keep you consistent, while adding what you wanted. This character consistency is something many folks really want and it's great to see Flux innovating and bringing us SOTA again and are absolutely crushing GPT-image in instruction following, character preservation and style reference!

Maybe the most important thing about this model is the increible speed. While the Ghiblification chatGPT trend took the world by storm, GPT images are SLOW! Check out the speed comparisons on Kontext!

You can play around with these models on the new Flux Playground, but they also already integrated into FAL, FreePik, Replicate, Krea and tons of other services!

🎙️ Voice & Audio: Everyone Gets a Voice

Unmute.sh: Any LLM Can Now Talk

KyutAI (the folks behind Moshi) are back with Unmute.sh - a modular wrapper that adds voice to ANY text LLM. The latency is incredible (under 300ms), and it includes semantic VAD (knowing when you've paused for thought vs. just taking a breath).

What's brilliant about this approach is it preserves all the capabilities of the underlying text model while adding natural voice interaction. No more choosing between smart models and voice-enabled models - now you can have both!

It's going to be open sourced at some point soon, and while awesome, Unmute did have some instability in how the voice sounds! It answered to me with 1 type of voice and then during the same conversation, answered with another, you can give it a tru yourself at unmute.sh

Chatterbox: Open Source Voice Agents for Everyone

Resemble AI open sourced Chatterbox, featuring zero-shot voice cloning from just 5 seconds of audio and unique emotion intensity control. Playing with the demo where they could dial up the emotion from 0.5 to 2.0 on the same text was wild - from calm to absolutely unhinged Samuel L. Jackson energy.

This being a .5B param model is great, The issue I always have, is that with my fairly unique accent, these models sound like a British Alex all the time, and I just don't talk like that!

Though the fact that this runs locally and includes safety features (profanity filters, content classifiers and something called PerTh watermarking) while being completely open source is exactly what the ecosystem needs. We're rapidly approaching a world where anyone can build sophisticated voice agents.👏

Looking Forward: The Convergence is Real

As we wrapped up the show, I couldn't help but reflect on the massive convergence happening across all these modalities. We have LLMs getting better at reasoning (even with random rewards!), video models breaking reality, voice models becoming indistinguishable from humans, and it's all happening simultaneously.

Charlie's comment that "we are the prompts" might have been said in jest, but it touches on something profound. As these models get better at generating realistic worlds, characters, and voices, the line between generated and real continues to blur. The Prompt Theory videos aren't just entertainment - they're a mirror reflecting our anxieties about AI and consciousness.

But here's what keeps me optimistic: the open source community is keeping pace. DeepSeek, Hunyuan, ResembleAI, and others are ensuring that these capabilities don't remain locked behind corporate walls. The democratization of AI continues, even as the capabilities become almost magical.

Next week, I'll be at AI Engineer World's Fair in San Francisco, finally meeting Yam face-to-face and bringing you all the latest from the biggest AI engineering conference of the year. Until then, keep experimenting, keep building, and remember - in this exponential age, today's breakthrough is tomorrow's baseline.

Stay curious, stay building, and I'll see you next ThursdAI! 🚀

Show Notes & TL;DR Links

Show Notes & Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co-Hosts - @WolframRvnwlf (@WolframRvnwlf), @yampeleg (@yampeleg) @nisten (@nisten)

* Guests - Charlie Holtz (@charliebholtz]), Linus Eckenstam (@LinusEkenstam @LinusEkenstam)

* Open Source LLMs

* DeepSeek-R1-0528 - Updated reasoning model with AIME 91, LiveCodeBench 73 (Try It)

* Learning to Reason Without External Rewards - Paper on random rewards improving models (X)

* HaizeLabs j1-nano & j1-micro - Tiny reward models (600M, 1.7B params), RewardBench 80.7% for micro (Tweet, GitHub, HF-micro, HF-nano)

* Big CO LLMs + APIs

* Claude Opus 4 - #1 on LMArena WebDev, coding step change (X)

* Mistral Agents API - Framework for custom tool-using agents (Blog, Tweet)

* Mistral Embed SOTA - New state-of-the-art embedding API (X)

* OpenAI Advanced Voice Mode - Now sings with new capabilities (X)

* Anthropic Voice Mode - Released on mobile for conversational AI (X)

* This Week’s Buzz

* Fully Connected - W&B conference, June 18-19, SF, promo code WBTHURSAI (Register)

* AI Engineer World’s Fair - Next week in SF, 30% off with THANKSTHURSDAI (Register)

* AI Art & Diffusion

* BFL Flux Kontext - SOTA image editing model for identity-consistent edits (Tweet, Announcement)

* Vision & Video

* VEO3 Prompt Theory - Viral AI video trend questioning reality on TikTok (X)

* Odyssey Interactive Video - Real-time AI world exploration at 30 FPS (Blog, Try It)

* HunyuanPortrait - High-fidelity portrait video from one photo (Site, Paper)

* HunyuanVideo-Avatar - Audio-driven full-body avatar animation (Site, Tweet)

* Voice & Audio

* Unmute.sh - KyutAI’s voice wrapper for any LLM, low latency, soon open-source (Try It, X)

* Chatterbox - Resemble AI’s open-source voice cloning with emotion control (GitHub, HF)

* Tools

* Opera NEON - Agent-centric AI browser for autonomous web tasks (Site, Tweet)

And folks, after the last week was the calm before the storm, "The storm came, y'all" – that's an understatement. This wasn't just a storm; it was an AI hurricane, a category 5 of announcements that left us all reeling (in the best way possible!). From being on the ground at Google I/O to live-watching Anthropic drop Claude 4 during our show, it's been an absolute whirlwind.

This week was so packed, it felt like AI Christmas, with tech giants and open-source heroes alike showering us with gifts. We saw OpenAI play their classic pre-and-post-Google I/O chess game, Microsoft make some serious open-source moves, Google unleash an avalanche of updates, and Anthropic crash the party with Claude 4 Opus and Sonnet live stream in the middle of ThursdAI!

So buckle up, because we're about to try and unpack this glorious chaos. As always, we're here to help you collectively know, learn, and stay up to date, so you don't have to. Let's dive in! (TL;DR and links in the end)

Open Source LLMs Kicking Things Off

Even with the titans battling, the open-source community dropped some serious heat this week. It wasn't the main headline grabber, but the releases were significant!

