ThursdAI - The top AI news from the past week

Hey y’all, Alex here, let me catch you up!

Jensen Huang went on Lex and said AGI has been achieved. We’ll get to that.

The biggest demo moment: Gemini 3.1 Flash Live launched - Google’s omni model that sees, hears, and searches the web in real time. We tested it live and I said “what the f**k” on air. It was really impressive!

Google Research also dropped TurboQuant (6x KV cache compression) which crashed Samsung and Micron stocks - we had Daniel Han from UnSloth help us make sense of why that’s overblown. OpenAI killed Sora - the app, the API, and the $1B Disney deal. Claude felt noticeably dumber this week AND max account quotas are melting as 500+ people confirmed on my X and Reddit. We have an official word from Anthropic as to why.

Mistral launched Voxtral TTS (open weight, claims to beat ElevenLabs), Cohere shipped an ASR model, and Google’s Lyria 3 Pro now generates full 3-minute music tracks inside Producer AI.

This and a lot more in today’s episode, let’s dive in (as always, show notes and links in the end!)

ThursdAI - Let me catch you up!

Gemini 3.1 Flash Live: The Real-Time AI Companion Is Here

Google dropped a breaking news on the show today, with Gemini 3.1 Flash - LIVE version. This one is an omni-model, that means it can receive text/audio/video on input and respond in text and voice. It has Google search grounding, and it felt... immediate!

I was blown away, really, check out the video, the speed with which it was able to “see” me, respond to my query, look up something on the web, was mind blowing. I don’t often get “mind blown” anymore, there’s just too many news, but this one did the trick!

With the pricing being around 10x cheaper than GPT-real-time, and the Google search grounding being super fast, I can absolutely see this model being hooked up to... robots (like ReachyMini), SmartGlasses that can see what you see, and a bunch more!

Gemini Live is available on Google AI studio and has been rolled out globally inside the Google Search app! So now when you pull up the Google Search app, just open it and point at anything. Truly a remarkable advancement.

Google research publishes TurboQuant - 6x reduction in KV cache with 0 accuracy loss

Google research posted some work (based on an Arxiv paper from almost a year ago) that shows that with geometry tricks, combining two other techniques like PolarQuant and QJL, they are able to compress the KV cache of running LLMs by nearly 6x, and show an 8X speed up for model inference with zero accuracy loss.

If you ever watched silicon valley the HBO show, this sounds like the fictional middle-out algorithm from PiedPiper. If this scales (and that’s a big if, we don’t know if this applies to other, bigger models yet), this means significant decreases in memory requirements to run the current crop of LLMs for longer context.

The claim is big, so we’ll continue to monitor if this indeed scales, but the most interesting thing about this piece of news is, that it broke the AI bubble and went to wall street, with finance brows deciding that this means that memory will not be needed as much any more and it tanked Samsung and Micron stocks. Which I found particularly ridiculous on the show, did they not hear about Jevons Paradox? This is reminiscent of the DeepSeek R1 saga that tanked Nvidia stocks over a year ago.

Daniel Han from Unsloth, who joined us on the show, pointed out that the approach is mathematically interesting even if it’s not necessarily better than existing open-source techniques like DeepSeek MLA. LDJ noted that the baseline comparison (16-bit KV cache) isn’t really fair since most production systems are already compressing beyond that. Yam implemented it himself and confirmed the speedups are real, but so is the trade-off.

Anthropic updates: Opus dumber? Quotas lower! Injunction won! Computer.. used.

Anthropic folks, especially on the Claude code side are shipping like crazy, we won’t be able to cover all the updates, but there was a few notable things I have to keep you up to date on.

Claude Opus seems to be getting “dumber”, again

I have to talk about this because it affected my work directly this week and hundreds of people confirmed the same experience.

I use Claude Opus for my standard ThursdAI prep workflow — generating the TL;DR with 10 bullet points and an executive summary for every topic we cover, creating episode pages, etc.

The format has not changed for over a year and yet this week I asked for 10 factoids. I got 4. It says “10” right there in the prompt. Four bullet points.

On the website builder, I’ve asked Opus to create a page for last weeks episode, and instead of adding it to the other episode, Opus decided to ... replace the last episode with this one. This would be funny if it wasn’t sad. This is Opus 4.6 we’re talking about, not some quantized open source LLM from last year!

The reason is unclear, and it’s not only me, Wolfram noticed that it’s easier to see these types of things in other languages and that for the last week Opus would forget to add Umlauts in German!? and Yam also felt it.

Pro/Max plan quotas burning up, Anthropic confirmed that they are tightening them for “peak hour” usage

This week, so many people started posting that something is wrong with their Claude Codes, I did a survey, and it blew up. Hundreds of people replied and confirmed that for the first week, they are hitting their session quotas on Pro and 20x $200/mo MAX accounts much much quicker than before. When I say much quicker, I mean, some fokls have hit the quota in as little as 5 minutes. While some others had no issues.

I personally btw did not have this. A few days later, Thariq from the Claude code team, and later an official post, confirmed that Anthropic had been rolling out a “tightening” of the Pro/Max accounts to accomodate for growth.

This is of course, a huge bummer to the folks who pay $200/mo for the 20x max tier, as they tend to run agents and subagents overnight. But here’s the thing, I don’t think that folks from Anthropic see what we see, some folks got no issues with hitting quota, and some are barely able to use their subscription. I hope that they will find and resolve these bugs quick, because some folks are switching to Codex, and the Anthropic IPO is coming up! I will say, I don’t envy Thariq’s job, he’s doing it gracefully, and maybe one of the only ones in Anthropic that does it at all.

Judge granted Anthropic an injunction against DoW and the whole “Supply chain risk” designation!

Just in as I’m writing this, a district judge in CA, granted Anthropic an injunction against being designated as a supply-chain-risk company. If you haven’t been following, the US Department of War, specifically Pete Hegseth, threatened and then designated Anthropic as a supply chain risk company, while us president Trump “fired” Anthropic and banned its use in any gov agencies.

Well, no so fast says Judge Lin, from CA District court. In this Order, she shows that Dept. of war didn’t meet any legal requirements for this designation. It’s really a fascinating read, but the highligth is this:

When asked why Hegseth made a public statementthat had no legal effect and that did not reflect the immediate intent of DoW, counsel stated, “I don’t know.”

This is just the first court and will likely be escalated further up the judicial system. This is still developing and apparently the Pentagon declared Anthropic a supply chain risk under two different statutes, and this only affects one of them. So while it’s good news, it’s not over yet.

Voice & Audio Explosion: Three Releases in One Hour

I had to hit the breaking news button mid-TLDR because three major voice releases dropped simultaneously during the show.

Mistral Voxtral TTS — Mistral’s first text-to-speech model, 3 billion parameters, open weight. They claim it beats ElevenLabs Flash v2.5 in human preference tests (58% win rate on flagship voices, 68% on zero-shot voice cloning).

We tested it live on the show — it’s decent, with emotion controls for neutral, happy, and frustrated voices. I was not super impressed tbh, it sits somewhere between the very good big labs TTS and the very small open source 82M param TTS.

Cohere Transcribe — Cohere enters the ASR game with a 2 billion parameter open-source model (Apache 2.0!) that immediately grabbed the #1 spot on HuggingFace’s Open ASR Leaderboard with a 5.42% word error rate, beating Whisper Large v3’s 7.44%. In human evaluations, it wins 61% of the time on average, and 64% specifically against Whisper. For anyone in regulated industries needing local inference for compliance, this could genuinely replace Whisper as the default.

