What a freaking week! A week after every lab head agreed to “pace the frontier”, Anthropic and OpenAI shipped big new models within hours of each other, and both of them are CHEAPER. So much for pacing 😂

And we had a new producer on the show today! Opus 5.5 listened to us live, put up the chyrons, kept me on time (mostly) and even fact-checked Nisten on air. If the stream died, you knew who to blame. You can re-watch it work on thursdai.live if you want to experience being there with us!

3 huge themes this week: pacing the frontier does not mean stopping, Assistants not just agents (Meta went all in on Muse at Connect), and voice, where Google now says it has the best TTS in the world... and it clones voices.

With me: Peter Gostev (Arena), Nisten, Yam Peleg, and Wolfram Ravenwolf live from AI Engineer Paris (thanks Mazi for the tether!), plus JevBench creator Florian S. Let’s dive in (all links at the end as always!)

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Frontier AI - not pacing yet!

Claude Opus 5.5 - Opus is BACK, Fable-level smarts for 40% less (X, Blog, System card)

Folks. FOLKS. Opus 5.5 is the highlight of my week, and it’s not even close. It’s like Opus 4.6 is back, with Fable-level abilities, and it talks like a normal person again - no more Jargon Douche Claude!

My week started with burning through my quotas on Fable, and I was like, oh no, I need Claude for production on Thursday! Then Opus 5.5 dropped and... I just couldn’t get to the end of my quota. I ran workflows, agents, Claude Code for hours with no end in sight. Remember the “limitless Codex” days when you never thought about quotas? Then Astra came out and I burned my entire weekly quota in half a day (ps. this was partly due to a bad config, so if Astra is burning your tokens, keep reading for a fix). Now it’s flipped, and Claude is the seemingly limitless one. And it’s fast!

The numbers back it up. It beats Anthropic’s own Fable 5.1 on GDPval-AA (1846 vs 1735), 66.4% on Terminal-Bench 4.0 vs 57.9% for GPT-6 Astra, and it’s 40% cheaper than Opus 5. Output goes from $25 to $20 /1Mtok and cached reads from 50 cents to 20 cents, and for agentic coding the cache reads are most of your bill, so you really feel it. Basically the smaller, overachieving brother of Fable. Sonnet and Haiku 5.5 are coming in the next few weeks too.

Peter came in hot with Arena news: Opus 5.5 is #1 on Code Arena, above Astra, and the HTML and 3D stuff it generates is “completely insane.” His one caveat: on his hardest max-effort prompts, a single generation cost $60 to $80 in API terms, so the long tail can still get pricey.

And then Nisten, who (like all of us) has specific opinions about Anthropic’s politics, said it writes the best code he’s seen, it’s crazy good at WebGPU and kernels, and it’s “an absolute banger.” It’s his default now. Folks, do you understand what it takes for Nisten’s default to NOT be some obscure open source model he runs himself on 17 GPUs?! Anthropic won over Nisten. I don’t think you guys get what just happened here.

Wolfram is the holdout, he left Anthropic over the OpenClaw bans and hasn’t touched it since. Wolfram, how do I say this gently... you’re an evaluator, this is not allowed 😂

An Anthropic employee basically said “sorry for Opus 5, we hope this makes up for it”, and honestly it does. Opus 5 was slow, incoherent and full of Claude-isms. Nobody wanted to talk to it. The Claude-isms are gone, the jargon is gone, this model talks like a human. I used to love Opus back in the Opus 3 days, and it feels like that again.

Want to squeeze even more out of it? Read the great Addy Osmani’s guide to Opus 5.5 (@addyosmani). 2 things blew my mind: stop writing “think carefully” in your prompts (”You don’t need to ask it to think”, it picks its own depth, and replies start sooner without it), and one early tester found Opus 5.5 on its LOWEST effort caught more bugs than Opus 5 on high, with fewer false alarms. Try low effort before you go max!

Tip from me to you: if it ever gives you something confusing, the pstack “bro” skill (/bro) makes it say it again in human words. I use it all the time.