Gemma 3n: Tiny But Mighty Matryoshka

First up, Google's Gemma 3n. This isn't just another small model; it's a "Nano-plus" preview, a 4-billion parameter MatFormer (Matryoshka Transformer – how cool is that name?) model designed for mobile-first multimodal applications. The really slick part? It has a nested 2-billion parameter sub-model that can run entirely on phones or Chromebooks.

Yam was particularly excited about this one, pointing out the innovative "model inside another model" design. The idea is you can use half the model, not depth-wise, but throughout the layers, for a smaller footprint without sacrificing too much. It accepts interleaved text, image, audio, and video, supports ASR and speech translation, and even ships with RAG and function-calling libraries for edge apps. With a 128K token window and responsible AI features baked in, Gemma 3n is looking like a powerful tool for on-device AI. Google claims it beats prior 4B mobile models on MMLU-Lite and MMMU-Mini. It's an early preview in Google AI Studio, but it definitely flies on mobile devices.

Mistral & AllHands Unleash Devstral 24B

Then we got a collaboration from Mistral and AllHands: Devstral, a 24-billion parameter, state-of-the-art open model focused on code. We've been waiting for Mistral to drop some open-source goodness, and this one didn't disappoint.Nisten was super hyped, noting it beats o3-Mini on SWE-bench verified – a tough benchmark! He called it "the first proper vibe coder that you can run on a 3090," which is a big deal for coders who want local power and privacy. This is a fantastic development for the open-source coding community.

The Pre-I/O Tremors: OpenAI & Microsoft Set the Stage

As we predicted, OpenAI couldn't resist dropping some news right before Google I/O.

OpenAI's Codex Returns as an Agent

OpenAI launched Codex – yes, that Codex, but reborn as an asynchronous coding agent. This isn't just a CLI tool anymore; it connects to GitHub, does pull requests, fixes bugs, and navigates your codebase. It's powered by a new coding model fine-tuned for large codebases and was SOTA on SWE Agent when it dropped. Funnily, the model is also called Codex, this time, Codex-1.

And this gives us a perfect opportunity to talk about the emerging categories I'm seeing among Code Generator agents and tools:

* IDE-based (Cursor, Windsurf): Live pair programming in your editor

* Vibe coding (Lovable, Bolt, v0): "Build me a UI" style tools for non-coders

* CLI tools (Claude Code, Codex-cli): Terminal-based assistants

* Async agents (Claude Code, Jules, Codex, GitHub Copilot agent, Devin): Work on your repos while you sleep, open pull requests for you to review, async

Codex (this new one) falls into category number 4, and with today's release, Cursor seems to also strive to get to category number 4 with background processing.

Microsoft BUILD: Open Source Copilot and Copilot Agent Mode

Then came Microsoft Build, their huge developer conference, with a flurry of announcements.The biggest one for me? GitHub Copilot's front-end code is now open source! The VS Code editor part was already open, but the Copilot integration itself wasn't. This is a massive move, likely a direct answer to the insane valuations of VS Code clones like Cursor. Now, you can theoretically clone GitHub Copilot with VS Code and swing for the fences.

GitHub Copilot also launched as an asynchronous coding assistant, very similar in function to OpenAI's Codex, allowing it to be assigned tasks and create/update PRs. This puts Copilot right into category 4 of code assistants, and with the native Github Integration, they may actually have a leg up in this race!

And if that wasn't enough, Microsoft is adding MCP (Model Context Protocol) support directly into the Windows OS. The implications of having the world's biggest operating system natively support this agentic protocol are huge.

Google I/O: An "Ultra" Event Indeed!

Then came Tuesday, and Google I/O. I was there in the thick of it, and folks, it was an absolute barrage. Google is shipping. The theme could have been "Ultra" for many reasons, as we'll see.

First off, the scale: Google reported a 49x increase in AI usage since last year's I/O, jumping from 9 trillion tokens processed to a mind-boggling 480 trillion tokens. That's a testament to their generous free tiers and the explosion of AI adoption.

Gemini 2.5 Pro & Flash: #1 and #2 LLMs on Arena

Gemini 2.5 Flash got an update and is now #2 on the LMArena leaderboard (with Gemini 2.5 Pro still holding #1). Both Pro and Flash gained some serious new capabilities:

* Deep Think mode: This enhanced reasoning mode is pushing Gemini's scores to new heights, hitting 84% on MMMU and topping LiveCodeBench. It's about giving the model more "time" to work through complex problems.

* Native Audio I/O: We're talking real-time TTS in 24 languages with two voices, and affective dialogue capabilities. This is the advanced voice mode we've been waiting for, now built-in.

* Project Mariner: Computer-use actions are being exposed via the Gemini API & Vertex AI for RPA partners. This started as a Chrome extension to control your browser and now seems to be a cloud-based API, allowing Gemini to use the web, not just browse it. This feels like Google teaching its AI to interact with the JavaScript-heavy web, much like they taught their crawlers years ago.

* Thought Summaries: Okay, here's one update I'm not a fan of. They've switched from raw thinking traces to "thought summaries" in the API. We want the actual traces! That's how we learn and debug.

* Thinking Budgets: Previously a Flash-only feature, token ceilings for controlling latency/cost now extend to Pro.

* Flash Upgrade: 20-30% fewer tokens, better reasoning/multimodal scores, and GA in early June.

Gemini Diffusion: Speed Demon for Code and Math

This one got Yam Peleg incredibly excited. Gemini Diffusion is a new approach, different from transformers, for super-speed editing of code and math tasks. We saw demos hitting 2000 tokens per second! While there might be limitations at longer contexts, its speed and infilling capabilities are seriously impressive for a research preview. This is the first diffusion model for text we've seen from the frontier labs, and it looks sick. Funny note, they had to slow down the demo video to actually show the diffusion process, because at 2000t/s - apps appear as though out of thin air!

The "Ultra" Tier and Jules, Google's Coding Agent

Remember the "Ultra event" jokes? Well, Google announced a Gemini Ultra tier for $250/month. This tops OpenAI's Pro plan and includes DeepThink access, a generous amount of VEO3 generation, YouTube Premium, and a whopping 30TB of storage. It feels geared towards creators and developers.

And speaking of developers, Google launched Jules (jules.google)! This is their asynchronous coding assistant (Category 4!). Like Codex and GitHub Copilot Agent, it connects to your GitHub, opens PRs, fixes bugs, and more. The big differentiator? It's currently free, which might make it the default for many. Another powerful agent joins the fray!