Google Lyria 3 Pro — Google’s most advanced music model is here.

It can now generate full 3-minute tracks with structural control — intros, verses, choruses, bridges. We generated a ThursdAI opening theme live on the show using Producer AI, and it was... honestly not bad?

It followed our instructions perfectly: drum and bass, 174 BPM, high energy podcast opener with vocals and introduction. The instruction-following was spot on. Nisten said it’s the best music generation model right now. It’s available to Gemini subscribers and via Producer AI and gemini, and it can even compose music from images. SynthID watermarked, royalty-free. We might actually use one of the generated tracks as a new show opener.

The craziest thing is, since Google acquired Composer, the team has been shipping. I only generated the audio during the live show, but now went back there to download it for you guys, and whoah, it can now generate whole clips by using other Google tech, this is really cool!

OpenAI kills SORA (and Atlas?)

Last week we reported on about OpenAI’s focus shift towards Codex and productivity, and this week we see the first casualty. OpenAI is killing SORA, the app, the Sora 2 and Sora 2 pro models and APIs.

Many AI haters are celebrating this as through “ai videos” is dead, but honestly, this is obviously about the GPU power and the other things OpenAI needs to do to win the fight against Anthropic. OpenAI is also apparently going to IPO this year (like Anthropic) and they absolutely need to win the productivity/agents in enterprise market.

As part of this shut down, the Disney + OpenAI partnership, is also dissolving, and Disney will no longer invest 1B into OpenAI.

So, say bye bye to having digital selfies with Sam Altman. I’ve generated this SORA vid to hear from Sam himself:

Atlas browser, OpenAI’s native browser endeavor is supposedly also going to transform, together with Codex and OpenAI native app into one super app that includes all three according to the same memo.

AGI is here according to Jensen, AGI is far away, according to ARC-AGI-3

The back to back this week can give anyone whiplash. First, Lex Friedman had Jensen Huang on the podcast, and asked him a very specific “WhenAGI” question, to which Jensen said “I believe it’s already here”

Then just a few short days layer, ArcPrize, released the 3rd version of Arc-AGI, Arc-AGI 3 a series of puzzle games, where humans get 100% pass-rate and the current LLM, top tier frontier LLMs, are getting less than 1%! It’s an interactive, agentic reasoning benchmark designed to test human-like generalization and intelligence in novel, abstract, turn-based environments.

The puzzles all look simple enough to do, and are actually fun, and while the wild claims of “AGI is not here yet” from the ArcPrize folks are quite interesting. The stated goal of the foudation is to release evaluations that are completely un-saturated, and this seems like one such thing at first glance.

There’s a bit of a debate in the community about the way Arc Prize went about this specific benchmark (no harnesses, raw LLM outputs), saying that humans got a “game” while the LLMs get just raw JSON and minimal and no extra tools.

For context, a agentic harness startup claims to have solved 35% already of the games in ArcAGI, but that result is unverified and self reported, becuase they are an agentic harness, which ArcAGI apparently disqualifies.

AI Art and Diffusion

I wanted to finish but I think these are important releases so I’ll include them briefly.

Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)

This was a surprising release, we previously seen Luma Labs do video, but this time they are posting their Uni-1 which is a… image model but it’s based on an LLM, so you talk to it, iterate together until you get results. Yes, Nano Banana via AI studio is kind of like this as well ,but Uni feels a bit different. It can also generate infographics, which I haven’t tried yet.

You can try Uni here

Phota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)

There’s tons of photo startups, but this one looks kind of crazy! You upload a bunch of your pictures, they train a “model” for you, and then you can create a whole bunch of images, and they do actually resemble you. Yes, Nano Banana can take a few reference pictures, but this somehow seems more accurate!

You can create professional photos, fix photos you like, add others to your photos. I do feel there’s a jump in capabilities here, specifically because of the personalization! Give them a try if you’re not worried about them training on your pics and let me know.

Modular made Flux.2 run in <300ms (X)

We told you about Modular, and Mojo before, and while they provide inference speedups, I was surprised to see them releasing a model optimization, and hope this comes to all image generations!

There’s a lot more to be said about this weeks updates, we went for over 2.5 hours (which I had to cut down to a bit over 1h45m) on the live show, and while I can go and on, I want to pause here. Weeks are getting crazier, denser and more unpredictable. I really thought we’d have a chill week until today!

P.S - Mario Zechner, the author of the Pi coding CLI, which sits at the heart of OpenClaw has posted an awesome essay called “thoughts on slowing the f**k down“, I strongly advice anyone with many agents running in parallel to read this.

Simultaneously, Alex Sidorenko posted this beautiful visualization of what happens when you have too many agents running in a loop, on your codebase. This is definitely starting to be noticeable as many companies use more and more agents, without reviewing their code. On weeks like this week, where Opus has almost deleted a part of my website, I feel this very strongly. Be careful out there!

See you next week!

* General

* Jensen says “AGI is here” (X, Lex full pod)

* Big CO LLMs + APIs

* Google drops Gemini Flash live - Gemini can see, hear and talk to you (X)

* OpenAI fully discontinues Sora, including app, API, and ChatGPT video features, as Disney deal collapses (X, X)

* Claude Code users blowing through weekly usage quotas by Monday/Tuesday (X)

* Anthropic tightens the Claude Pro/Max account quotas during Peak Hours (Anthropic announcement)

* ARC-AGI-3 launches: humans 100%, AI under 1% (X, Announcement)

* Anthropic gets an injunction against DoW in Supply-chain case (X)

* Open Source LLMs

* Google TurboQuant — KV cache 6x compression, 8x speedup, zero accuracy loss (X, Blog, Arxiv)

* Unsloth Studio: 10x faster inference, desktop shortcuts, auto-parameter detection (X, GitHub)

* Reka AI launches Edge, a 7B multimodal vision-language model built for sub-second latency on edge devices, now available on OpenRouter (X, HF, Announcement, Blog)

* Tools & Agentic Engineering

* Cursor Composer 2 tech report: 1T params trained on Kimi K2.5 (X, Blog)

* Modular 26.2 — FLUX.2 in <1 second, 99% cheaper than Nano Banana (X, Blog)

* litellm PyPI supply chain attack — SSH keys, cloud creds, API keys exfiltrated (X)

* Claude can now control your Mac - computer use arrives in Claude Cowork and Claude Code as a research preview (X, Announcement)

* Voice & Audio

* Mistral drops Voxtral TTS, a 3B-parameter open-weight text-to-speech model that beats ElevenLabs Flash in human preference tests (X, Blog)

* Cohere launches Transcribe, an open-source 2B ASR model that tops HuggingFace’s Open ASR Leaderboard with 5.42% word error rate (X, Blog, HF)

* Google DeepMind Lyria 3 Pro — full 3-minute music tracks with structural control (X, Announcement)

* Irodori-TTS-500M — Japanese TTS with emoji emotion control (X, HF)

* AI Art & Diffusion & 3D

* Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)

* Modular FLUX.2 — sub-1-second image generation, 99% cheaper than cloud (X)

* Phota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)

Howdy, Alex here, let me catch you up on everything that happened in AI:

(btw; If you haven’t heard from me last week, it was a Substack glitch, it was a great episode with 3 interviews, our 3rd birthday, I highly recommend checking it out here)

This week was started on a relatively “chill” note, if you consider Anthropic enabling 1M context window chill. And then escalated from there. We covered the new GPT 5.4 Mini & Nano variants from OpenAI. How MiniMax used autoresearch loops to improve MiniMax 2.7, Cursor shipping their own updated Composer 2 model, and how NVIDIA CEO Jensen Huang embraced OpenClaw calling it “the most important OSS software in history” and that every company needs an OpenClaw strategy.

