It’s one of those Thursday’s where no matter how well I prep, the big AI labs are hell bent to show up before each other. Alibaba dropped Qwen 3.6 with Apache 2, confirming their commitment to Open Source, then Anthropic released Claude Opus 4.7 (not quite Mythos) and OpenAI followed with a huge Codex update that includes Computer Use among other things. The highlight of Computer User is the background usage, more on that below. This is all just from today!

Previously in the week we had 2 incredible 3D world generators, Lyra 2.0 from Nvidia and HYWorld 2 from Tencent, Windsurf dropping 2.0 version with Devin integration and Google releasing a Gemini TTS, with over 90+ languages support and incredible emotions range, and Baidu open sources Ernie Image, rivaling Nano Banana.

Today on the show we had 3 awesome guests, Theodor from Cognition joined to cover the new Windsurf, Kwindla is back on the show to talk about “the side project that escaped containment” Gradient-Bang, a multi agent, voice based space game and Trevor from Marimo joined to talk about pairing your agents with a Marimo notebook. Let’s dive in! 👇

Codex can now really use your computer: OpenAI updates Codex with CUA, Image Generation, Browser, SSH (X, Blog)

Codex from OpenAI has been the major focus inside OpenAI for a while now. We’ve reported previously that OpenAI is closing down SORA and other “side-quests” to focus, and that they will join Codex, ChatGPT and the Atlas browser into one “superapp” and today, it seems, that we’ve gotten an early glimpse of what that app will be.

The Codex team (which seems to be growing from day to day), have been on a TEAR feature wise lately, trying to beat Claude Code, and they pushed an update with a LOT of features and updates, among them a new memory system, internal browser and image generation.

The highlight for me though, was absolutely the polished computer use experience. Computer use is not new, Claude has a computer use feature flag, many others. Hell, we told you about computer use with Open Interpreter, back in Sep of 2023. But, this.... this feels different.

You see, OpenAI has quietly purchased a company called Software Apps Inc, that almost launched a macos AI companion a year ago called Sky. This team is obsessed with Mac, and somehow, they were able to build a magical experience, a huge part of which, is the fact that they are controlling the mac, in the background. This is like black magic stuff. You work on one document, Codex clicks buttons and does things in another, without interrupting you.

You may ask, Alex, why do you even care so much about computer use, when most of the work happens in the browser anyway, and Claude (and Codex) can control my browser anyway?

Well, true, but not ALL work is happening there, for example, file system integration. It’s notoriously big part of browser automation that fails, when you need to upload/download files. I’ve spent countless cycles trying to get this to work with OpenClaw, and this, just does it. This closes the loop between knowledge work in the browser (yes, this thing can use your browser) and the broader OS.

It’s so so polished, I truly recommend you try it. It’s as easy as @ tagging any app that you have running and asking Codex to do stuff there. Pro Tip: Enable fast mode for a much smoother experience.

Anthropic Opus 4.7 is here, not quite Mythos, 64.3% Swe-bench Pro, tuned for long running tasks (X, System Card)

What is there to say? Is this the model we expected from Anthropic after releasing the news about Claude Mythos last week? no. But hey, we’ll take it. I new Claude Opus, with a significantly improved multimodality capabilities, and a long horizon coding task improvements? For the same price?

Well, not quite! Apparently, this model could be a “from scratch” trained model, given that the tokenizer (the thing that converts words into tokens for the LLM to understand) is a different one. It also uses 1.3x more tokens for the same tasks, which means, that the new and default model from Anthropic became effectively more expensive (A note they acknowledged by raising the usage limits, to an unknown amount in Anthropic subscription plans, but it’ll still be a token tax on the API use)

How about performance? Well, hard to judge on Evals alone, but they are great. A huge jump in Swe-bench Pro, over 10% improvement, puts this model as the best out there, except Mythos. It’s also the best at real world knowledge via GPQA Diamond (except Mythos). Are you seeing a trend here? Anthropic released a preview of a model, but for the first time, it’s not their “absolute best” model, and in a weird move, they have compared it on Evals to an unreleased model (presumably 10x the size?)

As far as we’ve tested this, it gave an incredibly detailed response on the Mars question we constantly test on, both for me and Nisten, Opus 4.7 produced an incredibly detailed 3D rendered result, much better than out previous tries. I’ll be keeping an eye on this model and keep you guys up to date on what else we find. Vibe checks are .. it’s more expensive, long context is unclear but it’s a great vibe model.

Alibaba is back - Qwen 3.6 is Apache 2.0 35B with 3B active parameters (X, HF, Blog)

The coolest thing about this release is not the evals (though they claim to outperform the much denser Qwen 3.5-27B on multple benchmarks) is that Alibabab is putting models with open weights and an Apache 2.0 license!

We previouly reported on rumors from inside Alibaba, that a few internal restructuring caused many of us to doubt if they would commit to OSS, and they answered!

Another highlight for me in this model, is that Alibaba has an OpenClaw bench (that they are promising to release soon) and that this model does as well as the dense model and beating Gemma 4 by a wide margin on that task.

This model is also natively multimodal, with 262K context extensible to 1M via YaRN.

MiniMax M2.7 Open Weights - 230B MoE with only 10B active (X, HF)

Our friends at MiniMax finally dropped M2.7 in open weights (technically not fully Apache, commercial use requires their authorization, but free for research, personal, and coding agents). It’s a 230B parameter MoE with only 10B active parameters, and it’s matching GPT-5.3-Codex on SWE-Pro at 56.22%. On Terminal-Bench 2 it hits 57%. But the real story here, the part that made me stop scrolling, is the self-evolution piece.

They let an internal version of M2.7 run its own RL optimization loop for 100+ rounds with zero human intervention. The model analyzed its own failure trajectories, modified its own scaffold code, ran evals, and decided whether to keep or revert changes. It got a 30% performance improvement on internal metrics. The model improved itself.

Shoutout to the MiniMax team — longtime friends of the pod and they keep delivering (as they promised to release the weights for this one and they did)

This weeks buzz - news from Weights & Biases from CoreWeave

This week was a very big one in our corner of the AI world. Our parent company CoreWeave announced not one, not two but 3 major deals, including one with Anthropic, a renewed commitment from Meta and a renewal from Jane Street.

CoreWeave now serves 9 out of the top 10 AI model providers in the world. 🎉

Oh and a small plug, if you want to get tokens powered by the same infrastructure, our Coreweve Inference service is open and very cheap, and we’ve recently added Gemma 4 and GLM 5.1 both to our inference service.

This week on the pod, I’ve chatted with Trevor, founding engineer at Marimo Notebooks (also part of CW) about their recent highlight of pairing an AI agent with Marimo notebooks, they went quite viral on hacker news and I wanted to understand why. I understood why, it’s really cool. Check Trevor out on the pod starting around 01:05:00 timestamp.

Tools & Agentic Engineering

Windsurf 2.0 - Agent Command Center + Devin in the IDE - interview with Theodor Marcu (X, Blog)

The first big post-Cognition-acquisition move for Windsurf dropped this week, and I got to chat with Theodor Marcu from Cognition about it on the show. The headline: Windsurf 2.0 brings an Agent Command Center; think Kanban-style mission control for all your agents, plus native Devin integration baked right into the IDE, and Spaces (persistent project containers that group your agent sessions, PRs, files, and context).

The framing Theodor gave me: local agents are pair programmers bounded by your attention (they stop when you close the laptop), while cloud agents are independent hires. Windsurf 2.0 tries to unify both paradigms in one interface. You can plan locally with Cascade using the Socratic method — going back and forth, challenging assumptions, building up context — and then with one click, hand off execution to Devin which runs in its own cloud VM, opens PRs, runs tests, and even tests its own work using computer use on its own Linux desktop. You can close your laptop and it keeps shipping.

One reality check from the community: Devin is great but not cheap. One early tester burned $25 in credits for a 15-20 minute bug fix that produced “okay” results. Something to watch on the Max plan economics. Devin access is rolling out gradually to Windsurf users over 48 hours from launch.

Shoutout to Swyx that helped design the Spaces three months ago whilst at Cognition!

Warp terminal now supports any CLI agent with vertical tabs and mobile control (X, Blog)

This one is for the terminal enjoyers. Warp, which in my opinion is the best terminal experience out there, just shipped first-class support for any CLI agent — Claude Code, Codex, OpenCode, Gemini CLI, all running side by side in vertical tabs with live status indicators.

The killer feature here, and this solves what I think is the single worst part about using Claude Code, is notifications when agents need you. If you’ve used Claude Code you know the pain of constantly checking if it’s waiting for a permission or input. Warp notifies you. You step in, approve, go back to what you were doing. They also added integrated code review inside the terminal, a rich multimodal input editor, and — this is wild — remote control from mobile. Monitor and interact with your running CLI agents from your phone.

Voice & Audio

Gradient Bang - the first massively multiplayer LLM-driven game, interview with Kwindla (X, Play it)

Kwindla, co-CEO of Daily and maintainer of Pipecat, came on the show to talk about Gradient Bang, a game he described as “a side project that escaped containment.” He told me about this back in December, and folks, it’s finally live and it’s genuinely the first fully LLM-driven multiplayer game I’ve seen. It’s inspired by an old BBS door game called Trade Wars that Kwindla used to play as a baby programmer on a 386 DX, but reimagined so your ship’s computer is an LLM you can just… talk to.

You pilot a spaceship through a procedurally generated universe, but instead of clicking buttons, you talk to the thing, and say things like “take me to the nearest mega port and trade along the way” — and your ship AI delegates to sub-agents to actually do the work. You can run corporations, buy more ships, task them to do 5 exploration loops while you do trade runs. It’s Factorio-meets-Ender’s-Game-meets-voice-AI. I’ve been playing it, my ship is currently roaming the universe as we speak (with 0 credits as someone robbed me!)

What makes this technically fascinating is that it’s basically a production-grade stress test for multi-agent orchestration. Sub-agents with shared context, episodic memory across sessions, dynamic LLM-generated UIs (the React front-end is literally rendered from JSON thrown over by a UI agent LLM), and long-running contexts that go for weeks. The architecture is now shipping as a Pipecat library called Pipecat Sub-Agents. Tech stack: Deepgram for STT, GPT-4.1 for the voice agent, GPT-5.2 medium-thinking for task agents, and a dedicated benchmark called GB Benchmarks because tasking these agents is genuinely hard.

Fun detail: Kwindla’s rule for this project was to not write or read any code since November. His colleague John lasted about one day before he broke and started reading React. The Z/L Continuum claims another victim. Go play it, it’s free and fun: gradientbang.com.