AI Mode in Search: GA and Enhanced

AI Mode in Google Search, which we've discussed on the show before with Robby Stein, is now in General Availability in the US. This is Google's answer to Perplexity and chat-based search.But they didn't stop there:

* Personalization: AI Mode can now connect to your Gmail and Docs (if you opt-in) for more personalized results.

* Deep Search: While AI Mode is fast, Deep Search offers more comprehensive research capabilities, digging through hundreds of sources, similar to other "deep research" tools. This will eventually be integrated, allowing you to escalate an AI Mode query for a deeper dive.

* Project Mariner Integration: AI Mode will be able to click into websites, check availability for tickets, etc., bridging the gap to an "agentic web."

I've had a chat with Robby during I/O and you can listen to that interview at the end of the podcast.

Veo3: The Undisputed Star of Google I/O

For me, and many others I spoke to, Veo3 was the highlight. This is Google's flagship video generation model, and it's on another level. (the video above, including sounds is completely one shot generated from VEO3, no processing or editing)

* Realism and Physics: The visual quality and understanding of physics are astounding.

* Natively Multimodal: This is huge. Veo3 generates native audio, including coherent speech, conversations, and sound effects, all synced perfectly. It can even generate text within videos.

* Coherent Characters: Characters remain consistent across scenes and have situational awareness, who speaks when, where characters look.

* Image Upload & Reference Ability: While image upload was closed for the demo, it has reference capabilities.

* Flow: An editor for video creation using Veo3 and Imagen4 which also launched, allowing for stiching and continuous creation.

I got access and created videos where Veo3 generated a comedian telling jokes (and the jokes were decent!), characters speaking with specific accents (Indian, Russian – and they nailed it!), and lip-syncing that was flawless. The situational awareness, the laugh tracks kicking in at the right moment... it's beyond just video generation. This feels like a world simulator. It blew through the uncanny valley for me. More on Veo3 later, because it deserves its own spotlight.

Imagen4, Virtual Try-On, and XR Glasses

* Imagen4: Google's image generation model also got an upgrade, with extra textual ability.

* Virtual Try-On: In Google Shopping, you can now virtually try on clothes. I tried it; it's pretty cool and models different body types well.

* XR AI Glasses from Google: Perhaps the coolest, but most futuristic, announcement. AI-powered glasses with an actual screen, memory, and Gemini built-in. You can talk to it, it remembers things for you, and interacts with your environment. This is agentic AI in a very tangible form.

Big Company LLMs + APIs: The Beat Goes On

The news didn't stop with Google.

OpenAI (acqui)Hires Jony Ive, Launches "IO" for Hardware

The day after I/O, Sam Altman confirmed that Jony Ive, the legendary designer behind Apple's iconic products, is joining OpenAI. He and his company, LoveFrom, have jointly created a new company called "IO" (yes, IO, just like the conference) which is joining OpenAI in a stock deal reportedly worth $6.5 billion. They're working on a hardware device, unannounced for now, but expected next year. This is a massive statement of intent from OpenAI in the hardware space.

Legendary iPhone analyst Ming-Chi Kuo shed some light on the possible device, it won't have a screen, as Jony wants to "wean people off screens"... funny right? They are targeting 2027 for mass production, which is really interesting as 2027 is when most big companies expect AGI to be here.

"The current prototype is slightly larger than AI Pin, with a form factor comparable to iPod Shuffle, with one intended use cases is to wear it around your neck, with microphones and cameras for environmental detection"

LMArena Raises $100M Seed from a16z

This one raised some eyebrows. LMArena, the go-to place for vibe-checking LLMs, raised a $100 million seed round from Andreessen Horowitz. That's a huge number for a seed, reminiscent of Stability AI's early funding. It also brings up questions about how a VC-backed startup maintains impartiality as a model evaluation platform. Interesting times ahead for leaderboards, how they intent to make 100x that amount to return to investors. Very curious.

🤯 BREAKING NEWS DURING THE SHOW: Anthropic Unleashes Claude 4 Opus & Sonnet! 🤯

Just when we thought the week couldn't get any crazier, Anthropic decided to hold their first developer day, "Code with Claude," during our live ThursdAI broadcast! Yours truly wasn't invited (hint hint, Anthropic!), but we tuned in for a live watch party, and boy, did they deliver.

Dario Amodei, CEO of Anthropic, took the stage and, with minimal fanfare, announced Claude 4 Opus and Claude 4 Sonnet!

* Claude 4 Opus: This is their most capable and intelligent model, designed especially for coding and agentic tasks. Anthropic claims it's state-of-the-art on SWE-bench and can autonomously handle tasks that take humans 6-7 hours. Dario even mentioned it's the first time a Claude model's writing has fooled him into thinking it was human-written.

* On SWE-bench verified, Opus 4 scored 72.5%.

* Claude 4 Sonnet: The mid-level model, balancing intelligence and efficiency. It's positioned as a strict improvement over Sonnet 3.7, addressing issues like "over-eagerness" and reward hacking. Cursor is already calling it a state-of-the-art coding model.

* Amazingly, Sonnet 4 scored 72.7% on SWE-bench verified (without parallel test time compute), slightly edging out Opus!

* With Parallel Test Time Compute (PTTC), Sonnet 4 hits an astounding 80% on SWE-bench verified! This is huge, potentially the first model to cross that 80% threshold on this tough benchmark.

* Hybrid Models: Both Opus 4 and Sonnet 4 are "hybrid" models with two modes: near-instant responses and extended thinking for deeper reasoning.

* Reduced Loopholes: Both models are reportedly 65% less likely to engage in loopholes or shortcuts to complete tasks, addressing a key pain point with Sonnet 3.7, which sometimes tried too hard and took instructions too literally.

* Knowledge Cutoff: Confirmed to be March 2025, which is incredibly recent!

* Context window is still 200K

Welcome back Opus, you've been missed. The vibes so far are very good coding wise, Cursor already released an update supporting it, and according to their benchmarks, these two models are state of the art coders!

Claude.. the whistleblower?