Also, OpenAI acquires Astral (ruff, uv tools) and Mistral releases a “small” 119B unified model and Cursor dropped their Opus like Composer 2 model. Let’s dive in:

ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Big Companies LLMs

1M context is now default for Opus.

Anthropic enabled the 1M context window they shipped Claude with in beta, by default, to everyone.

Claude, Claude Code, hell, even inside OpenClaw if you’re able to get your Max account in there, are now using the 1M long version of Opus. This is huge, because, while its not perfect it’s absolutely great to have 1 long conversation and not worry about auto-compaction of your context.

As we just celebrated our 3rd anniversary, I remember that back then, we were excited to see GPT-5 with 8K context. Love how fast we’re moving on this.

OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship cost

Last week on the show, Ryan said he burned through 1B (that’s 1 billion) tokens in a day! That is crazy, and there’s no way a person sitting in front of a chatbot can burn through this many tokens. This is only achieved via orchestration.

To support this use-case, OpenAI dropped 2 new smaller models, cheaper and faster to run. GPT 5.4 Mini achieves a remarkable 72.1% on OSWorld Verified, which means it uses the computer very well, can browse and do tasks. 2x faster than the previous mini, at .75c/1M token, this is the model you want to use in many of your subagents that don’t require deep engineering.

This is OpenAI’s ... sonnet equivalent, at 3x the speed and 70% the cost from the flagship.

Nano is even crazier, 20 cents per 1M tokens, but it’s not as performant, so I wouldn’t use it for code. But for small tasks, absolutely.

Here’s the thing that matters, these models are MEANT to be used with the new “subagents” feature that was also launched this week in Codex, all you need to do as... ask!

Just tell Codex “spin up a subagent to do... X” and it’ll do it.

OpenAI shifts focus on AI for engineering and enterprise, acquires Astral.sh makers of UV.

Look, there’s no doubt that OpenAI the absolutely leader in AI, brought us ChatGPT, with over 900M users using it weekly. But they see what every enterprise sees, developers are MUCH more productive (and slowly so are everyone else) when they use tools that can code.

According to WSJ, OpenAI executives will reprioritize some of the side-quests they have (Sora?) to focus on productivity and business. Which essentially means, more Codex, more Codex native, more productivity tools.

With that focus, today they announced that OpenAI / Codex is acquiring Astral, the folks behind the widely popular UV python package manager. This brings strong developer tools firepower to the Codex team, the astral folks are great at writing incredibly fast tools in rust! Looking forward to see how these great folks improve Codex even more.

Jensen Declares Total OpenClaw Victory at GTC, Announces NemoClaw (Github)

This was kind of surreal, NVIDIA CEO Jensen Huang, is famous for doing his stadium size keynote, without a teleprompter, and for the last 10 minutes or so, he went all in on OpenClaw. Calling it “the most important OSS software in history” and outlining how this is the new computer.

That Peter Steinberger with OpenClaw showed the world a blueprint for the new coputer, an personal agentic system, with IO, files, computer use, memory, powered by LLMs.

Jensen did outline that the 3 things that make OpenClaw great are also the things that enterprises cannot allow, write access to your files + ability to communicate externally is a bad combo, so they have launched NemoClaw.

They’ve got a bunch of security researchers to work with OpenClaw team to integrate their new OpenShell sandboxing effort, network guardrails and policy engine integration.

I reminded folks on the pod that the internet was very insecure, there was a time where folks were afraid of using their creditcards online. OpenClaw seems to be speed running that “unsecure but super useful” to “secure because it’s super useful” arc and it’s great to see a company as huge as NVIDIA embrace.

Not to mention that given that agents can run 24/7, this means way more inference and way more chips sold for NVIDIA so makes sense for them, but still great to see!

Manus “my computer” and other companies replicating “OpenClaw” success

This week it became clear, after last weeks Perplexity “computer”, Manus (now part of Meta) has also announced a local extension of their cloud agents, and those two are only the first announcements, it’s clear now that every company dissected OpenClaw’s moment and will be trying to give its users what they want. An agentic always on AI assistant with access to the users files, documents etc.

Claude code added “channels“ support with telegram and discord connectors today, which, also, is one big missing piece of the puzzle for them. Everything is converging on this. Even OpenAI is rumored to consolidate Codex (which sees huge success) with OpenAI and Atlast browser into 1 “mega” APP that would do these things and act as an agent.

ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

MiniMax M2.7: The Model That Built Itself

This one blew me away, it’s not quite open source (yet?) but the MiniMax folks are coming out with a 2.7 version just after their MiniMax 2.5 was featured on our show and .. they are claiming that this model trained itself.

Similarly to Andrej Karpathy’s auto-researcher, the MiniMax folks ran 100+ autonomous optimization loops, t get this model to 56.22% on the hard Swe-bench pro benchmark (close to Opus’s 57.3%!) and this one gets a 88% win rate vs the very excellent MiniMax 2.5.

They used the previous model to build the agent harness and scaffolding, with 1 engineer babysitting these agent, and writing 0 lines of human code, which as we said before, every company will be doing, as we’re staring singularity in the face!

We’ve evaluated this model as well (Wolfram has been busy this week!) and it’s doing really well on WolfBench with 52% average and 64% top score, it’s very close to 5.3 codex on our terminalBench benchmark!

We hope that this model will be open source at some point soon as well!

Cursor drops Composer 2 - nearly matching Opus 4.6, fast version (Blog)

Cursor decided to add to our show’s breaking news record of Thursday releases with a brand new in-house trained Composer 2. This time they released more benchmarks than only their internal “composer bench” and this model looks great! (we are pretty sure it’s a finetune of a chinese OSS model, but we don’t know which)

Getting 61% on Terminal Bench, beating Opus 4.6 is quite a significant achievement, but coupled with the incredible pricing they are offering, $0.5/1Mtok input and $2.50/M output tokens, Cursor is really aiming for the productivity folks and showing that they are more than just an IDE.

Early users are reporting noticeably cleaner code than both Opus and Composer 1.5 — better adherence to clean code principles, smarter multi-file implementations, and strong performance on long-horizon agentic tasks like full API migrations and legacy codebase refactoring. They also shipped a new interface called Glass (in alpha) that’s built for monitoring these long-running agent loops.

Open Source: Mistral is Back, Baby

Mistral Small 4: 119B MoE with 128 experts + Apache 2.0 (X, Blog, HF)

It’s been a while since Mistral dropped something properly open source, and this week they kicked off what looks like their fourth generation with Mistral Small 4. The name is a little funny given the actual size — 119 billion total parameters, 128 experts in the mixture — but with only 6 billion active per token. So you get the knowledge footprint of a massive model but the compute profile of a small one. Very MoE-brained.

The bigger story here is what’s unified inside: this is Magistral (reasoning), Pixtral (multimodal), and Devstral (coding) all rolled into one weights file. Previously you had to choose which Mistral “side quest” model you wanted. Now there’s a reasoning_effort parameter where you dial from none for fast cheap responses all the way up to high for step-by-step thinking, no model switch required.