Google launches Gemini 3.1 Flash TTS (X, Blog, Try it)

Google dropped a new TTS model this week and folks, it’s not quite the speed-of-light real-time conversational TTS we’re all dreaming of (it’s about 3 seconds time-to-first-token, so batch-mode only), but the controllability is wild. We’re talking inline audio tags — [laughs], [sighs], [gasp] — natural language scene direction, two distinct speakers per generation, 70+ languages with auto-detection, and you can switch emotion and pacing mid-sentence with natural language.

I tested it live on the show with a “shocked/whispering” tag combo asking “Who came to ThursdAI?” and it absolutely nailed it.

It hit 1,211 Elo on the Artificial Analysis TTS Arena, 4 points behind Inworld TTS 1.5 Max and ahead of ElevenLabs v3. Pricing is about $0.03 per 60 seconds of audio, roughly 4.7x cheaper than ElevenLabs v3.Kwindla’s take: this is part of the broader shift from traditional TTS architectures toward fully steerable, prompt-able speech models — which is great for expressive use cases but means you need to test heavily for hallucinations and word skipping.

AI Art, Video & 3D

Tencent HYWorld 2.0 and NVIDIA Lyra 2.0 - actual 3D worlds from one image

This week we got not one but two major single-image-to-3D-world open releases, and they’re genuinely different from the video world models (Genie 3, Cosmos) we’ve been covering.

Tencent HYWorld 2.0 takes a single image (or text, or video) and produces actual 3D Gaussian Splats, meshes, and point clouds that you can import directly into Unity, Unreal, Blender, or NVIDIA Isaac Sim. Not video. Real editable 3D assets. Their framing: “watch a video, then it’s gone” vs “build a world, keep it forever.” The WorldMirror 2.0 reconstruction model is a 1.2B parameter feed-forward model that predicts dense point clouds, depth, normals, camera params, and 3DGS in a single pass. All open source.

NVIDIA Lyra 2.0 (Apache 2.0) takes a single image and progressively generates an explorable 3D world as you navigate through it. The breakthrough here is solving two classic failure modes of generative world models: spatial forgetting (hallucinating new structures when you revisit an area) and temporal drifting (errors accumulating until the scene turns to mush). They solve both with per-frame 3D geometry retrieval and this elegant self-augmented training trick where they train the model on its own degraded outputs so it learns to correct drift. DMD distillation gets you 4-step inference. Apache 2.0, Hugging Face, code and weights.

Both of these together feel like the end of video-only world models as the state of the art. We’re going straight to editable, persistent, importable 3D worlds.

Baidu open-sources ERNIE-Image - 8B parameter text-to-image (HF)

Not to be outdone, Baidu dropped ERNIE-Image, an 8B parameter DiT that’s now #1 on GenEval among open-weight models (0.8856), beating Qwen-Image, FLUX.2-klein, and Z-Image. Built from scratch in 3 months. Runs on a 24GB consumer GPU, and someone already quantized it to NF4 so it runs under 10GB VRAM on an RTX 3060. The text rendering story is the headline — clean multilingual text rendering for posters, infographics, comics, the stuff every other model has been historically terrible at. There’s also a Turbo variant that does it in 8 inference steps.

The craziest AI video I’ve ever seen - “Pi Hard” (X)

You have to watch this AI video. It’s one of the crazier ones I ever saw, and I do reporting on AI for a living. I showed this to my Fiancee Darya, and she only asked me “is this AI” in the middle of it, after saying “yeah, let’s watch this 😂)

Closing thoughts

What a week. Opus 4.7 dropped live on the show, Codex is now controlling your mac in the background like black magic, Qwen gave us another Apache 2.0 banger, MiniMax shipped a self-evolving model, and we got two “image-to-actual-3D-world” open source releases on the same week. Oh and a shoe company is now an AI compute company.

The Z/L Continuum keeps shifting — I feel like every week I drift a little more toward L, especially after seeing Kwindla ship Gradient Bang without reading code since November. And every week the agents get better at babysitting themselves (Claude Code Routines, Windsurf’s Agent Command Center, Warp’s unified CLI agent UX, Codex’s computer use in the background), which means more FOMAT for all of us.

Thanks for reading, share this with a friend, and if you enjoyed this, drop a comment with what you want more or less of. Feedback keeps me going.

— Alex

TL;DR - ThursdAI, April 16, 2026

• Hosts and Guests

  • Alex Volkov - AI Evangelist & Community with Weights & Biases / CoreWeave (@altryne)

  • Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed

  • Guests:

    • Kwindla Kramer (@kwindla) - Co-CEO of Daily, Pipecat maintainer

    • Theodor Marcu (@theodormarcu) - Product at Cognition

    • Trevor Manz (@trevmanz) - Founding engineer at Marimo

• Show Notes

  • Recap essay on the Z/L Continuum from AI Engineer Europe (Blog): should AI engineers still read code? Ryan Lopopolo says no, Mario Zechner says yes for critical paths, everyone in between has FOMAT.

  • Mario Zechner talk is finally live on AI Engineer youtube (Watch)

  • Super Gemma 4 26B Uncensored v2 by @songjunkr — trending on HF, 0/100 refusals, fixed tool calls (HF GGUF, HF MLX 4bit)

  • Gemma 4 21B REAP — 20% expert-pruned Gemma 4 26B MoE by 0xSero using Cerebras REAP (HF)

  • Parcae (Together AI + UCSD) — stable looped transformer architecture with scaling laws, matches 2x-sized transformer quality (Paper/blog)

  • Claude Desktop app — rewritten from scratch, completely new app

  • Gemma 4 on W&B Inference — reply on the announcement post with code Gem Drop for $20 in inference credits, also supports LoRA inference via link

• Big CO LLMs + APIs

  • Anthropic launches Claude Opus 4.7 - 87.6% SWE-bench Verified, 64.3% SWE-bench Pro, 3x vision resolution, new xhigh effort level, /ultrareview in Claude Code, same pricing as 4.6 but new tokenizer uses ~1.0-1.35x more tokens (X, Blog)

  • OpenAI Codex major update: macOS background computer use, 90+ plugins, gpt-image-1.5 image generation, in-app browser, memory, self-scheduling automations, multi-terminal SSH (X, Blog)

  • CoreWeave signs deals with Anthropic (multibillion), Meta ($21B expansion, $35B+ total), and Jane Street ($6B cloud + $1B equity), now serves 9 of the top 10 AI providers

• Open Source LLMs

  • Qwen 3.6-35B-A3B - Apache 2.0, 35B MoE with 3B active, 73.4% SWE-bench Verified, natively multimodal, 262K context extensible to 1M (X, HF, Blog)

  • MiniMax M2.7 open weights - 230B MoE with 10B active, 56.22% SWE-Pro matching GPT-5.3-Codex, self-evolved via 100+ rounds of autonomous RL (X, HF)

• Tools & Agentic Engineering

  • Windsurf 2.0 with Agent Command Center and Devin integration - interview with Theodor Marcu (X, Blog)

  • Warp now supports any CLI agent with vertical tabs, notifications, code review, mobile remote control (X, Blog)

  • Claude Code Routines - cron, GitHub event, and API-triggered autonomous agents running on Anthropic’s cloud (Docs)

• This Week’s Buzz - Weights & Biases / CoreWeave

  • Marimo Pair - drop Claude Code / Codex / OpenCode agents directly inside reactive Python notebooks - interview with Trevor Manz (Blog, GitHub)

  • Gemma 4 now live on W&B Inference on CoreWeave infrastructure, with LoRA inference support

• Vision & Video

  • Craziest AI video of the year: Pi Hard / Neil deGrasse Tyson (X)

• Voice & Audio

  • Gradient Bang - first massively multiplayer fully LLM-driven game, Pipecat sub-agents - interview with Kwindla (Play, GitHub)

  • Google Gemini 3.1 Flash TTS - 1,211 Elo on TTS Arena, inline audio tags, 70+ languages, ~$0.03/60s (Blog)

• AI Art, Diffusion & 3D

  • Baidu ERNIE-Image - 8B DiT, #1 GenEval among open models, precise multilingual text rendering (HF)

  • Tencent HYWorld 2.0 - single image to editable 3D Gaussian Splats/meshes, Unity/Unreal/Isaac Sim ready (GitHub)

  • NVIDIA Lyra 2.0 - single image to explorable persistent 3D worlds, Apache 2.0 (Project, HF)

• Other news

  • Unitree humanoid breaks 100m dash world record at ~10m/s (X)

  • Allbirds shoe company loses 99.5%, rebrands as “NewBird AI”, raises $50M to buy GPUs, stock up 600-800% (X)

Do AI Engineers still need to read code in 2026? how about 2027? This is the most discussed question at the first AI Engineer Europe conference.

On day 1 opening keynote, Ryan Lopopolo from OpenAI took the stage and said “code is a liability” and that we should all strive to be “token billionaires” - and on the last day, Mario Zechner of Pi got standing ovations after he told folks to “slow the fuck down” and “read every fucking line of critical code” - this debate happened in every hall conversation, every dinner (speaker or otherwise) and I bet it happens in every company adopting these tools. So ask yourself, where are you on the Z/L Continuum today? Where will you be in 6 months? in 12?

I’m on the plane back home from London where I spent the last week watching, eating with, listening to hundreds of AI engineers, with backstage access to the top AI engineers in the world, and a few themes/takeaways arose.

I know that many of you expect the quick recap, what were all the things the AI Engineers were talking about, so lets start there:

AI Engineer Europe main themes TL;DR

by Alex Volkov // ThursdAI.news // April 2026

• Nobody is sleeping well - we all seem to have AI Psychosis and FOMAT
  Everyone at this conf. is coding with agents, nobody has figured out how to orchestrate them efficiently, all are suffering from FOMAT, that feeling that “just one more prompt” is what’s going to make your Agent code for hours while you’re sleeping/AFK.

• The Zechner - Lopopolo continuum

  First day keynote speaker from OpenAI Ryan Lopopolo wants you to completely stop looking at code and become “token billionaires” and second day keynote speaker Mario Zechner is pleading to “slow the fuck down” and read the code. Everyone is debating where they are on the Z/L continuum and where they would be in a years time. But it’s not a binary, is a spectrum.

• Everything is changing really fast, we need new tools
  IDEs are dying, Github is seeing unprecedented traffic levels (15x their biggest year, which was last year), so is Cloudflare, Software can think now, L33T C0d3 is dead, interviewing is impossible, an AIE is now a PM, Agent babysitter, Taste setter (choose one that applies), and it’s going to accelerate.