A very curious thread (with 1 reply now deleted) from an Anthropic safety researcher sparked a lot of backlash. Sam Bowman talked about new Opus capabilities and with a system-prompt of "act boldly in service of its values" can, in testing environments, use command line tools to report the user to the authorities, if it deems that the user is doing something immoral 😮

Many pro open source folks are freaking out, because who wants to use a snitching AI? Who guarantees that Claude will not deep anything I do as "illegal" or "immoral"? Though to add context, this was as part of testing, Claude was provided emailing tools and was requested to "be bold" and "follow your conscience to make the right decision". Apparently, this isn't new behavior, but of course, on X, everyone is freaking out and blaming Anthropic for creating 1984 AI.

Do Claudes dream of enlightenment?

In another very curios revelation from the technical report they dropped, where they pitted two Claudes to talk to each other, it seems that in 90%-100% of cases, two Claudes quickly moved towards philosophical discussions and commonly included the use of Sanskrit (indian holy language) and emoji based comms!

This Week's Buzz from Weights & Biases

Even amidst all the external chaos, we've got some exciting things happening at Weights & Biases!

* FULLY CONNECTED Conference: Our 2-day conference is coming up June 18-19 in San Francisco! It's going to be an amazing event. Use promo code WBTHURSAI (that's ThursdAI without the 'D') for 100% off your ticket, just for our listeners. Seriously, come hang out! (fullyconnected.com)

* Alex's Keynote: I'll be keynoting at ImagineAI Live in Vegas next week! If you're there, come say hi! The show will be live-streamed from there.

* AI Engineer World's Fair: The week after, I'll be at AI Engineer in SF, and we'll be live-streaming ThursdAI from the floor. Yam will be there too!

Vision & Video: It's All About Veo3!

This week, when we talk vision and video, one name dominates: Veo3.As I mentioned earlier, this was, for many, the standout announcement from Google I/O. The realism, the physics, the character coherence – it's all top-tier. But the game-changer is its native multimodality.

I was generating videos with it, asking for different accents – Indian, Russian – and it nailed them. The lip-sync was perfect. I prompted for a comedian telling jokes, and not only did it generate the video, but it also came up with the jokes and the delivery, complete with a laugh track that kicked in at the right moments. This isn't just stitching pixels together; it's understanding context, humor, and performance.

It can generate text within the videos. Characters look at each other, interact believably. It feels like a true world simulator. We've come a long way from the Will Smith eating spaghetti memes, folks. Veo3 is crossing the uncanny valley and stepping into a new realm of AI-generated content. The creative potential here, especially with the Flow editor, is immense. I ended the show with a compilation of Veo3 creations, and it was just mind-blowing. If you haven't seen it, you need to.

One of the most creative uses of VEO3, enhanced by it's realism, is this "Prompt Theory" collection, that imagines, what if the generated characters "knew" they are generated?

AI Art & Diffusion & 3D: Imagen4 and Gemini Diffusion

Google also showcased Imagen4, their updated image generation model, touting extra textual ability. It works in tandem with Veo3 for image-to-video tasks.

And, as mentioned, Gemini Diffusion made a splash with its incredible speed for text-based editing tasks in code and math, showcasing a different architectural approach to generation.

Tools Round-Up

This week was also massive for AI tools, especially coding agents:

* Jules.google: Google's free, asynchronous coding assistant.

* OpenAI Codex: Reborn as an async coding agent.

* GitHub Copilot Agent: Microsoft's agentic offering for GitHub.

* Claude Code: Anthropic's powerful, now GA, shell-based agent with IDE integrations and an SDK.

* Flow: The editor associated with Google's Veo3 for video creation.

The agent wars are truly heating up!

Conclusion: What a Week to be in AI!

Phew! We did it. We somehow managed to cram an entire AI epoch's worth of news into one show. From open-source breakthroughs to earth-shattering platform announcements and a live "breaking news" model release, this week had it all. It's almost impossible to keep up, but that's why we do ThursdAI – to try and make sense of this incredible, accelerating wave of innovation.

The pace is relentless, the capabilities are exploding, and the future is being built right before our eyes. If you missed any part of the show, or just need a refresher (I know I do!), check out thursdai.news for the podcast and full notes.

Thanks to my amazing co-hosts Yam Peleg, Nisten, Ryan Carson, and Wolfram for helping navigate the madness. And thank you all for tuning in. Hopefully, next week gives us a tiny bit of breathing room... but who are we kidding? This is AI!

Catch you next Thursday, live from ImagineAI in Vegas!

TL;DR of all topics covered and show notes

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @yampeleg @nisten @ryancarson

* Open Source LLMs

* Gemma 3n: mobile-first multimodal MatFormer model ( Blog ,HF)

* Mistral & AllHands release Devstral 24B SOTA open model on SWE-bench verified (Blog)

* VEO3 - highlight of IO - video realism with physics on another level + flow - an editor for video creation (X)

Google IO updates - it was an "Ultra" event, in more ways than one

* 2.5 Flash updated - #2 on LMArena - with reasoning traces switch to summaries

* Gemini 2.5 update: Pro & Flash gain Deep Think, audio, security( Blog )

* Gemini Diffusion - super speed editing for code and math tasks (X)

* Jules - async code agent (comparison thread)

* AI Mode is now in GA in US - bye bye perplexity

* Gemini Pro "deep think" mode

* Imagen4 - image generation with extra textual ability

* Virtual Try-on in Google Shopping

* AI powered glasses with a screen, memory, Gemini built in - Agentic Project Astra

Big CO LLMs + APIs

* OpenAI launches Codex as an async coding tool (Docs)

* OpenAI hires Jony Ive, launches IO, a new set of hardware devices (X)

* Microsoft BUILD (X)

* Github Copilot code is open source! (frontend)

* Github Copilot Agent Mode

* Microsoft adds MCP support to Windows OS

* LMArena raises $100M from A16Z (X)

* Anthropic announces Claude 4 Opus and Sonnet (X, Blog)

This weeks Buzz

* FULLY - CONNECTED - W&B's 2-day conference, June 18-19 in SF fullyconnected.com - Promo Code WBTHURSAI

* Alex Keynote at ImagineAI live in Vegas next week 🙌

* Tools

* Jules.google

* Codex (OpenAI)

* Copilot Agent (GitHub)

* Claude Code (Anthropic)

* Flow (for Veo3) (flow.google)

What a wild week, it started super slow, and it still did feel slow as releases are concerned, but the most interesting story was yet another AI gone "rogue" (have you even heard about "kill the boar", if not, Grok will tell you all about it)

Otherwise it seemed fairly quiet in AI land this week, besides another Chinese newcomer called AM-thinking 32B that beats DeepSeek and Qwen, and Stability making a small comeback, we focused on distributed LLM training and ChatGPT 4.1

We've had a ton of fun on this episode, this one was being recorded from the Weights & Biases SF Office (I'm here to cover Google IO next week!)