How does it perform? We ran it through WolfBench and it landed toward the lower end of Wolfram’s current leaderboard — around 17% on the agentic tasks, roughly on par with Nemotron at the same scale. It’s not competing with Opus or GPT-5.4, and we weren’t really expecting it to. What we’re excited about is that it does multimodal, reasoning, and coding in one Apache-licensed package, and people are already running IQ4 quants locally. Shout out to Mistral for the return to open source — it’s been a minute, and the community noticed.

Unsloth Studio: Fine-Tuning Gets a UI (Blog)

Something I think people are sleeping on this week is Unsloth Studio, the open-source web UI that the Unsloth team just launched for local LLM training and inference. Unsloth has been quantizing and compressing models better than basically anyone for a while now — 2x training speed, 70% less VRAM, zero accuracy loss — but that was all code-first. Studio is the no-code interface layer on top of all of that.

The numbers: supports 500+ models across text, vision, audio, and embeddings. It runs 100% offline with no telemetry. Julien Chaumond, the CTO of Hugging Face, confirmed it trains successfully on a Colab Pro A100. There’s even a free Colab notebook for models up to 22B parameters. For folks who want to fine-tune models overnight without spinning up cloud infra or wrestling with Docker, this is a genuine leap forward. Nisten compared it to what LM Studio did for local inference — making something that used to require deep expertise suddenly accessible to anyone. I think that comparison is spot on, and I want to get Daniel and the Unsloth team on the show to dig into this properly.

This Week’s Buzz: W&B iOS App & The Overthinking Paradox

The iOS App is Finally Here (app store)

Okay, I’m going to do a quick applause. 👏

The most requested feature in Weights & Biases history is now live: the W&B iOS mobile app. If you’ve ever kicked off a training run overnight and woken up to find it crashed at hour two without knowing about it until morning, you understand exactly why people have been begging for this. Live metrics, loss curves, KL divergence — all right on your phone. And native push notifications for alerts! The second your run fails or a custom metric crosses a threshold, you get a notification on your phone.

Please give us feedback through the app, the iOS team is actively building on top of this. Get it on the App Store and let us know what you need.

WolfBench insight: More Thinking ≠ Better Agents

This is one of the more counterintuitive findings we’ve surfaced from the W&B + Wolf Bench collaboration, and Wolfram laid it out really clearly.

He tested Opus 4.6 and GPT-5.4 across different thinking/reasoning effort levels inside the Terminal Bench 2.0 agentic benchmark framework — using both the default Terminus 2 harness and the OpenClaw agent framework. For GPT-5.4, the pattern was exactly what you’d expect: higher reasoning effort gets better results. At extra-high, it hit 71% with 85% ceiling on tasks it could solve.

For Opus 4.6, though? Turning it up to the maximum thinking level made it significantly worse. From 71% on standard settings all the way down to 59% on max reasoning. It lost tasks it had been reliably solving before. Wolfram dug into the traces in Weave and found out why: the model was overthinking. In an agentic benchmark where you have a one-hour time limit per task, spending ten minutes reasoning about what terminal command to try — and then getting an error — and then spending another ten minutes reasoning about it — is catastrophically inefficient.

We’ll keep you up to date with more Alpha from our bench efforts! Stay tuned and checkout wolfbench.ai

Voice & Audio

xAI relaunched the Grok Text-to-Speech API (try it)

It’s actually a pretty full-featured release right away. Multiple voices, expressive controls, WebSocket streaming, multilingual support, and the whole platform feel suggests xAI is very much trying to build a serious multimodal API stack, not just throw out a toy demo.

The inline control tags are the fun part. You can embed pauses, laughter, whispers, breathing cues, all that. Those controls matter a lot for agents because the difference between “reads text out loud” and “feels usable in a voice interaction” usually lives in those details. As you can see in the video.. it’s.. not perfect.. yet? but pretty fun!

But the thing I personally had the most fun with this week was Fish Audio. We didn’t get to cover it properly last week, and when I played with it more this week, I came away really impressed. It’s fast, expressive, open source, and the voice control vibe is genuinely cool.

My favorite moment was not even a benchmark thing. I used Fish Audio with an agent setup to make a character voice inspired by Project Hail Mary, then had my kid talk to it. And the result was weirdly magical. If you remember the Audio book of Hail Mary, fish audio was able to get the voice juuuust right + Opus via OpenClaw obliged with a great skill to talk like rocky. I won’t post this for obvious copyright reasons but I showed it on the live show, at the end.

Parting thoughts: I was hoping for a quieter week this week as I was sick, but it didn’t materialize, I should stop hoping for quiet weeks I think. After all, this is how the singularity starts, faster and faster developments, models that train themselves, every company becomes an agentic company.

We’ll keep you posted on the most important breakthroughs, cover breaking news and bring interesting folks to the show as guests.

Thank you for reading, see you next week 👋

ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

ThursdAI - Mar 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

* Big CO LLMs + APIs

* Anthropic makes Opus 4.6 with 1M context the default claude code - at the same price (X)

* OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship cost (X, Announcement, Announcement)

* Xiaomi - Omni modal and language only 1T parameters - MiMo (X)

* Google AI Studio gets a full-stack vibe coding overhaul with Antigravity agent, Firebase integration, and multiplayer support (X, Blog, Announcement)

* MiniMax M2.7: the first self-evolving model that helped build itself, hitting 56.22% on SWE-Bench Pro (X, X, Announcement)

* Cursor launches Composer 2, their first proprietary frontier coding model beating Opus 4.6 at a fraction of the cost (X, Blog)

* Open Source LLMs

* Mamba-3 drops with three SSM-centric innovations: trapezoidal discretization, complex-valued states, and MIMO formulation for inference-first linear models (X, Arxiv, GitHub)

* H Company releases Holotron-12B, an open-source hybrid SSM model for computer-use agents that hits 8.9k tokens/sec and jumps WebVoyager from 35.1% to 80.5% (X, X, HF, Blog)

* Hugging Face’s Spring 2026 State of Open Source report reveals 11M users, 2M models, and China dominating 41% of downloads as open source becomes a geopolitical chess board (X, Blog, X, X)

* Unsloth launches open-source Studio web UI for local LLM training and inference with 2x speed and 70% less VRAM (X, Announcement, GitHub)

* Astral (Ruff, uv, ty) joins OpenAI’s Codex team (announcement , blog , Charlie Marsh)

* Mistral Small 4: 119B MoE with 128 experts, only 6B active per token, unifying reasoning, multimodal, and coding under Apache 2.0 (X, Blog, HF)

* Tools & Agentic Engineering

* NVIDIA GTC: Jensen Huang declares “Every company needs an OpenClaw strategy,” announces NemoClaw enterprise platform (X, TechCrunch, NemoClaw)

* OpenAI ships subagents for Codex, enabling parallel specialized agents with custom TOML configs (X, Announcement, GitHub)

* Manus (now Meta) launches ‘My Computer’ desktop app, bringing its AI agent from the cloud onto your local machine for macOS and Windows (X, Blog)

* This weeks Buzz

* Weights & Biases launches iOS mobile app for monitoring AI training runs with crash alerts and live metrics (X, Announcement)

* GPT 5.4 went from worst to best on WolfBenchAI after an OpenClaw config fix exposed a max_new_tokens bottleneck (X, X, X)

* Voice & Audio

* xAI launches Grok Text-to-Speech API with 5 voices, expressive controls, and WebSocket streaming (X, Announcement)

* AI Art & Diffusion & 3D

* NVIDIA DLSS 5 is making waves with a new generative AI filter (Blog)

Hey, Alex here 👋 Today was a special episode, as ThursdAI turns 3 🎉

We’ve been on air, weekly since Pi day, March 14th, 2023. I won’t go too nostalgic but I’ll just mention, back then GPT-4 just launched with 8K context window, could barely code, tool calls weren’t a thing, it was expensive and slow, and yet we all felt it, it’s begun!