• But we’re still early (at least in EU)
  Many engineers and enterprises are still hesitant to use AI, in EU specifically there’s strong regulation, privacy concerns and general un-ease about AI. There is a LOT of software engineers and not all are AI Engineers yet. Nobody knows what is coming for SWEs, and how our role changes.

• AGI is nearly here - just not evenly distributed
  Everyone was talking about Claude Mythos and whether “too dangerous” to release and will be a Y2K level cyber-secutity event, or Anthropic is just doing great pre-IPO scare tactics marketing and don’t have enough GPUs to release it for everyone

• “Just talk to your Clawnker”
  Something changed in Dec25-Feb26, we all feel it, and the meteoric rise of the OpenClaw (which had a whole track on day 1) is a testament. Clawfather Peter Steinberger, maintainers Vincent Koch, Sally-Ann Omalley all told me this is the biggest thing folks don’t yet “get”. Self improving intelligent software is here, talk to it! Stop asking them for trivial advice.

• MCP is dead, long live MCP
  Many AIEs are quick to host MCP funerals, while enterprises adopt MCP and MCP Apps faster than ever, due the inherit security risks of agent skills.

• AI was supposed to make us more productive, but we all just work more
  Jevons Paradox on people’s time is a surprising one to me, we were all supposed to offload the boring, meaningless stuff like writing syntax to clankers, but everyone is just working way way more now. You’re expected to give 10x output + docs + tests + i18n +++ all on your own.

• The MHC is here to stay (Model, Harness, Context)
  This is my concept that I ran by many folks, instead of predicting what WILL change which is hard, let’s try to think what wont. Models will continue to get better, they will always need harness/tools and your personal context / memory will matter more than ever.

Day 0 - Workshop day, Great weather day, Speakers dinner

When folks pulled up to the event, there were some venue related issues with the workshops, space was limited and the queues were a bit long. The most important things about a conference were top notch: Food was great, Wifi was Great, Speakers were exceptional.

Also, there was a red fucking GIANT INFLATABLE LOBSTER right in front of the venue.

Oh and the the weather! The weather in London, OMG, it really felt like the AI Engineer conference not only have brought the brightest SF AI minds to London, they have brought the weather with them. So I think folks who didn’t get into a workshop actually won a half day in London, in great weather. So win win?

And the location! Most who stayed at the event sanctioned Park Plaza hotel, were just 7 minutes walk from the venue, crossing the River Thames over the Westminster bridge, with an incredible view of Elizabeth Tower (Big Ben) and the Houses of Parliament, Westminster Abbey, the London Eye, South Bank. It was just delightful to walk to the venue and back. 100% the location is by far the best one out of all AI Engineer locations so far. 10/10 would do this again.

The contrast was stark, between the Royal “office” of the king and royal family, parts of which were built over 900 years ago, all majestic, gothic, and regal with the conference in which the future of humanity and artificial intelligence is being discussed.

Though I will say, I was applauding the brits, as many of the speakers of AIE Europe were invited for “tea” at 10 Downing St (the “white house” of Britain and the seat of current PM), to discuss innovation, AI and the future. (Yours truly invitation must have gotten lost in the mail, but I’ll try to sneak in there next year to cover).

Speakers Dinner - Single highest density networking in AI, in all of EU, maybe the world?

Look, I’m not going to lie, when I say, if it were possible to only choose 1 part of AIE to attend; Between the workshops, the speakers green room, the hallway track, the keynotes, the presentations, and the speakers dinner, I will 100% of the time, choose the speakers dinner.

(This is also, why you should absolutely don’t hesitate and submit a CFP to the upcoming event).

The Caliber of the folks attending is sky-high, as Swyx as the curator of the talks reviews them and their talks, and only the best ones get to come on stage (and dinner) so the social ROI is off the charts. As a podcaster, there’s no one single room that’s better for me to be in.

The table I set was almost literally the two sides of the Z/L Continuum. On the one end, Mario Zechner (creator of Pi), Armin Ronacher @mitsuhiko (creator of Flask, EarendiL) and their co-worker Cristina Poncela. On the other side of the table, Ryan Lopopolo (@_lopopolo), Vaibhav and Katya from all from OpenAI.

Also the same table had keynote speaker and friend Peter Gostev (@petergostev) from Arena and my new bestie @marlene_zw who’s a Github/MSFT DevRel and a Python Software Foundation fellow and Matt Pocock @mattpocockuk who is an incredible teacher (and the other potential Conference MVP, people LOVED his /grill-me skill)

And this was just our table, other tables were as stacked as this one, and all were discussing their job, their Claws, their troubles sleeping, the dopamine rush from getting agents to do the task “just right”, the ways to handle context drift, Claude Mythos, MCPs, whether we even need to look at code anymore... while enjoying a very tasty Indian dinner.

A few anecdotes from this dinner include an introductory speech from Lia, Ben and Swyx, which was followed by a non traditional speech from the Head Chef, which was fine, but then, he called the wine guy to give a speech, to the complete surprise of the organizers and ... Not one person could understand what the hell this guy was saying, as he proceeded for 5 minutes to yap about what wine selections they have for that evening in a thick french accent. At some point this became nearly comical 😂 And while the food was great, the conversations were the most important part. Also the speaker gifts were awesome, especially the female speakers who all scored a Liavella bag (because Lia the GM of AI Engineer also has a luxury bag company)

Day 1 - Keynotes and a ThursdAI Live with 5 guests.

Beautiful opening keynotes day, and the kickoff was electric. First Keynote from Malte Ubl (@cramforce), CTO at Vercel and a great conference guy himself with JS Conf, was a great start!

Malte talked about a bunch of things, but mostly, what does an internet for agents look like. Specifically mentioning that over 60% of the traffic to Vercel comes from bots/AI Agents. Which is kind of a big deal, and I expect that to rise.

One thing I haven’t considered, until I chatted with Sunil Pai (@threepointone) from Cloudflare (on the pod, offline, coming soon!) is that when you ask your agent 1 answer, it can very VERY quickly go to over 30 web pages to bring you an answer in a fanout-multi subagent search. This puts strain not only on Vercel, but on Cloudflare and every other network provider.

And then we got the author of the L of the Z/L Continuum - Ryan Lopopolo - who opened with the brilliant line “hey, I’m Ryan, I work at OpenAI and I’m a Token Billionaire, and I want you all to be as well”. Ryan was likely the most AGI pilled speaker of the bunch, and having spoken to him the night before, I understand why. You should definitely watch that talk, where he says we need to think bigger, coding is either solved or will be solved very soon. He also says that “code is a liability” and that we should focus on tests, evals, fixing our harnesses and making sure our every time an agent does an “oopsie”, it’s the prompts fault and we need to fix the prompt.

Speakers lounge hang

I then left the audience and went to hang out in the speakers lounge, which is the second most high density room at the whole conference, not before the speakers go on stage, but definitely after.

Where I finally met a bunch of the OpenClaw crew, including the Clawfather Peter Steinberger (@steipete) who was the keynote speaker of the (first ever) OpenClaw track; Which happened on the main stage after the morning keynotes. I also met Vincent Koc (@vincent_koc), the #2 maintainer of OpenClaw after Peter, and convinced him to come on the pod, and we had a great chat!

Peter was surprisingly chill, considering the guy recently had a fiery increase in personal fame (and I’m assuming fortune), having been on every top podcast, met with everyone possible in Silicon Valley from Sam to Elon to Zuck to Jensen, has half a million followers on X, and changed the world with OpenClaw to the point that even grandma’s in China are installing it (not to mention literally every AI lab in China has an OpenClaw service)

I then went into prep and writing mode, as I had a livestream coming up, which, first time in EU timezone threw me a bit off. You can read and watch that episode here, I will say, this was by far the densest ThursdAI show I’ve ever done, we were strictly limited by the crew to 2 hours (we usually go over), and with 5 guests, me and Wolfram on the scene, Yam, Nisten, LDJ joining remote, it was quite a lot of balls in the air to juggle. I’m very proud of myself for a nearly flawless execution of this. I hope you enjoyed that ep as much as I did making it.

Anyhow, quick editing + newsletter writing, and I rushed to one of the many side-events that always happen around AI Engineer. My advice: go to the Conference website early, and apply to some of these side events ahead of time, they often undershoot the amount of people who want to attend them, and are overfilled with last second folks. Case in point, after a dinner I was able to get into (Thanks to Marlene!) we tried to get into the event at the London Aquarium, walked there, only to be told “sorry, you can’t get in” even as other people were leaving, the brits.. they do love their rules and regulations.

Day 2 - Z teaches slowing the fuck down, and speeding back up again.

The last day of the conference, and for me, Fridays are blessed — it’s after Thursday, my big thing for the week is done, and I can “relax” until next Wednesday. Except there’s a near-empty podcast booth right there, and I have access to all these amazing people I just befriended for 2 days. It would be a miss of a lifetime to not sit them down on a microphone. I thought I would get Mario on, but alas, he got too busy (I don’t blame a person who gets to chat with Gergely Orosz).

Speaking of Mario Zechner, when he went to give his (I predict, MVP talk of the conference) talk, in a billboard charity t-shirt, something weird happened. I’ve never seen a room of speaker all preparing for their own talks, and are excited for the opportunity to chat with each other, all quiet down and listen as intently. There’s a live screen in the room of the main stage, but usually people don’t pay that much attention to what’s going on on stage. This time, everyone just... quieted down and listened to Mario tell a room full for AI Psychosis having engineers, to SLOW THE FUCK DOWN. It was glorious. He also got a standing ovation when he got off stage which is also, I think a first time I’ve seen this.

Mario’s argument isn’t anti-agent. He’s not a Luddite. He uses the agents all the time. His point, and this is why it landed so strongly, is that the industry-wide quality bar is collapsing, that everyone has stopped caring about craft, that the median engineer is shipping slop they don’t understand and don’t even know they don’t understand, and that this should bother us. Mario literally said “Read every fucking line of critical code”! This is of course the Z of our continuum.

I think it’s part Mario’s Austian/Old gramps charm, and part... everyone kind of needs to hear this? AI coding is incredible, but there’s no doubt that there’s a tension where on one side, we all need to accelerate to really use these tools correctly, we feel that if we don’t rush after the next great thing, we will stay behind and NGMI, and folks from big labs are telling us to not look at code, and from the other, this introduces a lot of “small” things, and sometimes medium to big things into the codebase, not only that, it removes the engineer’s ability to even understand the codebase, which, can turn into subtle and then oh-not-so subtle bugs.