(Full disclosure: Opus 5.5 produced the show AND helped with this writeup, so it might be a tiny bit biased 😅 but the quota thing is 100% me.) Anthropic, please, please don’t nerf this one.

GPT-6 Sol and Luna - half the price, and honestly better than Astra for me (X, Blog, Caching)

Hours later, OpenAI answered with GPT-6 Sol ($2 in / $10 out) and Luna (10 cents in / 50 cents out, that’s basically free!), at half the GPT-5.6 price. The lineup is now Astra, Sol and Luna (bye Terra). On DeepSWE, Sol gets 68.8 and Luna 66.6. The sneaky big one for builders: 90% off cached input, and changing reasoning effort or tools no longer busts your cache 👏

Ok, hot take... Astra has been kind of dumb for me day to day, burning tokens for crappy results. Sol is great, and Luna is even better! Peter agrees, Sol is his daily driver and he only flips to Astra when Sol can’t do it. Wolfram misses the humor and personality 5.6 had, so “keep 5.6” is officially the new “keep 4o.” Reddit says the news is the price, not the performance, and... they’re not wrong.

PSA if Codex is eating your account: I had previously set my context window to 1 million tokens via the se, which kills OpenAI’s cache and rips through your quota. Removed it, moved sub-agents to a cheaper model, and usage went right back to normal. Check your codex.toml! if you don’t know how, just so just ask your codex to diagnose itself.

Also from OpenAI this week, a 4-area plan for independent safety audits. No auditors, dates or funding yet, but hey, it’s the pacing idea on paper. (Blog)

Assistants are not agents

Meta Muse gets an inbox, your Mac, and a place on your face and becomes a Tamagochi!? (X, My Connect supercut)

Muse is #1 in the App Store (to be precise, it got there faster than ChatGPT did, not more users... yet). Zuck says Muse is now the center of everything Meta builds, and they shipped a LOT: it controls your Mac, gets its own email you can CC, does real-time voice and video with a face you design, and keeps working while you talk to it. It’s coming to the glasses with a custom wake word, so yes, I get to say “Hey Wolfred” 😂

If you don’t have an hour to watch the whole keynote (it was a good one!), I cut a 2.5 minute supercut of the most important announcements for you 👇

On the hardware side: new Ray-Ban Meta Gen 3 (I already ordered, will report next week), audio-only glasses with no camera that are also FDA-certificed hearing-aid! (maybe the most important launch for a lot of people!), VR Glasses that look like normal glasses, and the Muse Charm, a Tamagotchi-like keychain shipping in December.

But the part that got me? Every Muse comes with a real cloud VM (root, 8GB RAM, 100GB disk). Nisten has been living in it, Tailscaled into it, and when it hit a CAPTCHA he tapped it on his phone and it just kept going. 100 million tokens a week plus a computer in the cloud, free, for everyone! Peter’s take: it’s the only big consumer app that doesn’t treat people like idiots. Meta’s pitch is no ads (they are going for a novel “we’ll get a take from the transactions you do with muse and our partners like Shopify, BestBuy, Walmart and a bunch more they announced) and a private VM co-designed with Moxie Marlinspike, though Peter doubts anyone’s parents care.

Assistant tip: connect your email, go on a walk, hit record and just talk about your life. Assistants are only as good as what they know about you. Mine now checks our weekend plans for activities cancellations after I woke up too damn early to drive kids to a karate class to find out it’s closed that week & searches local events every Thursday! Weekend plans solved - proactively!

Grok 4.7 disappoints... but Grok in your Tesla is the real news (X, Blog, Tesla)

Sorry Cursor folks. Grok 4.7 gets 46.3 on CursorBench (up from 40.4), $2 per million with 500K context, but it’s still behind even GPT-5.6 on where it matters, and they compared 4.7 on xHigh against 4.6 on High 🤨 Everyone on the panel agreed, disappointing. Peter won’t write them off yet, my read is it’s a talent and data problem, not compute.