Fast forward to today, and this week, we’ve covered Andrej Karpathy’s mini singularity moment with AutoResearcher, a whole fruit fly brain uploaded to a simulation, China’s OpenClaw embrace with 1000 people lines to install the agent. I actually created a new corner on ThursdAI, called it Singularity updates, to cover the “out of distribution” mind expanding things that are happening around AI (or are being enabled by AI)

Also this week, we’ve had 3 interviews, Chris from Nvidia came to talk to use about Nemotron 3 super and NVIDIA’s 26B commitment to OpenSource, Dotta (anon) with his PaperClips agent orchestration project reached 20K Github starts in a single week and Matt who created /last30days research skill + a whole bunch of other AI news! Let’s dive in.

ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Singularity updates - new segment

Andrej Karpathy open sources Mini Singularity with Auto Researcher (X)

If there’s 1 highlight this week in the world of AI, it’s this. Andrej, who previously started the AutoPilot program in Tesla, and co-founded OpenAI, is now, out there, in the open, just.. doing stuff like invent a completely autonomous ML research agent.

Andrej posted to his almost 2M followers that he opensourced AutoResearch, a way to instruct a coding agent to do experiments against a specific task, test the hypothesis, discard what’s not working and keep going in a loop, until.. forever basically. In his case, it was optimizing speed of training GPT-2. He went to sleep and woke up to 83 experiments being done, with 20 novel improvements that stack on top of each other to speed up the model training by 11%, reducing the training time from 2.02 hours to 1.8 hours.

The thing is, this code is already hand crafted, fine tuned and still, AI agents were able to discover new and novel ways to optimize this, running in a loop.

Folks, this is how the singularity starts, imagine that all major labs are now training their models in a recursive way, the models get better, and get better at training better models! Reminder, OpenAI chief scientist Jakub predicted back in October that OpenAI will have an AI capable of a junior level Research ability by September of this year, and it seems that... we’re moving quicker than that!

Practical uses of autoresearch

This technique is not just for ML tasks either, Shopify CEO Tobi got super excited about this concept, and just posted as I’m writing this, that he set an Autoresearch loop on Liquid, Shopify’s 20 year old templating engine, with the task to improve efficiency. His autoresearch loop was able to get a whopping 51% render time efficiency, without any regressions in the testing suite. This is just bonkers. This is a 20 year old, every day production used template. And some LLM running in a loop just made it 2x faster to render, just because Karpathy showed it the way.

I’m absolutely blown away by this, this isn’t a model release, like we usually cover on the pod, but still, a significant “unhobbling” moment that is possible with the current coding agents and models. Expect everything to become very weird from here on out!

Simulated fruit fly brains - uploaded into a simulator

In another completely bonkers update that I can barely believe I’m sending over, a company called EON SYSTEMS, posted that they have achieved a breakthrough in brain simulation, and were able to upload a whole fruit fly brain connectome, of 140K neurons and 50+ million synapses into a simulation environment.

They have... uploaded a fly, and are observing a 91% behavioural accuracy. I will write this again, they have uploaded a fly’s brain into a simulation for chirst sake!

This isn’t just an “SF startup” either, the board of advisors is stacked with folks like George Church from Harvard, father of modern genome sequencing, Stephen Wolfram who needs no introduction but one of the top mathematicians in the world, whos’ thesis is “brains are programs”, Anders Sandberg from Oxford, Stephen Larson who apparently already uploaded a worms brain and connected it to lego robots before. These folks are gung ho on making sure that at some point, human brains are going to be able to get uploaded, to survive the upcoming AI foom.

The main discussion points on X were around the fact that there was no machine learning here, no LLMs, no attention mechanisms, no training. The behaviors of that fly were all a result of uploading a full connectome of neurons. This positions connectome (the complete diagram of a brain with neurons and connections) as an ananalouge to an pre-trained LLM network for biological intelligence.

I encourage everyone who’s reading this, to watch Pantheon on Netflix, to understand why this is of massive importance. Combined with the above Autoresearch, things are going to go very fast here. The next step is uploading a mouse brain, which will be a 500x Neurons and 2000x more synapses, but if we’re looking at the speed with which AI is improving, that’s NOT out of the realm of possibility for the next few years!

OpenClaw Mania Sweeps China: Thousand-Person Lines & Government Subsidies, Grandmas raising a “red lobster”

They’re calling it “raising a red lobster” (养小龙虾). That’s the phrase that swept Chinese social media for what is, at its core, installing an open source GitHub project on your laptop. Grandmas are doing it. Mac Minis are sold out. A cottage industry of paid installers popped up overnight on Xiaohongshu, charging up to $100 for an in-person setup. And yes, there are now also people charging to uninstall it.

On March 6th, roughly a thousand people lined up outside Tencent’s Shenzhen HQ for free OpenClaw installation. Appointment slots ran out within an hour. People brought NAS drives, MacBooks, mini PCs. Tencent engineers set up folding tables and just... started installing OpenClaw for strangers. I have pictures. I’m not making this up.

All five major Chinese cloud providers jumped in simultaneously: Tencent Cloud, Alibaba Cloud, ByteDance Volcano Engine, JD.com Cloud, and Baidu Intelligent Cloud, each racing to offer one-click OpenClaw deployment. Why? Follow the money. Per HelloChinaTech, ByteDance, Alibaba, and Tencent spent roughly $60B combined on AI infrastructure. Chatbots don’t burn enough tokens to justify that spend. But a single OpenClaw instance runs 24/7 and consumes 10-100x more tokens per day than a chatbot user. Every install is round-the-clock API revenue. The cheaper the models get, the more people run agents, the more infra gets sold. Self-reinforcing loop.

Local governments are pouring fuel on the fire. Shenzhen’s Longgang district is offering up to 2M yuan ($290K) per project. Hefei and Wuxi are going up to 10M yuan ($1.4M), plus free computing, office space, and accommodation for “one-person companies.” Meanwhile, China’s central cybersecurity agency issued TWO warnings, banning banks and state agencies from installing OpenClaw. So local governments are subsidizing it while the central authority is trying to pump the brakes. Peak 2026.

With nearly half of all 142,000+ publicly tracked OpenClaw instances are now from China. OpenClaw is the most-starred GitHub repo in history, surpassing Linux’s 30-year record in just 100 days. Device makers are piling on too — Xiaomi announced “miclaw” for smartphones, MiniMax built MaxClaw, Moonshot AI built a hosted version around Kimi.

Now, Ryan was honest on the show and I want to echo that honesty here: OpenClaw is still hard to get working. There are many failure states. It’s not “install and go to the beach.” Wolfram compared it to Linux in the late ‘90s — painful to set up, but if you push through, you can see the future behind the friction. This is real technology with real limitations, and a lot of disappointed folks in China are watching tokens burn with no actual work getting done.

But here’s the thing I keep coming back to. The memetic velocity of OpenClaw is unlike anything I’ve seen in tech. It’s not just a tool, it’s a concept that penetrated the cultural resistance to AI. People who are scared of terminals, people who’ve never touched GitHub — they’re standing in line for this. I broke through that resistance with my own fiancée. She’s now running two OpenClaws. Not enough for her. She needs another one.