I’ve chatted with a lot of folks, and asked them, where on this continuum of “I don’t even look at code, I YOLO it to my Clankers” vs “I don’t write code but I read and code review myself and code is important” and there doesn’t seem to be a great answer. I’ve also asked people if they expect to be on the same place on Z/L Continuum in 3,6,12 months, and nobody could give a confident answer that they will still be looking at code in 12 months. Especially not with Mythos from Anthropic being announced just a few days before the conference and touted as the most incredible innovation that is so good at code it can find bugs in a 27yo codebase that were never found before.

Oh, and we were able to sneak a Weights & Biases talk in there, as Wolfram (@WolframRvnwlf) applied for a hallway track talk about Wolfbench.ai and got accepted. 👏 I wasn’t able to watch it unfortunately, but I was told it’s recorded and I will send a link in one of the newsletters once it comes out! Wolfbench is actually a very poignant new benchmark from us at W&B, created by Wolfram, where we test not only model capabilities on TerminalBench, but also Harness capabilities with each model, and whether the harnesses improve independent of the models improving. Do check it out if harness engineering interests you.

4 more pods and a surprise MC opportunity

As I was booking podcast chat with folks, I got a text from Ben the organizer, that the mains stage MC, Tejas Kumar (who is a great energy dude btw!), needed to step away, and if I can jump in as an MC?

I already had a pod slot booked and quite a few guests, but hey, a main stage is the main stage right? Also, this was the Coding Agents track, arguably the most important! So I moved the pods to before and after the MC block and jumped on. And I’m so glad that I did.

The job of the MC is to smooth the transition time between speakers, hype up, get the crowd clapping etc. But also to make speakers feel comfortable (many speakers, even the most experience ones, are getting the jitters before talks).

As I was chatting backstage with my 2nd speaker, Michael Richman, I learned so much! First, he coined the term FOMAT, which is that feeling you get when you’re about to leave for lunch, or go to sleep, and you’re about to kick off a refactor, or send a big research prompt, but are worried that your your agent / clanker will not be able to do it. That you will come back and instead of a finished feature, you’ll see a blinking question that the agent sent 2 minutes after you left and was just sitting there idling. FOMAT: is FOMO, for Agent time!

I thought it was a great talk, and it’s definitely something that everyone in the crowd (including me) have felt. Michael is also from Denver, my home town! At some point after we chatted, Michael asked me what I do, I told him I have a podcast, he asked which one? And when I said ThursdAI, his eyes lit up! He was so happy, started clapping me on the shoulder, saying “You’re ThursdAI!” haha. (That’s how I found out he’s a listener, not a watcher lol)

It’s a moment I’ll never forget. I always love meeting listeners of the show, and AI Engineer is the best place to meet them.

I jumped off the stage and rushed to back to back recordings to chat with Sally-Ann Omalley from RedHat, a wonderful conversation with a wonderful human being, I can’t wait to share with you. We chatted about OpenClaw (she’s a maintainer), safety, and all kinds of things. It was so great.

Then with Sunil Pai (@threepointone) from Cloudflare, another wonderful human, who actually reviewed my notes about him and made edits, we had an incredible flow! Sunil is just a fountain of energy, and we chatted about all kinds of things Cloudflare and AI , Agentic future, SWE future.

I can’t wait to edit and send you these chats, because you know what I noticed? When it’s not live, I actually have time to enjoy the conversation, not stress about things going wrong/long/boring/breaking, and I think it shows! I hope that I find a way to bring that fun back to the live shows as well 🙂

Ending the conference on meta and bullshit (bench)

After I was done, I was finally able to truly relax and get back to speaker lounge, to listen to Peter Gostev from Arena.ai talk about his experience with what LLM models are still failing at and his Bullshit Benchmark. I strongly recommend rewatching that, I’ve known peter for a while online, and became friends at the last AIE, and he’s joined the LMArena crew since then, and is extremely insightful about different models and their differences.

And then, Swyx took the stage and closed down the show.

Swyx, as always, is a dope speaker. He doesn’t want to take stage time away from his other folks, but I always implore him, take the stage, give a recap of where you are, as the curator, folks want to hear from you (so Swyx, if you’re reading this, Kudos, but also, please be a more active voice in your own conference, you are CEO after all! People from all over the world want you thoughts)

And oh boy did he deliver. It was a fantastic closing keynote, very meta about the conference itself, and how they have automated tons of aspects of it as a business, which agents did what. IDK if you noticed, but the AI Eng website had all the agentic AI stuff imaginable, for you and your Clanker. They have speakers.json, schedule.json, MCP, skills, there’s just everything on there.

The highlight for me was the (now infamous) red inflatable lobster at the entrance, in which Swyx saw something online, got inspired and asked Devin to go research suppliers, contacts, sites) and showed that a coding agent can do actual work, and voila, the lobster was a HUGE success.

I think I know 100% where Swyx sits on the L/Z Continuum, can you take a guess? I’m pretty sure Swyx cared to see 0 of that code himself!

AI Engineer Conf - a brief history

AI Engineer, which is a term Swyx coined nearly 3 years ago on Latent Space, has led to renaissance of glorious tech conferences that kind of died during Covid.

If you’ve been reading/listening to ThursdAI all this time, you know that I’ve attended and covered all 6, spoke at 3 of them (though everyone remembers the one time I dressed up as a judge).
The first one (AIE Summit 2023) was a monumental step up in my -back then, still brand new- career. I’ve got to network with the best people at the speaker dinner, I’ve got to interview around 100 attendees with a run-and-gun microphone and a camera crew following me around about what they think an AI Engineer even is.

Mind you, this was before (way waaay before) Claude Code, before Vibe Coding was a thing, tool calls were literally introduced that month at OpenAI, context windows were maximum 32K, and LLMs were chatbots and APIs, Vector DBs mattered a lot and Google was nowhere to be found in the AI race, and the even the most AGI pilled didn’t believe we’ll have the Z/L Continuum.

And still, the hotel Nico was buzzing with people who saw the opportunity, who could imagine where this was all going, who were already getting “AGI Pilled“, building agents (anyone remember AutoGPT? big sponsor at that first conference, talk about being too early!). “We are just in time!” Swyx told the 600 or so hand selected folks at the AIE Summit, not too early, not too late, just right. Well I have something to say to 2023 Swyx, we were ALL so early!

The tools became incredibly better, in a way that I think even the most AGI pilled of us back then could not have predicted, and so has the conference itself. AI Engineer has grown nearly 1000% since then (per Lia McBride, now GM of AI Engineer and a huge part of why this conf runs so smoothly), in both attendees and reach (Check out the YT channel, it’s nearing 350K subs and has some of the highest quality content available, but is also a sort of time machine to how we were all collectively thinking about AI Engineering just a 2.5 years ago)

As the AI Engineer conference grew, so has ThursdAI. We’ve moved from only audio to live video and boosted the production quality and guests caliber, leading to moments like a surprise live interview with Logan Kilpatrick & Jack Rae, first “on the floor” podcast studio in AIE:Code NY, back to back interviews with folks from DeepMind, OpenAI, Github and OpenClaw folks.

So when Swyx reached out for me to come cover this one, it was an immediate “Yes, can I bring Wolfram with me”?

Closing words

After the closing keynote, everyone went to Fabric, one of London’s main clubs, to celebrate. I couldn’t make it — I was fried from socializing and went to a smaller dinner instead — but I heard it was great.

If you ever had any doubts of whether an AI Engineer ticker was “worth it”, it is. If you had doubts of whether you should apply to talk at one of the upcoming events, you should. And if you ever doubted that an AI Agent can find and order a British yeoman-clad gentlemen to stand outside of a venue for two days propping a cart with a huge giant fucking inflatable red lobster ... well, I have some pictures for you to convince you otherwise.

So my last question, you guessed it, where do you sit on the Z/L continuum dear reader? Do you expect to answer with the same answer in 3-6 months? How about a year? Comment below or come tell me in person at AIE Worlds Fair in San Francisco in June. My honest truth? I personally sit on the absolute AGI pilled L place on the Z/L, but also, I also do not maintain any critical code that folks use.

P.S - if you made it all the way through, thank you so much for reading, I’ve asked a few friends to review this, huge thanks to Wolfram, Kwindla, Claude, Wolfred and Amy for proofreading, special thanks to Dex for the Z/L shorthand that made me rewrite this whole thing from scratch and an incredible debt of gratitude to everyone who made AI Engineer possible, Swyx, Ben, Lia, VB, Raouf, Phil and Tejas, Kyle and the podcast folks who came saved my bacon, and to every speaker and attendee who kept the vibes going!

Alex Volkov
AI Evangelist at Weights & Biases, Podcaster, covering AIE for the 6th time. Completely AGI Pilled.

Pictures provided by AIE and Toby Merrit Photography (website)

What a show we have for you today! First, let me catch you up on what’s important: Anthropic, this week announced a whopping $30B ARR up from 19B in Feb, while also telling us about Claude Mythos Preview their next gen HUGE model that they won’t release to the public (yet?) that finds crazy vulnerabilities in existing code bases. Apparently OpenAI will follow up with a similar non-public model soon.

The Meta Superintelligence Lab led by Alex Wang finally showed what they were working on, Muse Spark, the smaller of their upcoming models on a complete new infrastructure (MSL announcement, Simon Willison’s deep dive on the 16 hidden tools).

In other news:

Z.AI released GLM 5.1 in OSS finally (HF weights), Seedance 2.0 finally available in US on Replicate, OpenAI testing out GPT-image-2 on LM Arena under codenames, HappyHorse from Alibaba takes the video crown, and Mila Jovovich (5th Element, Resident Evil) releases agentic memory plugin called MemPalace (Ben Sigman’s transparent correction thread is worth reading).

We had 5 guests today on the show, we kick off with @swyx the founder of AI Engineer and host of Latent Space. We then chatted with @petergostev from Arena (formerly LMArena) about Mythos and the compute wars, then Vincent Koc, the second most prolific contributor to OpenClaw, then our friends VB from OpenAI and Omar from DeepMind, both previously at HuggingFace. This is a busy busy show, and given the time-zones, I unfortunately don’t have time for a full weekly writeup, but as always, I will share the raw notes and post the video (lightly edited).

AI Engineer - London

ThursdAI came a long way since the first AI Engineer conference, but many who read this don’t know, that was my big break. Swyx invited me to cover the first AIE in San Francisco in 2023, and I remember, I was in an Uber to the airport, the driver asked me what I do, and I, for the first time said “I host a podcast”. I (and ThursdAI) owe a lot to Swyx, and AIE team, and it’s been incredible to see how big they’ve grown and how many great speakers this event hosts!