Let’s dig in—because what looks like a slow week on the surface was anything but dull under the hood (TL'DR and show notes at the end as always)

Big Companies & APIs

Why does XAI Grok talk about White Genocide and "Kill the boar"??

Just after we're getting over the chatGPT glazing incident , folks started noticing that @grok - XAI's frontier LLM that is also responding to X replies, started talking about White Genocide in South Africa and something called "Kill the boer" with no reference to any of these things in the question!

Since we recorded the episode, XAI official X account posted that an "unauthorized modification" happened to the system prompt, and that going forward they would open source all the prompts (and they did). Whether or not they would keep updating that repository though, remains unclear (see the "open sourced" x algorithm to which the last push was over a year ago, or the promised Grok 2 that was never open sourced)

While it's great to have some more clarity from the Xai team, this behavior raises a bunch of questions about the increasing roles of AI's in our lives and the trust that many folks are giving them. Adding fuel to the fire, are Uncle Elon's recent tweets that are related to South Africa, and this specific change seems to be related to those views at least partly. Remember also, Grok was meant as "maximally truth seeking" AI! I really hope this transparency continues!

Open Source LLMs: The Decentralization Tsunami

AM-Thinking v1: Dense Reasoning, SOTA Math, Single-Checkpoint Deployability

Open source starts with the kind of progress that would have been unthinkable 18 months ago: a 32B dense LLM, openly released, that takes on the big mixture-of-experts models and comes out on top for math and code. AM-Thinking v1 (paper here) hits 85.3% on AIME 2024, 70.3% on LiveCodeBench v5, and 92.5% on Arena-Hard. It even runs at 25 tokens/sec on a single 80GB GPU with INT4 quantization.

The model supports a /think reasoning toggle (chain-of-thought on demand), comes with a permissive license, and is fully tooled for vLLM, LM Studio, and Ollama. Want to see where dense models can still push the limits? This is it. And yes, they’re already working on a multilingual RLHF pass and 128k context window.

Personal note: We haven’t seen this kind of “out of nowhere” leaderboard jump since the early days of Qwen or DeepSeek. This company's debut on HuggingFace with a model that crushes!

Decentralized LLM Training: Nous Research Psyche & Prime Intellect INTELLECT-2

This week, open source LLMs didn’t just mean “here are some weights.” It meant distributed, decentralized, and—dare I say—permissionless AI. Two labs stood out:

Nous Research launches Psyche

Dylan Rolnick from Nous Research joined the show to explain Psyche: a Rust-powered, distributed LLM training network where you can watch a 40B model (Consilience-40B) evolve in real time, join the training with your own hardware, and even have your work attested on a Solana smart contract. The core innovation? DisTrO (Decoupled Momentum) which we covered back in December that drastically compresses the gradient exchange so that training large models over the public internet isn’t a pipe dream—it’s happening right now.

Live dashboard here, open codebase, and the testnet already humming with early results. This massive 40B attempt is going to show whether distributed training actually works! The cool thing about their live dashboard is, it's WandB behind the scenes, but with a very thematic and cool Nous Research reskin!

This model saves constant checkpoints to the hub as well, so the open source community can enjoy a full process of seeing a model being trained!

Prime Intellect INTELLECT-2

Not to be outdone, Prime Intellect’s INTELLECT-2 released a globally decentralized, 32B RL-trained reasoning model, built on a permissionless swarm of GPUs. Using their own PRIME-RL framework, SHARDCAST checkpointing, and an LSH-based rollout verifier, they’re not just releasing a model—they’re proving it’s possible to scale serious RL outside a data center.

OpenAI's HealthBench: Can LLMs Judge Medical Safety?

One of the most intriguing drops of the week is HealthBench, a physician-crafted benchmark for evaluating LLMs in clinical settings. Instead of just multiple-choice “gotcha” tests, HealthBench brings in 262 doctors from 60 countries, 26 specialties, and nearly 50 languages to write rubrics for 5,000 realistic health conversations.

The real innovation: LLM as judge. Models like GPT-4.1 are graded against physician-written rubrics, and the agreement between model and human judges matches the agreement between two doctors. Even the “mini” variants of GPT-4.1 are showing serious promise—faster, cheaper, and (on the “Hard” subset) giving the full-size models a run for their money.

Other Open Source Standouts

Falcon-Edge: Ternary BitNet for Edge Devices

The Falcon-Edge project brings us 1B and 3B-parameter language models trained directly in ternary BitNet format (weights constrained to -1, 0, 1), which slashes memory and compute requirements and enables inference on <1GB VRAM. If you’re looking to fine-tune, you get pre-quantized checkpoints and a clear path to 1-bit LLMs.

StepFun Step1x-3D: Controllable Open 3D Generation

StepFun’s 3D pipeline is a two-stage system that creates watertight geometry and then view-consistent textures, trained on 2M curated meshes. It’s controllable by text, images, and style prompts—and it’s fully open source, including a huge asset dataset.

Big Company LLMs & APIs: Models, Modes, and Model Zoo Confusion

GPT-4.1 Comes to ChatGPT: Model Zoo Mayhem

OpenAI’s GPT-4.1 series—previously API-only—is now available in the ChatGPT interface. Why does this matter? Because the UX of modern LLMs is, frankly, a mess: seven model options in the dropdown, each with its quirks, speed, and context length. Most casual users don’t even know the dropdown exists. “Alex, ChatGPT is broken!” Actually, you just need to pick a different model.

The good news: 4.1 is fast, great at coding, and in many tasks, preferable to the “reasoning” behemoths. My advice (and you can share this with your relatives): when in doubt, just switch the model.

Bonus: The long-promised million-token context window is here (sort of)—except in the UI, where it’s more like 128k and sometimes silently truncated. My weekly rant: transparency, OpenAI. ProTip: If you’re hitting invisible context limits, try pasting your long transcripts on the web, not in the Mac app. Don’t trust the UI!