Every major US lab is watching this closely. OpenAI brought Peter Steinberger on staff. Perplexity just announced they’re building a local agent for Mac. Anthropic has Claude Cowork. This is where all of computing is headed — always-on, autonomous, personal AI that actually does things for you. OpenClaw is the first front door, not the final destination. But what a front door it is.

ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Open Source: Nvidia Goes All In with Nemotron 3 Super 120B (X, Blog, HF)

We had Chris Alexiuk from Nvidia join us — a friend from a dinner Nisten and I hosted in Toronto. Chris is basically “NeMo” embodied, sitting at the intersection of product and research, and he gave us the full breakdown on what might be the most complete open-source model release we’ve seen from a major lab.

Here are the numbers: 120B total parameters, 12B active during inference (it’s a Mixture of Experts), 1 million token context window, and a hybrid Mamba-Transformer architecture they call “Hybrid Mamba Latent MoE with Multi-Token Prediction.” It’s hitting 450 tokens per second on the Terminal Bench leaderboard — faster than any other model on there. Modal is reporting over 50% faster token generation compared to other top open models.

What Chris was emphatic about — and I want to highlight this — is that “most open” is a real designation here. They released the model checkpoint in three precisions (BF16, FP8, NVFP4), the base checkpoint before post-training, the SFT training data, and in a move that genuinely surprised people, pre-training data and a full end-to-end training recipe. You can, in theory, reproduce their training run. That’s rare. That’s a real commitment to open source.

There’s also a huge piece of news in the background here: there’s a confirmed report that Nvidia will spend $26 billion over the next five years building the world’s best open source models. Jensen presumably has GTC remarks incoming on this. America is genuinely back in the open source AI race, and it’s Nvidia leading the charge. Chris has been in the open source world since the Hugging Face early days and said it feels genuine inside the company — not a PR exercise. And I tend to believe him. Now, all eyes are on GTC next week!

I ran Nemotron 3 Super with my own OpenClaw instance yesterday via W&B inference and it’s genuinely fast and capable. At $0.20/M input tokens and $0.80/M output tokens on W&B inference, it’s not going to replace Opus for your hardest tasks — but for running an always-on agent that needs to be cost-efficient? It’s an incredible option. More on that in the this weeks buzz section below.

Tools & Agentic Engineering

Paperclip: Zero Human Companies, Now Open Source (Github)

We had the anonymous Dotta on the show — the first AI video avatar anon person to join ThursdAI — to talk about Paperclip, an open source agent orchestration framework that hit 20,000 GitHub stars in its first week. The premise is simple and audacious: build zero-human companies.

Now this may sound familiar to you, as we had Ben from Polsia on just two weeks ago, which is a similar concept, but Paperclip is an OpenSource project, which you can run right now on your own.

The core “thing” that got me excited about Paperclip is that you can “hire” your own existing OpenClaw agents, or Cursor or Codex or whatever else to play roles in this autonomous company. The premise is simple, you’re the board of directors, you hire an AI Agent CEO, and it then asks you if it needs to “hire” more AI agents to do tasks autonomously. These tasks all live inside Paperclip interface, and you or your Agents can open them.

The core concept of this whole system is the heartbeat concept, each agent receives their own instructions on what to do every time they are “woken up” by a timer. This is what’s driving the “autonomous” part of the whole thing, but it’s also what’s eating the tokens up, even if there’s no work being done, agents are still burning tokens asking “is there work to be done?”

Dotta gave us a great metaphor, asking if we saw the movie Memento, where the protagonist lost his memory and every time he woke up, he woke up with a blank slate, and had to reconstruct the memories. AI agents are like the memento man, and Paperclip is an attempt to give those agents the whole context so they can continue working on your tasks productively. Dotta told us that the future of Paperclip is the ability to “fork” entire companies, structures that will actually run and do things on your behalf. Looking forward to that future, but for now I will be turning off my Paperclip interface as it’s costing me real money without the need.

Symphony: Agents Writing Their Own Jira Tickets

We mentioned Symphony last week, and I texted Ryan the link before the show, and voila, of course, he set it up and went viral, yet again! We’re so lucky to have Ryan on the show to tell us from first hand experience what it’s like to run this thing.

Symphony was open sourced by OpenAI last week, and it’s basically an instruction manual for how to run agents autonomously via Linear ticketing system. (Github)

The highlight for Ryan was, the whole system is running creating pull requests while he’s a sleep, and at some point, he noticed a ticket that he didn’t create. One of the agents found a bug, and created a very detailed ticket for him to approve.

I’m just happy that I can keep even my co-hosts up to date hehe

This weeks buzz - we’ve got skills and nemotrons!

Look, we told you about Skills in the start of the year, since then, via OpenClaw, Hermes Agent, Claude Code, they exploded in popularity. One downside of skills is, it’s very easy to make a bad one! So, we’re answering the challenge, and are publishing the official wandb skill 🎉

Installing it is super simple, npx skills add wandb/skills and voila, your agents are now officially “I know kung fu” pilled with the best Weights & Biases practices. For both Weave and Models 👏 Please give us feedback on Github if you have used the skills! Github

Also, we’ve partnered with Nvidia to support the best US open source model on day 0, and we have Nemotron 3 Super on our inference service, for all to use at $0.20/1Mtok! It’s super easy to setup with something like Hermes Agent or OpenClaw and runs really really fast! Check it out here.

Is it going to perform like Opus 4.6? No. But are you going to run Opus 4.6 at 20 cents per million? Also no.

Gemini drops SOTA embeddings and gets dethroned 2 days later live on the show.

This always happens, but I didn’t expect this to happen in a fairly niche segment of the AI world... multimodal embeddings!

Gemini posted an update earlier this week with Gemini Embeddings 2.0, a way to unify images, text, video, audio embeddings under 1 roof, and posted a SOTA embedding model!

Then, just as we launch the show, a friend of the pod Benjamin Clavie, drops me a DM, basically saying that his company Mixbread is going to deploy an embedding model that will beat Gemini Embedding 2 on almost every benchmark on that table, and then... they did!

The most notable (and absolutely crazy) jump in this comparison is, the LIMIT benchmark, where they achieved a 98% score vs Gemini’s ... 6.9 percent. I didn’t believe this at first, but asked Ben to explain the findings, and he did. Congrats to folks for moving the search space forward every 2 days!

Grok 4.20 in the API for $2/1Mtok

Elon Musk and XAI co finally released Grok 4.20 in the API. Look I said what I said about XAI models, they are great for research, and for factuality, but they aren’t beating the major labs. The last firing of almost of XAI folks doesn’t help either. So this model was not “released” in any traditional sense, there’s no benchmarks, no evals, and everyone who got access to it evaluated it and, it’s no better than GLM5 on many benchmarks. So it does makes sense to release it quietly.

It is very fast though, and again, for research and for X access, it’s an absolute beast, so I’ll be trying this out!

Parting thoughts and a small reflection. For the past 3 years, we’ve had a front-row seat to the singularity shaping up. 2.5 years ago, I went all in, decided to pivot into podcasting full time. In those years, ThursdAI became known, we’ve had guests from nearly all major AI labs (including Chinese ones, for which I’m particularly proud), I got to meet with executives, ask leaders questions about where this is all going, and most of all, share this journey with all of you, candidly. We rarely do hype on the show, we don’t speculate, we try to do a positive outlook on the whole thing, and counter doomerism, as there’s too much of that out there.