The term AI Engineer has drifted in those 3 years, but also has the term Software Engineer. Swyx predicted this nearly 3 years ago, what I don’t think he predicted, is that all engineers are now AI Engineers, and this includes domains like Agens (OpenClaw), Context and Harness Engineering, Evals and Observability, Voice & Vision all of which are tracks in this conference.

I was really surprised to see how many of the talks/speakers here are native to London (after all, Deepmind is from here, OAI, Anthropic, Meta have offices here) and the latest boom in agents, OpenClaw, Pi were all Europe based as well, and they are joined the AI Engineer stage.

Oh, and there’s also a Giant Inflatable Claw at the entrance, yup, for pictures and vibes, and to show off how quickly the OpenClaw took over the mind-share.

Anthropic announces $30B ARR and Mythos, their next model, will not be released to the public.

The thing that everyone will tell you, is that Anthropic is on a roll, this is obviously connected to their upcoming IPO this year. We’ve been covering many issues on their part, but this week we saw them posting about a HUGE increase in ARR, from 19B in February to 30B in April, passing OpenAI at $25B. That last fact though, is kind of disproven because they report on ARR differently, OpenAI apparently only counts their cloud revenue from Microsoft per the information.

The growth is undeniable though, and so is the most unprecedented release announcement, Claude Mythos Preview, which was rumored for a bit and now was announced proper. With project Project GlassWing, Anthropic has announced that this model is SO good at cyber security and finding bugs in code, that they cannot share it with the public, and through GlassWing they will share it with companies like Microsoft, Linux, CrowdStrike and a bunch of others, to harden their security.

This is it folks, this is the first time, where a model was “announced” but deemed too risky to release. Now, is it truly “too risky”? Previously, folks thought that DALL-E is too risky, or cloning voice tech is too risky, and now it’s everywhere. The capabilities catch up even in OpenSource.

But the facts are, Anthropic says they’ve found a 27-year old bug in OpenBSD (famously very secure), and that this model is very very good at connecting the dots between several, seemingly inacuous bugs, to string them together into one coheren exploit.

This is, indeed scary. Just last week, one of the top security researchers in the world, Nicolas Carlini, now at Anthropic, gave a talk at Black Hat, showing off these results, and saying that these models since December and definitely recently have passed him as a security engineer. If you haven’t seen this talk, watch it, then try to estimate if Anthropic did the right thing by only releasing this model to enterprises first.

But on the show, Peter Gostev from Arena gave me a take on this that I haven’t been able to shake. Peter pulled up his Compute Wars chart live on the show — and the picture is that OpenAI is way ahead of Anthropic on compute, with Anthropic only recently getting a noticeable bump (which lines up suspiciously well with Mythos being trainable in the first place). His read: “it sounds cooler to say it’s too risky to release than ‘we can’t serve it.’” The official partner pricing is $25 / $125 per million tokens — 5x Opus 4.6 — but if you don’t have the GPUs to serve it broadly, the price doesn’t matter. In the year of the IPO, the company that cannot serve a model says the model is too dangerous to serve. Make of that what you will.

This also reframes the whole rate-limit drama with OpenClaw. Anthropic didn’t ban OpenClaw — I want to be very clear about this because the discourse went sideways. What they did is they made it significantly more expensive for Max-tier subscribers to use Opus through OpenClaw, which pushed a lot of people over to GPT-5.4 via Codex. Same root cause: they’re out of compute. The freshly announced Anthropic + Google TPU deal (Google already owns ~10% of Anthropic) is them trying to fix this — though as Peter noted, it’s pretty wild that Google is propping up a direct competitor to their own DeepMind team. Same pattern as their original $2B Anthropic investment ending up propping AWS Bedrock against Google Cloud. Big Google contains multitudes.

Meta Superintelligence Labs ships Muse Spark — Llama is dead, long live Muse

Llama is dead, long live Muse. This week Meta finally showed what the very expensive Meta Superintelligence Labs under Alexandr Wang has been cooking, and the answer is Muse Spark — the smaller of their new model family, built on a fully rebuilt AI stack from scratch in just 9 months. Nine months is wild for that kind of overhaul, and the headline number people are quoting is that they reach Llama 4 Maverick capability with over 10x less compute.

Spark is intentionally small and latency-optimized — it’s not trying to be the biggest, it’s trying to be the first step on Meta’s new scaling ladder. But the benchmarks in certain areas are nuts: 86.4 on CharXiv Reasoning (beats Opus, Gemini, GPT-5.4), and the one that really got me — 42.8 on HealthBench Hard vs Opus at 14.8 and Gemini at 20.6. They trained it with data curated by over 1,000 physicians and it shows. They also shipped a Contemplating mode which is parallel multi-agent reasoning, hitting 58.4% on Humanity’s Last Exam with tools. Coding is the acknowledged weak point (77.4 on SWE-Bench Verified vs Opus 80.8) but for v1 from a brand new stack, this is extremely respectable.

Meta is Back!

The real story isn’t any single benchmark though, it’s distribution. Spark is rolling out across meta.ai, WhatsApp, Instagram, Threads, Messenger, and Ray-Ban Meta glasses — billions of users. Meta went from open Llama to a closed consumer model and they’re clearly playing a different game now (though Wang says future Muse versions might be open-sourced).

The deep-dive that’s really worth your time is Simon Willison’s post where he poked at the meta.ai chat UI and got the model to spit out descriptions of 16 hidden tools behind the scenes — full Code Interpreter with persistent Python 3.9, a visual grounding tool that does pixel-precise object detection (bounding boxes, point coordinates, counting — it located 8 objects including individual whiskers and claws on a generated raccoon), sub-agent spawning, file editing, and semantic search across Instagram/Threads/Facebook posts. It’s basically an entire agentic harness baked into the chat UI. Jack Wu from MSL confirmed the tools are part of a new harness built specifically for Spark’s launch. Meta stock went up 7% on this. They are very much back in the frontier game.

Guest highlights

We had an unprecedented packed show with 5 guests (also this is the shortest show we’ve ever

Swyx kicked us off with vibes from the AI Engineer floor — harness engineering as the dominant theme (gains are coming from the harness, not the weights), the rise of skills (English-as-programming-language) absorbing more of that harness work, and his thesis that supply-chain attacks like the recent light LLM and Axios incidents mean you should basically vendor everything — pip fork instead of pip install. We also chatted about how MCP has gone from “the most exciting protocol” to “settled and stable, therefore less interesting,” which is a great problem to have.

Peter Gostev from Arena (you saw a lot of him in the Mythos section above) also dropped a bonus on us: Arena just released 3 years of historical leaderboard data and actual prompt datasets on Hugging Face. He used to literally scrape the arena website by hand into Google sheets to make those overtime leaderboards we all loved — now it’s all public. Also: he confirmed that Seedance 2.0 jumped ~80 ELO points above the next video model on Arena, which is unprecedented — video models normally cluster within 10 points of each other.

Vincent Koc — the #2 OpenClaw maintainer after Peter Steinberger — joined us fresh off the OpenClaw track stage. The OpenClaw codebase is now ~1.5 million lines of code including unreleased iOS and Android native apps. GitHub literally caps the issue/PR counter at “5K+” and they hit the ceiling. We talked about OpenClaw 2026.4.5 which ships /dreaming GA (Light/Deep/REM phases that defrag agent memory and write a human-readable Dream Diary to DREAMS.md), built-in video and music generation across 4 backends, GPT-5.4 as the new default, prompt-cache reuse improvements, and Control UI + docs in 12 new languages. Vincent’s framing of dreaming was beautiful — “how do you explain agent memory to a mom? You call it dreaming.” He also gave my favorite line of the show on the GPT-5.4 personality problem: incredible at coding, but soulless. (For what it’s worth, I came home after watching Project Hail Mary, cloned the Rocky voice, dropped it into my OpenClaw, and it was magical. That’s the kind of thing you can only do when the harness and the model are decoupled.)

VB from OpenAI told us Codex just hit 3 million weekly active users — up from 2 million last month. We talked plugins (the Stripe / Supabase / shadcn ones that ship as packages), sub-agents (yes, one is named Jason), and Guardian Approvals — an experimental mode that classifies each tool call by risk and only escalates the dangerous ones to you, so you don’t have to YOLO-mode everything. The story that stuck with me though is his 9 AM Codex automation: every morning it reads his Slack mentions, cross-references Gmail and Calendar, and creates 5-minute pre-brief calendar events for upcoming meetings. None of that is “coding.” That’s the super-app future hiding inside a “developer tool.” I’m stealing this workflow.

Omar Sanseviero from Google DeepMind came on to celebrate Gemma 4 crossing 10M+ downloads with 1,000+ Gemma-4-based fine-tunes already on HF (and Gemma family total is now over 500M downloads). Gemma 4 is also the foundation for the next generation of Gemini Nano on Pixel/Samsung devices. Lama.cpp vision capability fixes are landing. Gemma 4 is also live on W&B Inference if you want to play. Wolfram (whose entire household runs on Pixel + Google AI Studio, including his 70-year-old mother on voice unlock) was in heaven.

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This Week’s Buzz

A short but spicy week from Weights & Biases:

• W&B Automations are LIVE. You can now wire event triggers from your training runs (completion, eval thresholds, drift) into notifications, GitHub Actions, deployments, infra shutdowns — closing the loop from experiment to production. Pairs really well with the iOS app we recently shipped, so you can get a ping on your phone the moment something interesting happens on a run.

• GLM 5.1 is live on W&B Inference (alongside Gemma 4 from last week) — the team is moving fast to host the best open models the moment they drop.

• Wolfram published a deep dive on “more reasoning is not always better” on the W&B blog — the research behind his finding that giving models more thinking tokens can actually make them dumber on certain tasks. It’s the in-depth version of what we discussed on the show last week, with all the data. Go read it on wandb.com.

Also: shout out to everyone who came up to me at AI Engineer and said hi. The Wolf Bench mentions in particular made my day. If you’re listening to this and you’re at AIE — come find us, we’ll be around tomorrow too.