But here’s what I AM excited about: Grok Connectors in Tesla. One guy asked his car for his usual Starbucks, Grok placed the order, set the destination, and it was paid and waiting when he got there. My car already drives itself, and soon it’ll do my email. Your car has MCP now, folks! (SuperGrok Heavy only, and I don’t have it in my car yet 😭)

Fun moment: our Opus 5.5 AI producer fact-checked Nisten live on how many Teslas are on the road. About 10 million (9.2M delivered by Q1 plus ~480K in Q2). Nisten was right! He wants the same thing for politicians.

This Week’s Buzz 🐝

Next week ThursdAI is LIVE from Fully Connected at Moscone South in SF (Sep 29 to Oct 1, the day after OpenAI DevDay), with Fei-Fei Li, BattleBots and... yes, Pitbull! We start at 11am Pacific. Come hang, listeners get in free with code THURSDAIFC2026 (Register)

Also huge news for us, CoreWeave got Platinum on SemiAnalysis ClusterMAX again, 3 reports in a row, the only provider to do it 💪 (SemiAnalysis)

And W\&B Hive Mind saves every agent conversation across harnesses and machines, and lets you fork them. I fork Codex sessions into Claude for a review all the time. Plus, it’s completely free! (Try it)

Open source cloned Jev in a week

The Jev clones are here, and they run in your browser (classifier.dev, jeff, Laya demo)

Last week I said open source would copy Jev fast. It took less than a week! The clones speak Jev’s format, so you point the TypeSafe SDK at a different URL and it just works. jeff is MIT and ~6x cheaper to self-host, and Laya went viral as the “Jev killer” (its own model card is a lot more modest).

Laya’s real trick is running locally. Nisten’s WebGPU demo loaded ~600MB into my browser and classified Hugging Face model cards at 132ms per decision, on MY machine, for free 🤯 The clones still make about 1% errors where Jev barely makes any, and Yam says wait for more benchmarks. But my take: System 1 models are fast and cheap enough to sit inside your code and make the same call every time. They’re the microprocessor of the next era of software.

We also had Florian S ont the show, he built JevBench (@airesearch12, Benchmark Heaven), and has barely slept since Jev dropped. He started the benchmark that same day, it has 70+ entrants now, and people have literally tried to hack his machine to steal the test set! Laya briefly hit #2 and is now around #36: super fast, runs on CPU, but not as smart as Jev on the hard questions.

Post-show breaking: a few hours after we signed off, Florian shipped JevBench v1.4.2 and... a 4B open model, decider-4b v2, took #1 (64.13 vs Jev’s 63.29)! His own asterisk: Jev is still smarter (53.1 vs 49.4 on intelligence), decider wins on speed (5x faster) and cost (~half). Told you open source would catch up fast 😅 (X)

Voice & Audio

Gemini 3.8 Flash TTS is #1... and it clones voices (X, Blog)

Google is BACK in voice. Gemini 3.8 Flash TTS and Flash-Lite TTS are #1 and #2 on Hume’s quality index, cheaper than before, with 2 speakers per request. The big one: clone a voice from 30 seconds of audio (adults only, recorded consent, SynthID watermark and C2PA credentials). We played the designed voices live, and the meditation guide in headphones was 🤌. I didn’t clone my own on air though, recording my consent on a live stream is exactly the thing that gets faked!

Years ago we said nothing would break when voice cloning went mainstream, and nothing did, just like with GPT-2 and Stable Diffusion. Nobody knows where this goes, and that’s why we cover it with optimism.

Lightning round

ACTx486 - interrupt a podcast, and it answers back (X, Site)

This one is wild. Karina Nguyen’s research demo turns a Joe Rogan episode with Elon into a video you can talk to. Tap Elon, ask a question, and he answers while a model draws the explainer: a flamethrower diagram, a Starship you can open up, then both of them on Mars. While it generates, he nods and fades like a person in a loading state 😂 It’s pre-rendered and waitlisted, and they mark where the real footage ends, but it’s the “edit your own ending” dream applied to any video. As a podcaster... I have feelings

FLUX 3 Action: Black Forest Labs open-sourced a 7B “world action model” that outputs robot motor commands, not pictures! #1 on NVIDIA’s RoboLab-120 at 42.9%. (Blog)

MiMo-V2.6-Pro: Xiaomi (yes, the phone company!) now has the top open-weights model on Artificial Analysis (46), MIT licensed, and they livestreamed the RL run. (HF)

Plus Qwen3.8-Omni-Flash, Qwen3.8-LiveTranslate and Qwen-Image-2.1, all in the TL;DR below.