I am very glad this resonates, and continue to be thankful for your attention! If you wanted to give us any kind of a birthday present, subscribe or give us a 5 star review on Apple Podcasts or Spotify, it’ll greatly help other folks to discover us.

See you next week,

Alex 🫡

ThursdAI - Mar 12, 2026 - TL;DR

* Hosts and Guests

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

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

* Chris Alexiuk from Nvidia (@llm_wizard) - Nemotron

* @dotta - creator of Paperclip.ing AI agent orchestration framework

* Matt Van Horn - @mvanhorn - creator of @slashlast30days

* Singularity updates

* Andrej Karpathy’s autoresearch achieves 11% speedup on GPT-2 training through autonomous AI agent experimentation (X, GitHub, GitHub)

* Eon Systems uploads first complete fruit fly brain to a physics-simulated body, achieving 91% behavioral accuracy (X, Announcement, Announcement)

* OpenClaw mania sweeps China as all five major cloud providers race to support it (HelloChinaTech, Reuters, SCMP, MIT Tech Review)

* Big CO LLMs + APIs

* xAI quietly releases Grok 4.20 API with massive 2M token context window and multi-agent capabilities (X, Blog)

* Google launches Gemini Embedding 2, the first natively multimodal embedding model supporting text, images, video, audio, and PDFs in a unified vector space (X, Announcement)

* Open Source LLMs

* NVIDIA launches Nemotron 3 Super: 120B open MoE model with 1M context window designed for agentic AI at 5x higher throughput (X, Announcement)

* MiroMind releases MiroThinker-1.7 and H1 - open-source research agents with 256K context, 300 tool calls, achieving SOTA on deep research benchmarks (X, HF, HF, HF)

* Covenant-72B: World’s largest permissionless decentralized LLM pre-training achieves 72B parameters on Bittensor with 146x gradient compression (X, Arxiv, HF, HF)

* Tools & Agentic Engineering

* ACP is the open standard that lets any AI coding agent plug into any editor — and this week Cursor officially joined the registry, meaning you can now run Cursor’s agent inside JetBrains IDEs (JetBrains blog, Cursor blog)

* This weeks Buzz

* W&B launches official Agent Skills for coding agents, turning experiment dashboards into terminal queries (X, Announcement, Announcement)

* Video

* LTX-2.3 — Lightricks open-source video model (GitHub, HF,Blog)

* Voice & Audio

* Fish Audio launches S2: Open-source TTS with sub-150ms latency and absurdly controllable emotion (X, HF, Blog, Announcement)

* Show notes and links

* Paperclip.ing by Dotta ( @dotta) - Github

* Last30days skill by Matt Van Horn Github

* Agency Agents repo Github

* OpenAI Symphony (Github)

* Mixbread Embeddings (X)

Hey folks, Alex here, let me catch you up!

Most important news about this week came today, mid-show, OpenAI dropped GPT 5.4 Thinking (and 5.4 Pro), their latest flagship general model, less autistic than Codex 5.3, with 1M context, /fast mode and the ability to steet it mid-reasoning. We tested it live on the show, it’s really a beast.

Also, since last week, Anthropic said no to Department of War’s ultimatum and it looks like they are being designated as supply chain risk, OpenAI swooped in to sign a deal with DoW and the internet went ballistic (Dario also had some .. choice words in a leaked memo!)

On the Open Source front, the internet lost it’s damn mind when a friend of the pod Junyang Lin, announced his departure from Qwen in a tweet, causing an uproar, and the CEO of Alibaba to intervene.

Wolfram presented our new in-house wolfbench.ai and a lot more!

P.S - We acknowledge the war in Iran, and wish a quick resolution, the safety of civilians on both sides. Yam had to run to the shelter multiple times during the show.

ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

OpenAI drops GPT 5.4 Thinking and 5.4 Pro - heavy weight frontier models with 1M context, /fast mode, SOTA on many evals

OpenAI actually opened this week with another model drop, GPT 5.3-instant, which... we can honestly skip, it was fairly insignificant besides noting that this is the model that most free users use. It is supposedly “less cringe” (actual words OpenAI used). We all wondered when 5.4 will, and OpenAI once again proved that we named the show after the right day. Of course it drops on a ThursdAI.

GPT 5.4 Thinking is OpenAI latest “General” model, which can still code, yes (they folded most of the Codex 5.3 coding breakthroughs in here) but it also shows an incredible 83% on GDPVal (12% over Codex), 47% on Frontier Math and an incredible ability to use computers and browsers with 82% on BrowseComp beating Claude 4.6 at lower prices than Sonnet!

GPT 5.4 is also ... quite significantly improved at Frontend design? This landing page was created by GPT 5.4 (inside the Codex app, newly available on Windows) in a few minutes, clearly showing significant improvements in style.

I built it also to compare prices, all the 3 flagship models are trying to catch up to Gemini in 1M context window, and it’s important to note, that GPT 5.4 even at double the price after the 272K tokens cutoff is still.... cheaper than Opus 4.6. OpenAI is really going for broke here, specifically as many enterprises are adopting Anthropic at a faster and faster pace (it was reported that Anthropic is approaching 19B ARR this month, doubling from 8B just a few months ago!)

Frontier math wiz

The highlight from the 5.4 feedback came from a Polish mathematician Bartosz Naskręcki (@nasqret on X), who said GPT-5.4 solved a research-level FrontierMath problem he had been working on for roughly 20 years. He called it his “personal singularity,” and as overused as that word has become, I get why he said it. I’ve told you about this last week, we’re on the cusp.

Coding efficiency

There’s tons of metrics in this release, but I wanted to highlight this one, where it may seem on first glance that on SWE-bench Pro, this model is on par with the previous SOTA GPT 5.3 codex, but these dots here are thinking efforts. And a medium thinking effort, GPT 5.4 matches 5.3 on hard thinking! This is quite remarkable, as lower thinking efforts have less tokens, which means they are cheaper and faster ultimately!

Fast mode arrives at OpenAI as well

I think this one is a direct “this worked for Anthropic, lets steal this”, OpenAI enabled /fast mode that.. burns the tokens at 2x the rate, and prioritizes your tokens at 1.5x the speed. So, essentially getting you responses faster (which was one of the main complains about GPT 5.3 Codex). I can’t wait to bring the fast mode to OpenClaw with 5.4, which will absolutely come as OpenClaw is part of OpenAI now.

There’s also a really under-appreciated feature here that I think other labs are going to copy quickly: mid-thought steering. OpenAI now lets you interrupt the model while it’s thinking and redirect it in real time in ChatGPT and iOS. This is a godsend if you’re like me, sent a prompt, seeing the model go down the wrong path in thinking... and want to just.. steer it without stopping!

Anthropic is now designated as supply-chain risk by DoW

Last week I left you with a cliffhanger: Anthropic had received an ultimatum from the Department of War (previously the Department of Defense) to remove their two remaining restrictions on Claude — no autonomous kill chain without human intervention, and no surveillance of US citizens. Anthropic’s response? “we cannot in good conscience acceede to their request”

So much has happened since then; US President Trump said “I fired Anthropic” referring to his Truth Social post demanding intelligence agencies drop the use of Claude (which apparently was used in the war with Iran regardless); Sam Altman announced that OpenAI has agreed to DoW and will provide OpenAI models, causing a lot of people to cancel their OpenAI subscriptions, and later apologizing for the “rushed rollout”; Dario Amodei posted a very contentious internal memo that leaked, in which he name-called the presidency, Sam Altman and his motives, Palantir and their “safety theater”, for which he later apologized

Honestly this whole thing is giving me whiplash trying to follow, but here’s the facts. Anthropic is now the first US company in history, being designated “supply chain risk” which means no government agency can use Claude, and neither can any company that does contracts with DoW.