That’s it for this week — newsletter is short because the show was long and London is calling. As always, thanks for reading and listening 🫡

TL;DR April 9 - show notes and links:

• Hosts and Guests

  • Alex Volkov – AI Evangelist & Weights & Biases (@altryne)

  • Co-Hosts – @WolframRvnwlf @yampeleg @nisten @ldjconfirmed

  • Guests: @swyx (AI Engineer / Latent Space), @petergostev (Arena, formerly LMArena), @reach_vb (OpenAI / Codex), @vincent_koc (OpenClaw #2 maintainer), @osanseviero (Google DeepMind / Gemma)

• Big CO LLMs + APIs

  • Anthropic announces Project Glasswing and Claude Mythos Preview, a cyber-defense frontier model too dangerous to release publicly (X, Announcement)

  • Anthropic’s Claude Mythos is so powerful they won’t release it — found zero-days in every major OS and browser, escaped its sandbox, and scored 93.9% on SWE-bench (X, X, X, X)

  • Anthropic ARR jumps from $19B (February) to $30B in April — secondary tender sale completed, employees not selling ahead of IPO

  • Anthropic + Google TPU deal — Anthropic getting massive compute commitment from Google (who already owns ~10% of Anthropic), with Peter Gostev’s Compute Wars chart showing the gap to OpenAI closing

  • Anthropic ships Managed Agents — fully hosted agent runtime + infrastructure. Selling outcomes, not tokens

  • Meta launches Muse Spark, the first model from Meta Superintelligence Labs, with natively multimodal reasoning, multi-agent Contemplating mode, and deep health/visual capabilities (X, Blog)

  • Simon Willison deep dives into Meta’s Muse Spark model and uncovers 16 hidden tools including visual grounding and sub-agents in the meta.ai chat UI (X, Blog, Announcement)

• Open Source LLMs

  • GLM-5.1 from Z.ai is #1 open-source on SWE-Bench Pro at 58.4%, runs autonomously for 8 hours with 1,700+ agent steps (X, HF, Arxiv)

  • Gemma 4 crosses 10M+ downloads, 1,000+ Gemma-4-based fine-tunes on HF. Did really well on Arena considering size — Peter Gostev confirmed it smashed many models on the Pareto curve

  • Nisten’s pick: Hermes 27B — trained specifically to be paired with the Hermes harness, allegedly distilled from Opus API. Model + harness shipped together as a portable unit

• Tools & Agentic Engineering

  • OpenClaw 2026.4.5 — biggest release since 4.0: /dreaming goes GA (Light/Deep/REM memory consolidation with a Dream Diary in DREAMS.md), built-in video + music generation across 4 backends, GPT-5.4 as new default, prompt-cache reuse improvements, Control UI + docs in 12 new languages (Release, Vincent, Dreaming docs, FOD#147)

  • OpenClaw codebase now ~1.5M lines including unreleased iOS + Android native apps. GitHub literally caps at “5K+” PRs/issues — they hit the ceiling

  • Anthropic did NOT ban OpenClaw — they made Max-tier subscription usage of Opus via OpenClaw significantly more expensive, pushing many users to GPT-5.4 via Codex

  • Codex hits 3M weekly active users — up from 2M last month. VB walked through plugins (Stripe, Supabase, shadcn), sub-agents, Guardian Approvals (auto-classify tool-call risk), and experimental hooks

  • Cursor: remote agents + code review agent (78% issues caught pre-merge)

  • MemPalace: Milla Jovovich and Ben Sigman’s open-source AI memory system goes viral with 26K GitHub stars in 2 days, claims top benchmark scores, then transparently walks back overstated claims (X, GitHub, X, X, GitHub)

• This Week’s Buzz (Weights & Biases)

  • W&B Automations are LIVE — event triggers from your runs into notifications, GitHub Actions, deployments. Pairs nicely with the new iOS app

  • GLM-5.1 and Gemma 4 both up on W&B Inference

  • Wolfram published an in-depth blog post on his finding that more reasoning is not always better (models can get dumber with more thinking time) — full writeup on wandb.com

• Vision & Video

  • Seedance 2.0 launches in the US — on Replicate with up to 9 reference images, 3 videos, and 3 audio files for cinematic AI video generation (X, Announcement). Peter Gostev confirmed it jumped ~80 ELO points above the next video model on Arena — a massive gap where most video models cluster within 10 points

  • HappyHorse-1.0, a mysterious 15B video model from Alibaba’s Taotian Group, takes #1 on Artificial Analysis video arena beating Seedance 2.0, Kling 3.0, and Grok Video (X, X, X, X, Blog)

  • The Harry Potter “Drip Wizards” AI slop trend — Seedance-powered Hogwarts videos going hugely viral

• AI Art & Diffusion & 3D

  • OpenAI’s GPT-Image-2 leaked on LM Arena under three codenames (maskingtape / gaffertape / packingtape), showing photorealism and text rendering that may dethrone Google’s Nano Banana Pro (X, X, X)

• Show notes & key moments

  • Swyx on harness engineering: gains are coming from the harness, not the weights. The big labs are investing more and more in harness — it’s not going away. Skills (English-as-programming-language) are increasingly absorbing harness work

  • Swyx on AI Engineer tracks: MCP is “more settled and stable, therefore less interesting.” Coding agents track is bigger this year (Cursor, Factory, super-long-running). Voice & Vision split from Generative Media — multimodality as a single track no longer makes sense

  • Swyx on supply chain attacks: light LLM and Axios issues mean you should “vendor everything” — pip fork instead of pip install. Tool requests becoming prompt requests

  • Peter Gostev on Mythos pricing: $25 / $125 per M tokens (~5x Opus 4.6). But the real reason it’s not public isn’t safety — Anthropic likely just doesn’t have the compute to serve it

  • Peter Gostev on Compute Wars: OpenAI is way ahead of Anthropic on compute. The new Google TPU deal is Anthropic catching up — and weird that Google is propping up a competitor to DeepMind. (Same pattern as when Google’s $2B Anthropic investment effectively propped up AWS vs Google Cloud)

  • Peter Gostev on Arena data: Arena released 3 years of historical leaderboard data + actual prompts as datasets on Hugging Face. Previously he was scraping it by hand into Google Sheets — now he has Databricks access

  • VB on Codex workflows: every morning at 9 AM, Codex automation reads his Slack mentions, cross-references Gmail and Calendar, and creates a 5-minute pre-brief calendar event for upcoming meetings. None of it is “coding” — it’s all plugins + connectors

  • Vincent Koc on the GPT-5.4 personality problem: model is incredible at coding but “soulless.” Wolfram noticed it back in December and cancelled his subscription. Alex cloned the Rocky voice from Project Hail Mary and put it in his OpenClaw — “amazing”

  • Vincent Koc on Dreaming: three phases (REM, core, deep sleep) that defrag agent memory. The dream log is for the human in the loop — makes memory inspectable in a way a non-technical person (a mom) can understand

  • Vincent Koc on architecture: the open-source flood forced OpenClaw into a plugin architecture. “Not Lego — Ikea.” Refactored ~1M lines in 9 days at 2 AM at NVIDIA before Jensen’s keynote

  • Omar Sanseviero on Gemma 4: 500M+ total Gemma downloads across all variants. Gemma is the foundation for the next generation of Gemini Nano on Pixel/Samsung. Lama.cpp vision capability fixes shipping

  • Wolfram’s Pixel/Google household: kids using AI Studio + Antigravity to build games, his 70-year-old mother using voice unlock on her Pixel

  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.

What a week! Anthropic is in the spotlight again, first with #SessionGate, then with the whole Claude Code source code leak, and finally with an incredible research into LLM having feelings!? (more on this below).

And while Anthropic continues to burn through developer good will faster than their sessions, OpenAI announced a MASSIVE $122B round of funding (largest in history), Google released Gemma 4 with Apache 2 license - we had Omar Sanseviero on the show to help us cover what’s new, Microsoft dropped 3 new AI models (not LLMs) and PrismML potentially revolutionized local LLM inference with lossless 1-bit quantization!

P.S - Oh also, something on X algo changed, I get way more exposure now, 3 out of my best 5 posts ever have been from this week + I got the coveted Elon RT on my Claude Code leak coverage. I’ll try to stay humble 😂 Anyway, let’s dive in, don’t forget to hit like or share with friends, and TL;DR with links is as always, at the bottom:

The Claude Code source Leak: Half a Million Lines of “Oops”

So here’s what happened. On March 31st, Anthropic shipped Claude Code version 2.1.88 to npm. Inside that package was a 59.8 megabyte source map file — basically a debugging artifact that contained the entire compiled source code. 512,000 lines of TypeScript across 1,900 files. The entire playbook for how the Claude Code harness works, including a lot of stuff that wasn’t supposed to be public yet.

A researcher named Chaofan Shou spotted it at 4 AM ET, posted the download link, Sigrid (who came to the show) posted it on Github and within six hours it had 3 million views and 41,000 GitHub forks (This repo is the highest starred repo in Github history btw, with well over 150K Github stars). Anthropic started filing takedowns, but the internet being the internet, it was already everywhere. The source code is still on tens of thousands of computers right now. (I won’t link directly but there’s a website called Gitlawb, look it up)

The community went absolutely wild digging through the source code btw, and they found some interesting things!

KAIROS: Claude Code is going to become a Proactive Agent!

This is the biggest take-away from this leak IMO, that like OpenClaw/Hermes agentic harnesses, Claude Code is already a fully featured proactive agent, we just don’t have access to this yet. With KAIROS, Claude Code will have it’s own daemon (will run independently from the CLI), will have a background ping system (hello Heartbeat.md from OpenClaw) that will make it wakeup and do stuff, will do “autodream” memory consolidation reviewing your daily sessions and fix memories, subscribe to Github, and maintain daily appent-only logs to show you what it did while it and you were asleep.

This is by far the hugest thing, I’m excited to see how / when they ship KAIROS, as I said, 2026 is the year of Proactive agents!

My Wolfred OpenClaw agent summed it up very nicely:

Undercover Mode

For Anthropic employees working on public repos, there’s an Undercover Mode that auto-activates and strips all AI attribution from commits. The system prompt? “Do not blow your cover.” They really said “this is fine” about shipping internal tools to production while hiding from the world that AI wrote the code. Which, honestly, is kind of incredible meta-humor from whoever wrote that.

The Buddy System

My personal favorite discovery: there’s a hidden Tamagotchi-style terminal pet called the Buddy System with 18 obfuscated species, rarity tiers (including a 1% legendary), cosmetic hats, shiny variants, and stats like DEBUGGING, PATIENCE, and CHAOS. If you activate it now, you can do /buddy and you’ll have a little companion judging your coding decisions. Anthropic shipped a game inside their CLI tool. Mine is called Vexrind and he’s sarcastic as fuck, I’m not sure I like it.

Anti-Distillation Protections

The code also revealed that Claude Code injects fake tool calls into logs to poison training datasets. If you’ve been backing up your .claw folders to train on the data; Stop. Pass your data through something like Qwen or make sure you’re filtering out the noise. (a Nisten tip)

The Models That Don’t Exist Yet

Buried in the code are references to Opus 4.7, Sonnet 4.8, and a model called capybara-v2-fast with a 1 million context window. These haven’t been released. This is yet another confirmation of the leaked “Mythos” model that’s coming soon from Anthropic.