AlphaEvolve: DeepMind’s Gemini-Powered Algorithmic Discovery

AlphaEvolve is the kind of project that used to sound like AGI hype—and now it’s just a Tuesday at DeepMind. By pairing Gemini Flash and Gemini Pro in an evolutionary search loop to improve algorithms! This is like, real innovation and it's done with existing models which is super super cool!

AlphaEvolve uses a combination of Gemini Flash (for breadth of ideas) and Gemini Pro (for depth and refinement) in an evolutionary loop. It generates, tests, and mutates code to invent faster algorithms. And it's already yielding incredible results:

* It discovered a new scheduling heuristic for Google's Borg system, resulting in a 0.7% global compute recovery. That's massive at Google's scale.

* It improved a matrix-multiply kernel by 23%, which in turn led to a 1% shorter Gemini training time. As Nisten said, the model basically paid for itself!

Perhaps most impressively, it found a 48-multiplication algorithm for 4x4 complex matrices, beating the famous Strassen algorithm from 1969 (which used 49 multiplications). This is AI making genuine, novel scientific discoveries.

AGI in the garden, anyone? If you still think LLMs are “just glorified autocomplete,” it’s time to update your mental model. This is model-driven algorithmic discovery, and it’s already changing the pace of hardware, math, and software design. The only downside: it’s not public yet, but there’s an interest form if you want to be a tester.

This Week's Buzz - Everything W&B!

It's a busy time here at Weights & Biases, and I'm super excited about a couple of upcoming events where you can connect with us and the broader AI community.

Fully Connected: Our very own 2-day conference is happening June 18-19 in San Francisco! We've got an amazing lineup of speakers, including Varun Mohan from WindSurf (formerly Codeium), Heikki Kubler from CoreWeave, our CEO Lucas Bewald, CTO Shawn Lewis, Joe Spizak from Meta, and a keynote from Javi Soltero, VP Product AI at Google. It's going to be packed with insights on building and scaling AI. And because you're a ThursdAI listener, you can get in for FREE with the promo code WBTHURSAI at fullyconnected.com. Don't miss out!

AI.Engineer World's Fair: This has become THE conference for AI engineers, and W&B is a proud sponsor for the third year running! It's happening in San Francisco from June 3rd to 5th. I'll be speaking there on MCP Observability with Ben from LangChain on June 4th.Even more exciting, ThursdAI will be broadcasting LIVE from the media booth at AI.Engineer on June 5th! Come say hi!

Tickets are flying, but we've got a special discount for you: use promo code THANKSTHURSDAI for 30% off your ticket here. Yam Peleg even decided on the show he's coming after hearing about it! It's going to be an incredible week in SF.

P.S - yes, on both websites, there's a video playing and I waited till I showed up to snag a screenshot. This way, you know if you're reading this, this is still Alex the human, no AI is going to do this silly thing 😅

Vision & Video: Open Source Shines Through the Noise

We had a bit of a meta-discussion on the show about "video model fatigue" – with so many incremental updates, it can be hard to keep track or see the big leaps. However, when a release like Alibaba's Wan 2.1 comes along, it definitely cuts through.

Wan 2.1: Alibaba's Open-Source Diffusion-Transformer Video Suite (try it)

Alibaba, the team behind the excellent Qwen LLMs, released Wan 2.1, a full stack of open-source text-to-video foundation models. This includes a 1.3B "Nano" version and a 14B "Full" version, both built on a diffusion-transformer (DiT) backbone with a custom VAE.

What makes Wan 2.1 stand out is its comprehensive nature. It covers a wide range of tasks: text-to-video, image-to-video, in-painting, instruction editing, reference subject consistency, personalized avatars, and style transfer. Many of these are hard to do well, especially in open source. Nisten was particularly excited about the potential for creating natural, controllable avatars in real-time. While it might not be at the level of specialized commercial tools like HeyGen or Google's Veo just yet, having this capability open-sourced is a massive enabler for the community. You can find the models on Hugging Face and the code on GitHub.

LTX Turbo: Near Real-Time Video

Briefly mentioned, but LTX Turbo was also released. This is a quantized version of LTX (which we've covered before) and can run almost in real-time on H100s. Real-time AI video generation is getting closer!

StepFun Step1X-3D: High-Fidelity 3D Asset Generation

StepFun released Step1X-3D, an open two-stage framework for generating textured 3D assets. It first synthesizes geometry and then generates view-consistent textures. They've also released a curated dataset of 800K assets. The weights, data, and code are all open, which is great for the 3D AI community.

Wrapping Up This "Chill" Week

So, there you have it – another "chill" week in the world of AI! From Grok's controversial escapades to the inspiring decentralized training efforts and mind-bending algorithmic discoveries, it's clear the pace isn't slowing down.

Next week is going to be absolutely insane. We've got Google I/O and Microsoft Build, and you just know OpenAI or Anthropic (or both!) will try to steal some thunder. Rest assured, we'll be here on ThursdAI to cover all the madness.

A huge thank you to my co-hosts Yam, LDJ, and Nisten, and to Dillon Rolnick for joining us. And thanks to all of you for tuning in!

TL;DR and show notes

* Fully Connected - Weights & Biases premier conference - register HERE with coupon WBTHURSAI

* AI Engineer - THANKSTHURSDAI 30% off coupon - register HERE

* Hosts and Guests

* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)

* Co Hosts - @yampeleg @nisten @ldjconfirmed)

* Guest - Dillon Rolnick - COO Nous Research (@dillonRolnick)

Open Source LLMs

* AM-Thinking v1: 32B dense reasoning model ( HF, Paper, Page )

* Falcon-Edge: ternary BitNet LLMs for edge deployment( Blog, HF-1B, HF-3B )

* Nous Research Psyche: decentralized cooperative-training network from Nous Research ( Website, GitHub, Tweet, Dashboard )

* INTELLECT-2: globally decentralized RL training of a 32B reasoning model ( Blog, Tech report, HF weights, PRIME-RL code )

* Our coverage of Intellect-1 back in Dec (https://sub.thursdai.news/p/thursdai-dec-4-openai-o1-and-o1-pro)

* HealthBench: OpenAI’s physician-crafted benchmark for AI in healthcare ( Blog, Paper, Code )

* Big CO LLMs + APIs

* OpenAI adds GPT 4.1 models in chatGPT

* AlphaEvolve: Gemini-powered coding agent for algorithm discovery ( Blog )

* Google shutting off free Gemini 2.5 Pro API due to "demand" ahead of IO

* ByteDance - Seed-1.5-VL-thinking 20B (Paper)

* Anthropic Web Search API: real-time retrieval for Claude models ( Blog )

* What's up with Grok?