Anthropic says it’s illegal and will challenge this in court , while reporting $19B in annual recurring revenue, nearly doubling since last 3 months, and very closely approaching OpenAI at $25B.

Look, did I want to report on this stuff when I decided to cover AI? no... I wanted to tell you about cool models and capabilities, but the world is changing, and it’s important to know that the US Government understands now that AI is inevitable, and I think this is just the first of many clashes between tech and government we’ll see. We’ll keep reporting on both. (but let me know in the comments if you’d prefer just model releases)

OpenAI’s GPT-5.3 Instant Gets Less Cringe, Google’s Flash-Lite Gets Faster (X, Announcement)

We also got two fast-model updates this week that are worth calling out because these are the models that often end up powering real product flows behind the scenes. As I wrote before, OpenAI’s instant model is nothing to really mention, but it’s worth mentioning that OpenAI seems to have an answer for every Gemini release.

Gemini released Gemini Flash-lite this week, which boasts an incredible 363 tokens/s speed, which doing math at a very good level, 1M context and great scores compared to the instant/fast models like Haiku from Anthropic. Folks called out that this model is more expensive than the previous 2.5 Flash-lite. But with 86.9% on GPQA Diamond beating GPT-5 mini, and 76.8% MMMU-pro multimodal reasoning, this is definitely worth taking a look at for many agentic, super fast responses!

For example, the heartbeat response in OpenClaw.

Qwen 3.5 Small Models & The Departure of Junyang Lin (X, HF, HF, HF)

Alibaba’s Qwen team continued releasing their Qwen 3.5 family, this time with Qwen 3.5 Small, a series of models at 0.8B, 2B, 4B, and 9B parameters with native multimodal capabilities. The flagship 9B model is beating GPT-OSS-120B on multiple benchmarks, scoring 82.5 on MMLU-Pro and 81.7 on GPQA Diamond. These models can handle video, documents, and images natively, support up to 201 languages, and can process up to 262K tokens of context. And.. they are great! They are trending on HF right now.

What’s also trending is, tech lead for Qwen, a friend of the pod Junyang Lin, has posted a cryptic tweet that went viral with over 6M views. There was a lot of discussions on why he and other Qwen leads are stepping away, what’s goig to happen with the future of OpenSource. The full picture seems to be, there are a lot of internal tensions and politics, with Junyang being one of the youngest P10 leaders in the Alibaba org.

A Chinese website 36KR ( Kind of like a chinese techcrunch) reported that this matter went all the way up to Alibaba CEO, who is no co-leading the qwen team, and that this resignation was related to an internal dispute over resource allocation and team consolidation, not a firing.

I’m sure Junyang is going to land somewhere incredible and just wanted to highlight just how much he did for the open source community, pushing Qwen relentlessly, supporting and working with a lot of inference providers (and almost becoming a co-host for ThursdAI with 9! appearances!)

StepFun releases Step 3.5 Flash Base (X, HF, HF, Announcement, Arxiv)

Speaking of Open Source, StepFun just broke through the noise with a new model, a 196B parameter sparse Mixture of Experts model activating just 11B parameters when ran. It has some great benchmarks, but the main thing is this: they are releasing the pretrained base weights, a midtrain checkpoint optimized for code and agents, the complete SteptronOSS training framework, AND promising to release their SFT data soon - all under Apache 2.0!

Technically the model looks strong too, with multi-token prediction, 74.4% on SWE-bench verified bench (though, as we told you last week, it’s.. no longer trusted) and full apache 2!

This Week’s Buzz: presenting Wolfbench.ai

I’m so excited about this weeks “this weeks buzz”, Wolfram has been hard at work preparing and presenting a new framework to test out these models, and named it wolfbench.ai

Wolfbench is our attempt to compare how the same model performs via different agentic harnesses like ClaudeCode, OpenClaw and Terminalbench’s own Terminus.

You can check out the website on wolfbench.com but the short of it is, a single number is not telling the full story.

Wolf Bench breaks it into a four-metric framework: the average score across runs, the best single run, the ceiling (how many tasks can the model solve at least once across all runs), and the floor (how many tasks does it solve consistently across every single run). That last one is what I find most illuminating. Opus 4.6 might be able to solve 88% of Terminal Bench tasks on average, but only about 55% of tasks it solves every single time. Reliability matters enormously for agents, and benchmarks almost never surface this.

If you want to run your own evals with the same config, reach out to Wolfram—he’s open to community contributions. Wolfram has also already kicked off a Wolf Bench run on GPT-5.4 since we tested it live today, so stay tuned for those results.

There’s quite a few more releases we didn’t have time to get into on the show given the GPT 5.4 drop, you’ll find all those links in the show notes!

Next week will mark 3 years since I’ve started talking about AI on the internet and created ThursdAI (It was March 14th, 2023, same day as GPT4 launched) and we’ll have a little celebration, I do hope you join us live 🔥

As a birthday present, you may choose to share ThursdAI with a friend or two, or rate us in your podcast player of choice! See you next week,

Alex 🫡

ThursdAI - Mar 05, 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

* Big CO LLMs + APIs

* OpenAI launches GPT-5.4 Thinking and Pro (X, X, X, X)

* Anthropic, Dept of War and OpenAI walk into a bar

* Alibaba Qwen departures: Friend of the pod JunyangLin and Binyuan Huy both depart Qwen (X)

* OpenAI Rolls Out GPT-5.3 Instant (X)

* Google launches Gemini 3.1 Flash-Lite (X, Announcement)

* Evals and Benchmarks

* MarinLab shows degradation in Opus 4.6 (X)

* B******t Bench from Peter Gostev (X)

* Open Source LLMs

* StepFun releases Step 3.5 Flash Base models (X, HF, HF, Announcement, Arxiv)

* Alibaba releases Qwen 3.5 Small Model Series (X, HF, HF, HF)

* Yuan 3.0 Ultra (X, Blog, HF)

* Tools & Agentic Engineering

* Cognition: SWE-1.6 preview (X, Blog)

* OpenAI launches Codex app on windows (X)

* Google released Google Workspace CLI (X)

* OpenAI released Symphony (Github)

* This weeks Buzz

* Early preview of Wolf Bench (wolfbench.ai) from W&B

* AI Art & Diffusion & 3D

* Black Forest Labs introduces Self-Flow (X, Announcement)

Hey, it’s Alex, let me tell you why I think this week is an inflection point.

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.

Thanks for reading ThursdAI - Highest signal weekly AI news show! This post is public so feel free to share it.

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/)

Hey, it’s Alex, let me catch you up!

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)

Hey dear subscriber, Alex here from W&B, let me catch you up!

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)

Hey, Alex from W&B here 👋 Let me catch you up!

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)

Hey guys, Alex here 👋 This week was so dense, that even my personal AI assistant Wolfred was struggling to help me keep up! Not to mention that we finally got to try one incredible piece of AI tech I’ve been waiting to get to try for a while!

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

Hey! Alex here, with another weekly AI update!

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)