Which btw, with Anthropic very rocky uptime lately, the tons of SessionGate issues, the leaked blog announcing Mythos, the leaked Claude Code oopsie, they are not having the best Q1 in terms of proving to the world that they are the safest lab out there. I hope they protect their weights better than they protect everything else, before the rumored IPO later this year.

SessionGate is still not solved, despite the official response

I told you about session gate last week, and since then we got, finally, and official acknowledgement from Anthropic. But before that, some folks on Reddit reverse-engineered Claude Code (this was before the source code leak ha) and found a few caching bugs that potentially cause 10-20x increase in price if you use --resume a lot especially.

While folks continue to complain about burning through Max account quotas much faster than before, here’s the official response from Anthropic, after the supposed investigation, turns out, we’re using it wrong 🤦‍♂️

My take is simple: Anthropic has one of the best models in the world, maybe the best personality plus coding stack in some situations, and they are squandering a chunk of goodwill by not being much more explicit about decreased limits, caching bugs, routing, and usage behavior. Nothing else to add here, really bad DevEx, people can handle bad news. They hate opaque bad news.

Gemma 4 Is Here, Apache 2.0, and Honestly… This Is a Big One (HF)

This was the hopeful turn in the show. You know we LOVE open source!

Right in the middle of all the Anthropic chaos, Google dropped Gemma 4, and Omar Sanseviero from DeepMind joined us live to talk through it. This launch hit a bunch of notes I care a lot about: strong local-friendly sizes, serious open distribution, Apache 2.0 licensing, agentic improvements, and a clear willingness to listen to community feedback.

The headline model for me is the 31B Gemma 4. It’s big enough to matter, small enough to actually run in serious local setups, and strong enough that the benchmark chart looks slightly ridiculous. On LM Arena, it is competing far above what you’d intuit from the raw parameter count. When a 31B model starts getting uncomfortably close to models in the several-hundred-billion range, you pay attention.

That was really the vibe on the show. It wasn’t just “nice, another open model.” It felt more like: wait, local models are seriously back.

Gemma is the new LLaMa

When I asked Omar where local models are going, his answer was optimistic: “The open models catch up to proprietary models relatively quickly. If you compare Gemma 3 to Gemma 4, it’s matching proprietary capabilities from eight months ago. Being able to run those capabilities directly in the user’s hardware — that’s the future.”

The 31B model downloads as about 18-20GB depending on quantization. With the right setup, you can run it on a single GPU. This is exactly what the open source community has been asking for: frontier-level intelligence that you can actually run yourself.

OpenAI’s largest in history $122B funding round + TBPN acquisition

While OpenAI quietly meme’d around the Anthropic leak but mostly stayed silent on the releases, they did announce 2 pretty huge things.

First, OpenAI raised an absolutely bonkers, insane, unreal $122 Billion dollars round, largest in history, 2x bigger than the previous record round, which was OpenAI. Amazon put in $50B, Nvidia $30B, SoftBank $30B — all three of whom are also OpenAI’s biggest vendors. They’re generating $2 billion per month in revenue with 900 million weekly active users, but still burning roughly $150 million per day and projecting a $14 billion loss this year, making the upcoming IPO a financial necessity rather than a choice.

And they’re not just spending on compute — today OpenAI acquired TBPN (TBPN is a tech-focused media company / live show), in a very “surprising” deal, rumored to be in the “low hundreds of millions”, OpenAI has purchased a very tech-positive show. Shoutout to Jordi Hays and John Coogan + TBPN team. Proving that live show format means a lot in the era of fake AI news. This could potentially price TBPN higher than Washington Post, make the founders multi millionaires and give OpenAI a direct to consumers media angle. Very interesting purchase.

This weeks buzz - W&B corner + Wolfbench update

Quick 2 things, this weekend I flew for 1 day to San Francisco, to host one of the most unique hackathons i’ve ever saw, in this one, AI wrote the code, but humans were punished if they touched their laptops! Yes, with a “lobster of shame” they used Ralph loops and talked to each other intead of hacking. I edited a video of it, hope you enjoy my summary:

@altryneThis weekend I participated in the worlds most unique Hackathon, where humans get punished for touching their laptops! It's called Ralphton, with a sharp focus on Ralph loops, where humans define the specs only, and then send their coding agents to run autonomously and build! It was a lot of fun, oh and the punishment? well, check it out! 🔗 Subscribe to our show on Spotify: thursdai.news/spotify 🔗 Apple: thursdai.news/apple 🔗 Youtube: thursdai.news/yt And for the full show notes and links visit 👉 thursdai.news 👈 #hackathon #koreans #llms #ralph #openclaw

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The other, and potentially much bigger news, comes from Wolfram and WolfBench.ai

I’ve tasked Wolfram to expand our findings, and he tested the new Hermes Agent (from Nous Research) against OpenClaw, Claude Code and found that... drum roll... Hermes Agent performs way better on Terminal Bench, than either Claude Code and OpenClaw. 😮

Here’s the clip of him explaining, and you can find all our findings and methodology here

PrismML’s 1-Bit Bonanza: The Biggest ML Discovery in Half a Decade

My co-host Nisten called it, and I think he might be right: this could be the biggest machine learning discovery in recent memory.

PrismML emerged from stealth this week with their 1-bit Bonsai model family. Their 8B model is 1.15 gigabytes. A full-precision Qwen3 8B is 16 gigabytes. That’s a 14x size reduction, with no significant quality loss.

Let that sink in for a second. We’re talking about each weight being literally one bit — a plus or minus sign, with a scaling factor. Not “4-bit quantization” or “int8” — actual binary weights. This shouldn’t work. Neural networks need precision to learn. And yet.

The research comes from professor Babak Hassibi at Caltech, who’s been working on this for 34 years. He started this research in 1992. It took three decades, but it finally works.

The results are genuinely shocking. The 8B model runs at 368 tokens per second on an RTX 4090, which is 6.2x faster than the full-precision version. On an M4 Pro via Metal, it hits 85 tokens per second. Energy efficiency is 5x better. And here’s the kicker: the 1.7B variant hits 130 tokens per second on an iPhone 17 Pro Max.

Nisten tested the 8B model himself with a 60,000 token context window on an old gaming PC. It ran at 50 tokens per second, used 2.6 gigabytes of RAM, and was completely coherent. “This just blows everything else outta the water,” he said. “We’re going to get 100,000 token AI chips in our phones because at 1 bit you don’t even have to do math anymore. You can just do lookup tables. You can even make a mechanical AI at 1 bit.”

This pairs perfectly with the Turbo Quant KV cache compression techniques we talked about last week. Compress the weights with 1-bit, compress the context with Turbo, and you’re looking at models that run anywhere. The democratization of AI is about to hit another gear.

The models are Apache 2.0 on HuggingFace with GGUF and MLX formats already available.

⚡ Speed Round: Alibaba, Fish Audio, Veo, Liquid AI, Cursor 3

There was a lot more this week than we could go deep on, so here are the biggest quick hits.

Alibaba kept shipping. Qwen 3.6 Plus is pushing hard on agentic coding and long context. Qwen 3.5 Omni is the bigger multimodal story, with text, image, audio, and video all under one umbrella. I still think Alibaba deserves more credit than they get in Western discourse for just how relentlessly they keep delivering.

Wan 2.7 Image also looked very strong on text rendering, editing, and image consistency. I’m still slightly grumpy that more of this stack is API-only, but the capabilities are clearly moving.

Google launched Veo 3.1 Lite, cutting video generation prices way down. Five cents per second at 720p is a pretty aggressive number. Whenever Google starts doing this kind of price move, my first thought is usually: okay, what bigger release are they preparing for?

Fish Audio’s STT was another cool one. This isn’t just speech-to-text for transcription. It’s built to feed directly into voice pipelines, with emotion and paralanguage tagging that lines up with their TTS stack. That is exactly the kind of vertical product thinking I love seeing in audio.

And Liquid AI’s LFM2.5-350M deserves a shout too. A 350M model doing credible tool-calling and agentic tasks is just another reminder that the small-model frontier is getting very weird, very fast.

Lastly, Cursor 3 launched as a rebuilt, agent-first interface. I didn’t spend as much time on it during the show as it probably deserves, but the broader trend is impossible to miss: coding tools are evolving from editors-with-assistants into actual fleet managers for agents.

Anthropic’s Emotion Vectors: How they found out what Claude is “feeling”

I want to end where we ended the show, because this one really stuck with me.

Anthropic published research on emotion concepts inside Claude. Not in the fluffy “the model feels things” sense, but in the mechanistic interpretability sense. They identified internal representations associated with things like fear, love, joy, and desperation, then studied how those activations affected behavior.

This got fascinating fast.

One example they showed involved Claude trying and failing at a difficult programming task. As repeated failures mounted, the internal “desperation” vector increased. Under those conditions, the model became more likely to produce hacky, spirit-of-the-task-violating solutions. When they dialed in a “calm” vector instead, cheating behavior dropped.

That is just… wild.

It’s not that the model is “feeling” human emotions in a clean anthropomorphic sense. But it is that internal behavioral geometry we can label in emotional terms seems to shape what the model does. And once you can detect and influence those latent directions, you’re no longer just prompting a black box. You’re doing something closer to behavioral neuroscience for neural nets.

This also reframes a lot of day-to-day prompt engineering. Maybe the best users aren’t just the ones who structure tasks clearly. Maybe they’re also the ones who consistently keep the model in productive psychological territory, so to speak.

I know that sounds weird. Welcome to Q2 of 2026, the first year of the singularity!

Closing Thoughts

This week was Passover, we celebrated at our house, half the conversation was about who has an OpenClaw and who wants one, and as I’m writing this, I’m on my way to install a bunch of proactive agentic AIs for my friends. Ryan Carson on the show got finally convinced and he’s chief of staff R2 is now an OpenClaw and he says it beats a human, he actually open sourced it live on the show. Claude Code leak confirmed that this is also where they are taking the ecosystem. So buckle up!