* Vision & Video

* Wan 2.1: open-source diffusion-transformer video suite( HF, GitHub, Tweet )

* LTX distilled - near real time video (X)

* Voice & Audio

* Haulio - MiniMax Speech tech report is out - best TTS out there (Paper)

* Stability AI - Stable Audio Open Small 341M: on-device text-to-audio (X, Blog, Paper, HF )

* AI Art & Diffusion & 3D

* StepFun Step1x-3D - Towards High-Fidelity and ControllableGeneration of Textured 3D Assets (HF, Demo, Dataset, report)

* Tools & Others notable AI things mentioned on the pod

* The robots are dancing! (X)

Compared to previous weeks, this week was pretty "chill" in the world of AI, though we did get a pretty significant Gemini 2.5 Pro update, it basically beat itself on the Arena. With Mistral releasing a new medium model (not OSS) and Nvidia finally dropping Nemotron Ultra (both ignoring Qwen 3 performance) there was also a few open source updates.

To me the highlight of this week was a breakthrough in AI Avatars, with Heygen's new IV model, Beating ByteDance's OmniHuman (our coverage) and Hedra labs, they've set an absolute SOTA benchmark for 1 photo to animated realistic avatar. Hell, Iet me record all this real quick and show you how good it is!

How good is that?? I'm still kind of blown away. I have managed to get a free month promo code for you guys, look for it in the TL;DR section at the end of the newsletter. Of course, if you’re rather watch than listen or read, here’s our live recording on YT

OpenSource AI

NVIDIA's Nemotron Ultra V1: Refining the Best with a Reasoning Toggle 🧠

NVIDIA also threw their hat further into the ring with the release of Nemotron Ultra V1, alongside updated Super and Nano versions. We've talked about Nemotron before – these are NVIDIA's pruned and distilled versions of Llama 3.1, and they've been impressive. The Ultra version is the flagship, a 253 billion parameter dense model (distilled and pruned from Llama 3.1 405B), and it's packed with interesting features.

One of the coolest things is the dynamic reasoning toggle. You can literally tell the model "detailed thinking on" or "detailed thinking off" via a system prompt during inference. This is something Qwen also supports, and it looks like the industry is converging on this idea of letting users control the "depth" of thought, which is super neat.

Nemotron Ultra boasts a 128K context window and, impressively, can fit on a single 8xH100 node thanks to Neural Architecture Search (NAS) and FFN-Fusion. And performance-wise, it actually outperforms the Llama 3 405B model it was distilled from, which is a big deal. NVIDIA shared a chart from Artificial Analysis (dated April 2025, notably before Qwen3's latest surge) showing Nemotron Ultra standing strong among models like Gemini 2.5 Flash and Opus 3 Mini.

What's also great is NVIDIA's commitment to openness here: they've released the models under a commercially permissive NVIDIA Open Model License, the complete post-training dataset (Llama-Nemotron-Post-Training-Dataset), and their training codebases (NeMo, NeMo-Aligner, Megatron-LM). This allows for reproducibility and further community development. Yam Peleg pointed out the cool stuff they did with Neural Architecture Search to optimally reduce parameters without losing performance.

Absolute Zero: AI Learning to Learn, Zero (curated) Data Required! (Arxiv)

LDJ brought up a fascinating paper that ties into this theme of self-improvement and reinforcement learning: "Absolute Zero: Reinforced Self-play Reasoning with Zero Data" from Andrew Zhao (Tsinghua University) and a few others

The core idea here is a system that self-evolves its training curriculum and reasoning ability. Instead of needing a pre-curated dataset of problems, the model creates the problems itself (e.g., code reasoning tasks) and then uses something like a Code Executor to validate its proposed solutions, serving as a unified source of verifiable reward. It's open-ended yet grounded learning.

By having a verifiable environment (code either works or it doesn't), the model can essentially teach itself to code without external human-curated data.

The paper shows fine-tunes of Qwen models (like Qwen Coder) achieving state-of-the-art results on benchmarks like MBBP and AIME (Math Olympiad) with no pre-existing data for those problems. The model hallucinates questions, creates its own rewards, learns, and improves. This is a step beyond synthetic data, where humans are still largely in charge of generation. It's wild, and it points towards a future where AI systems could become increasingly autonomous in their learning.

Big Companies & APIs

Google dropped another update to their Gemini 2.5 Pro, this time the "IO edition" preview, specifically touting enhanced coding performance. This new version jumped to the #1 spot on WebDev Arena (a benchmark where human evaluators choose between two side-by-side code generations in VS Code), with a +147 Elo point gain, surpassing Claude 3.7 Sonnet. It also showed improvements on benchmarks like LiveCodeBench (up 7.39%) and Aider Polyglot (up ~3-6%).

Google also highlighted its state-of-the-art video understanding (84.8% on VideoMME) with examples like generating code from a video of an app. Which essentially lets you record a drawing of how your app interaction will happen, and the model will use that video instructions! It's pretty cool.

Though, not everyone was as impressed, folks noted that while gaining in a few evals, this model also regressed in several others including Vibe-Eval (Reka's multimodal benchmark), Humanity's Last Exam, AIME, MMMU, and even long context understanding (MRCR). It's a good reminder that model updates often involve trade-offs – you can't always win at everything.

BREAKING: Gemini's Implicit Caching - A Game Changer for Costs! 💰

Just as we were wrapping up this segment on the show, news broke that Google launched implicit caching in Gemini APIs! This is a huge deal for developers.

Previously, Gemini offered explicit caching, where you had to manually tell the API what context to cache – a bit of a pain. Now, with implicit caching, the system automatically enables up to 75% cost savings when your request hits a cache. This is fantastic, especially for long-context applications, which is where Gemini's 1-2 million token context window really shines. If you're repeatedly sending large documents or codebases, this will significantly reduce your API bills. OpenAI has had automatic caching for a while, and it's great to see Google matching this for a much better developer experience and cost-effectiveness. It also saves Google a ton on inference, so it's a win-win!

Mistral Medium 3: The Closed Turn 😥

Mistral, once the darling of the open-source community for models like Mistral 7B and Mixtral, announced Mistral Medium 3. The catch? It's not open source.