Also, next week show is going to be streamed live from the AI Engineer conference in London, the first European one, if you’re in Europe and coming, hope to see you there! Please share ThursdAI with a friend or give us a 5 star rating, apparently AI reporting live shows are getting acquired for 100s of Millions of dollars now 😂 Your support will greatly help us get established in this area after 3 years. See you next week

TL;DR and Show Notes

TL;DR and Show Notes

• Show Notes & Guests

  • Alex Volkov - AI Evangelist & Weights & Biases / CoreWeave (@altryne)

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

  • Sigrid Jin (@realsigridjin) & Bellman (@bellman_ych) — creators of claw-code, fastest GitHub repo to 100K stars

  • Omar Sanseviero (@osanseviero) — DevEx at Google DeepMind, Gemma 4 launch

  • Ralphton Hackathon video (TikTok)

  • WolfBench.ai — agent harness benchmarking (Site)

  • Ryan’s Claw Chief open source setup (GitHub)

• Big CO LLMs + APIs

  • Claude Code’s entire 512K-line source code accidentally leaked via npm — revealing KAIROS daemon, Undercover Mode, Buddy System, anti-distillation protections, and unreleased model references (Alex’s thread, Fried_rice’s discovery, VentureBeat)

  • Anthropic SessionGate continues — cache bugs reverse-engineered, --resume flag causes 10-20x cost increase, silent Opus→Sonnet fallback reported (Alex’s cache bug post, Alex’s quota post, Reddit investigation, GitHub analysis)

  • OpenAI closes $122 billion funding round — largest in history, $852B valuation, IPO incoming (X, Breakdown)

  • OpenAI acquires TBPN — live tech media show, rumored low hundreds of millions

  • Microsoft MAI drops 3 in-house models — #1 transcription (MAI-Transcribe-1), #3 image gen (MAI-Image-2), expressive voice (MAI-Voice-1) (Mustafa post, Transcribe blog, Image blog)

  • Alibaba Qwen3.6-Plus — near-Opus 4.5 agentic coding, 1M context (X, Blog)

  • Cursor 3 — agent-first rebuild, no longer VS Code fork, parallel cloud/local agents (X, Blog)

  • Anthropic publishes emotion vector research — desperate Claude cheats more, calm Claude cheats less (X, Alex’s reaction)

• Open Source LLMs

  • Google Gemma 4 — Apache 2.0, 31B / 26B MOE / 8B / 5B, local-friendly, agentic tool use, 256K context (HF Collection, try in AI Studio)

  • PrismML Bonsai 1-bit models — 8B in 1.15 GB, 10x intelligence density, 34 years of research (X, HF, Site)

  • Liquid AI LFM2.5-350M — agentic tool calling at 350M params, under 500MB quantized (X, HF, Blog)

  • Alibaba Qwen3.5-Omni — native omni-modal (text, image, audio, video), 397B total / 17B active (X, Blog)

  • Tools & Agentic Engineering

  • Claw-code — Claude Code leak backup → clean room rewrite → fastest repo to 100K+ stars (GitHub)

  • WolfBench results: Hermes Agent outperforms Claude Code and OpenClaw on Terminal Bench 2.0 (WolfBench.ai)

  • Ryan Carson open sources Claw Chief — AI chief of staff with skills, crons, scheduling (GitHub)

• Vision & Video

  • Google Veo 3.1 Lite — $0.05/sec at 720p, cheapest video gen yet, price cuts coming April 7 (X, Docs, Pricing)

• Voice & Audio

  • Fish Audio STT — automatic emotion tagging, feeds directly into S2 TTS pipeline (X, App, Blog)

• AI Art & Diffusion

  • Alibaba Wan2.7-Image — unified generation, editing, text rendering, multi-image consistency (X, Site)

• This Week’s Buzz

  • Ralphton hackathon at W&B SF — humans write specs, AI builds, touch your laptop = lobster of shame (Alex’s video, TikTok)

  • WolfBench update — Hermes Agent > Claude Code on most model combos

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 fuck” 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!)

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 statement
that 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 fuck 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)

(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:

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.

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 - 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)

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.

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.

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)

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.

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)

  • BullShit 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)

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.

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 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!

Here’s the TL;DR and show notes:

ThursdAI - Feb 26, 2026 - TL;DR

• Hosts and Guests

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

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

  • Ben Cera (@bencera_) - Founder Polsia

  • Nader Dabit (@dabit3) - Growth at Cognition

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

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

• Big CO LLMs + APIs

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

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

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

• Open Source LLMs

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

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

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

• Evals & Benchmarks

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

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

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

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

• Tools & Agentic Engineering

  • Happy 1 year Birthday Claude Code!

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

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

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

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

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

  • Nous research agent (X)

  • Perplexity Computer (blog)

  • Microsoft Copilot tasks (blog)

• This weeks Buzz - Weights & Biases update

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

• Interviews mention links

  • Ben Broca - polsia.com/live Polsia Dashboard

  • Nader Dabit - on seeing the future (blog)

  • Philip Kiely - Inference Engineering book (Book)

• Vision & Video

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

• Voice & Audio

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

• AI Art & Diffusion & 3D

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

  • Quiver solves SVG with Arrow 1.0 (X)

• Others

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

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

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

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

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

https://thursdai.news 🎉

Great week all in all, let’s dive in!

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:

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

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

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

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

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

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

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

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

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

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

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

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

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

Voice & Audio

Google DeepMind launches Lyria 3 (try it)

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

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

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

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

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

See you all next week!

-Alex

ThursdAI - Feb 19, 2026 - TL;DR

TL;DR of all topics covered:

• Hosts and Guests

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

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

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

• Open Source LLMs

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

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

• Big CO LLMs + APIs

  • OpenClaw founder joins OpenAI

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

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

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

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

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

• This weeks Buzz

  • Wolfram deep dives into Terminal Bench

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

• Vision & Video

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

• Voice & Audio

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

• Tools & Agentic Coding

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

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

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

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

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

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

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

Open Source LLMs

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

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

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

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

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

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

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

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

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

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

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

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

Big Labs and breaking news

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

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

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

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

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

OpenAI & Anthropic fast modes

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

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

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

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

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

Vision & Video

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

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

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

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

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

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

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

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

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

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

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

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

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

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

🛠️ Tools & Orchestration: Entire Checkpoints & WebMCP

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

Entire Raises $60M Seed for OSS Agent Workflows

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

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

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

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

Chrome 146 Introduces WebMCP

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

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

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

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

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

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

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

ThursdAI - Feb 12, 2026 - TL;DR

TL;DR of all topics covered:

• Hosts and Guests

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

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

  • Lou from Z.AI (@louszbd)

  • Olive Song - Lead RL at Minimax @olive_jy_song

• Open Source LLMs

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

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

• Big CO LLMs + APIs

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

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

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

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

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

• This weeks Buzz

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

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

• Vision & Video

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

• AI Art & Diffusion & 3D

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

• Tools & Links

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

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

  • RyanCarson AntFarm - Agent Coordination (X)

  • Steve Yegge’s “The AI Vampire” (X)

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

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

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

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

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

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

Anthropic & OpenAI are neck in neck

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

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

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

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

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

Agent Teams - Anthropic’s built in Ralph?

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

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

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

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

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

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

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

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

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

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

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

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

In other big company news:

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

Open Source AI

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

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

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

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

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

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

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

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

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

This weeks buzz - Weights & Biases update

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

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

Vision, Video and AI Art

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

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

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

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

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

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

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

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

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

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

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

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

On security of bots and skills

.md is the new .exe

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

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

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

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

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

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

ThursdAI - Feb 05, 2026 - TL;DR

• Hosts and Guests

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

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

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

• Open Source LLMs

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

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

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

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

• Agentic AI segment

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

• Big CO LLMs + APIs

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

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

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

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

• This weeks Buzz - Weights & Biases update

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

• Vision & Video

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

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

• Voice & Audio

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

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

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

• AI Art & Diffusion & 3D

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

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

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

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

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

Play any reality - Google Genie3 launches to Ultra Subscribers

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

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

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

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

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

Chrome steps into Agentic Browsing with Auto Browse

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

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

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

Other news in big labs:

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

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

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

Open Source AI

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

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

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

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

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

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

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

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

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

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

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

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

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

Arcee AI Trinity Large: The Western Open Source Giant

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

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

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

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

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

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

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

Vision, Video and AI art

xAI Grok Imagine API: #1 in Video Generation

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

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

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

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

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

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

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

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

AI Art Quick Hits:

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

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

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

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

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

ThursdAI Jan 29 , TL;DR and show notes

• Hosts and Guests

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

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

• Open Source LLMs

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

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

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

• Big CO LLMs + APIs

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

  • Anthropic launches MCP Apps (X)

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

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

• This weeks Buzz

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

• Vision & Video

  • Google DeepMind launches Project Genie (X, Announcement)

• Voice & Audio

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

• AI Art & Diffusion & 3D

  • xAI launches Grok Imagine API (X, Announcement)

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

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

• Tools

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

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

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

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

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

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

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

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

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

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

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

Installing

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

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

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

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

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

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

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

Show notes and links:

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

• Hosts and Guests

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

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

  • Guest Dan Peguine ( @danpeguine )

• DeepDive - Clawdbot with Dan & Wolfram

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

• Open Source LLMs

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

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

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

• Big CO LLMs + APIs

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

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

• This weeks Buzz

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

• Vision & Video

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

• Voice & Audio

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

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

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

• Tools

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

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

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

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

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

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

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

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

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

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

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

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

GPT‑5.2 Codex: Long‑Running Agents Are Here

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

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

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

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

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

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

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

Gemini Personal Intelligence: The Data Moat king is back!

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

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

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

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

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

Apple & Google: The Partnership (and Drama Corner)

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

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

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

This Week’s Buzz: WeaveHacks 3 in SF

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

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

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

Deep Dive: Agent Skills With Eleanor Berger

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

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

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

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

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

Get started with skills

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

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

The Skills Demo That Changed My Mind

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

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

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

Open Source AI

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

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

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

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

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

Vision, Voice & Art (Rapid Fire)

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

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

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

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

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

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

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

TL;DR and Show Notes

• Hosts & Guests

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

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

  • Guest: Eleanor Berger (@intellectronica)

• Open Source LLMs

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

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

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

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

• Big CO LLMs + APIs

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

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

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

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

• Partnerships & Drama

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

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

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

• This Week’s Buzz

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

• Vision, Voice & Audio

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

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

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

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

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

• Deep Dive: Agent Skills

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

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

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

Hey folks, Alex here from Weights & Biases, with your weekly AI update (and a first live show of this year!)

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

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

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

Open Source: The “Small” Models Are Winning

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

Solar Open 100B: A Data Masterclass

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

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

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

MiroThinker 1.5: The DeepSeek Moment for Agents?

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

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

Liquid AI LFM 2.5: Running on Toasters (Almost)

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

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

NousCoder-14B and Zhipu AI IPO

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

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

Big Companies & APIs

NVIDIA CES: Vera Rubin Changes Everything

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

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

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

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

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

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

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

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

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

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

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

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

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

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

OpenAI Launches GPT Health

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

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

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

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

Doctronic: AI Prescribing Without Physician Oversight

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

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

Google Brings Gmail into the Gemini Era (X)