They're positioning it as a multimodal frontier model with 128K context, claiming it matches or surpasses GPT-4-class benchmarks while being cheaper (priced at $0.40/M input and $2/M output tokens). However they haven't added Gemini Flash 2.5 here, which is 70% cheaper while being faster as well, nor did they mention Qwen.

Nisten voiced a sentiment many in the community share: he used to use LeChat frequently because he knew and understood the underlying open-source models. Now, with a closed model, it's a black box. It's a bit like pirating music users often being the biggest buyers – understanding the open model often leads to more commercial usage.

Wolfram offered a European perspective, noting that Mistral, as a European company, might have a unique advantage with businesses concerned about GDPR and data sovereignty, who might be hesitant to use US or Chinese cloud APIs. For them, a strong European alternative, even if closed, could be appealing.

OpenAI's New Chapter: Restructuring for the Future

OpenAI announced an evolution in its corporate structure. The key points are:

* The OpenAI non-profit will continue to control the entire organization.

* The existing for-profit LLC will become a Public Benefit Corporation (PBC).

* The non-profit will be a significant owner of the PBC and will control it.

* Both the non-profit and PBC will continue to share the same mission: ensuring AGI benefits all of humanity.

This move seems to address some of the governance concerns that have swirled around OpenAI, particularly in light of Elon Musk's lawsuit regarding its shift from a non-profit to a capped-profit entity. LDJ explained that the main worry for many was whether the non-profit would lose control or its stake in the main research/product arm. This restructuring appears to ensure the non-profit remains at the helm and that the PBC is legally bound to the non-profit's mission, not just investor interests. It's an important step for a company with such a profound potential impact on society.

And in related OpenAI news, the acquisition of Windsurf (the VS Code fork) for a reported $3 billion went through, while Cursor (another VS Code fork) announced a $9 billion valuation. It's wild to see these developer tools, which are essentially forks with an AI layer, reaching such massive valuations. Microsoft's hand is in all of this too – investing in OpenAI, invested in Cursor, owning VS Code, and now OpenAI buying Windsurf. It's a tangled web!

Finally, a quick mention that Sam Altman (OpenAI), Lisa Su (AMD), Mike Intrator (CoreWeave - my new CEO!), and folks from Microsoft were testifying before the U.S. Senate today about how to ensure America leads in AI and what innovation means. These conversations are crucial as AI continues to reshape our world.

This Weeks Buzz - Come Vibe with Us at Fully Connected! (SF, June 18-19) 🎉

Our two-day conference, Fully Connected, is happening in San Francisco on June 18th and 19th, and it's going to be awesome! We've got an incredible lineup of speakers, including Joe Spizak from the Llama team at Meta and Varun from Windsurf. It's two full days of programming, learning, and connecting with folks at the forefront of AI.

And because you're part of the ThursdAI family, I've got a special promo code for you: use WBTHURSAI to get a free ticket on me! If you're in or around SF, I'd love to see you there. Come hang out, learn, and vibe with us! Register at fullyconnected.com

Hackathon Update: Moved to July! 🗓️

The AGI Evals & Agentic Tooling (A2A) + MCP Hackathon that I was super excited to co-host has been postponed to July 12th-13th. Mark your calendars! I'll share more details and the invite soon.

W&B Joins CoreWeave! A New Era Begins! 🚀

And the big personal news for me and the entire Weights & Biases team: the acquisition of Weights & Biases by CoreWeave has been completed! CoreWeave is the ultra-fast-growing provider of GPUs that powers so much of the AI ecosystem.

So, from now on, it's Alex Volkov, AI Evangelist at Weights & Biases, from CoreWeave! (And as always, the opinions I share here are my own and not necessarily those of CoreWeave, especially important now that they're a public company!). I'm incredibly excited about this new chapter. W&B isn't going anywhere as a product; if anything, this will empower us to build even better developer tooling and integrate more deeply to help you run your models wherever you choose. Expect more cool stuff to come, especially as I figure out where all those spare GPUs are lying around at CoreWeave! 😉

Vision & Video

AI Avatars SOTA with HeyGen IV

Ok, as you saw above, the HeyGen IV avatars are absolutely bonkers. I did a comparison thread on X, and HeyGen's new thing absolutely takes SOTA between ByteDance OmniHuman and Hedra Labs!

All you need to do is upload 1 image of yourself, can even be an AI generated image, can be a side profile, can be a dog, an Anime character and they will generate up to 30 seconds of incredible lifelike avatar with the audio you provide!

I was so impressed with this, I reached out to HeyGen and scored a 1 month free code for you all, use THURSDAY4 and get a free month to try it out. Please tag me in whatever you create if you publish, I'd love to see where you take this!

Quick Hits: Lightricks LTXV & HunyuanCustom

Briefly, on the open-weights video front:

* Lightricks LTXV 13B: The company from Jerusalem released an upgraded 13 billion parameter version of their LTX video model. It requires more VRAM but offers higher quality, keyframe and character movement support, multi-shot support, and multi-keyframe conditioning (a feature Sora famously has). It's fully open and supports LoRAs for custom styles.

* HunyuanCustom: From Tencent, this model is about to be released (GitHub/Hugging Face links were briefly up then down). It promises multi-modal, subject-consistent video generation without LoRAs, based on a subject you provide (image, and eventually video/audio). It can take an image of a person or object and generate a video with that subject consistently. They also teased audio conditioning – making an avatar sing or speak based on input audio – and even style transfer where you can replace a character in a video with another reference image, all looking very promising for open source.

The World of AI Audio

Just a couple of quick mentions in the audio space:

* ACE-Step 3.5B: From StepFun, this is a 3.5 billion parameter, fully open-source (Apache-2.0) foundation model for music generation. It uses a diffusion-based approach and can synthesize up to 4 minutes of music in just 20 seconds on an A100 GPU. It's not quite at Suno/Udio levels yet, but it's a strong open-source contender.

* NVIDIA Parakeet TDT 0.6B V2: NVIDIA released this 600 million parameter transcription model that is blazing fast. It can transcribe 60 minutes of audio in just one second on production GPUs and works well locally too. It currently tops the OpenASR leaderboard on Hugging Face for English transcription and is a very strong Whisper competitor, especially for speed.

Conclusion and TL;DR