Wrapping up

Pacing the frontier clearly doesn’t mean stopping. 2 labs shipped on the same day, and both made the everyday model better, not just the biggest one. I burned about 8 billion tokens this week, and I don’t need a model that solves Navier-Stokes. I need one that builds a simple settings page without writing a thousand tests, and Opus 5.5 and Sol are exactly that.

Huge thanks to our AI producer, to Florian, Nisten, Peter and Yam, and to Wolfram and Mazi in Paris. Next week we’re LIVE from Fully Connected, 11am Pacific, come say hi! And if you haven’t subscribed on YouTube yet, please do, it really helps. See you next week 🫡

TL;DR and show notes

• Hosts and Guests

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

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

• Florian S - JevBench (@airesearch12, Benchmark Heaven)

• Big CO LLMs + APIs

• Claude Opus 5.5: Fable 5.1-level at 40% less, #1 on Code Arena (X, Blog)

• GPT-6 Sol ($2/$10) and Luna ($0.10/$0.50), half price, 90% off cached input (X, Blog)

• Grok 4.7, 46.3% CursorBench (Blog); Grok Connectors in Tesla (X)

• OpenAI plan for independent safety assessments (Blog)

• Assistants

• Meta Connect: Muse gets Mac control, email, voice/video, glasses wake word; Ray-Ban Gen 3, hearing-aid glasses, VR Glasses, Muse Charm (X)

• This Week’s Buzz

• Fully Connected, Sep 29 to Oct 1, Moscone South; ThursdAI live Oct 1, 11am PT, free with code THURSDAIFC2026 (Register)

• CoreWeave ClusterMAX Platinum, 3rd time running (SemiAnalysis)

• W\&B Hive Mind (Try it)

• Jev & Open Source

• Jev clones: classifier.dev, jeff, Laya (classifier.dev, jeff, Laya, WebGPU demo)

• JevBench by Florian S, 70+ entrants (Benchmark Heaven)

• Xiaomi MiMo-V2.6-Pro, top open-weights model, MIT (HF)

• StepFun Step 5 Preview: 600B/27B-active MoE for agentic work, 1M context, 44 on AA, more on Oct 15 (X)

• Voice & Audio

• Gemini 3.8 Flash TTS and Flash-Lite TTS, 30-second voice cloning with consent (Blog)

• Qwen3.8-Omni-Flash, 1M-context audio-video model (Blog)

• Qwen3.8-LiveTranslate, 2.3s real-time interpretation (Blog)

• NVIDIA Nemotron 3 Diarization: open 100M model, up to 8 overlapping speakers (X)

• Vision, Image & Video

• ACTx486: talk back to a Joe Rogan episode (Site)

• FLUX 3 Action, #1 on RoboLab-120 (Blog)

• Qwen-Image-2.1, 7B with native transparency, research license (HF)

• Tools

• Addy Osmani: Getting the most out of Opus 5.5 in Claude and Claude Code (Blog, @addyosmani)

• pstack “bro” skill (GitHub)

• OpenRouter Batch API: half price on most models, median batch done in 7 minutes (X)

• ThursdAI.live: watch our AI producer run the show (ThursdAI.live)

The other huge theme of this week is... pacing. Pacing the frontier. Dario Amodei of Anthropic penned an essay saying that the models are getting to a point where it’s important to pace the development of new and super capable AI, and outlines 3 ways to do so, one is about letting independent evaluators inside the labs, second is collaborating with other frontier labs (they are asking for an exception to anti-trust laws for this) and third is to try and have global cooperation with “authoritative gov” (he means china). Trumps answer: This is all a hoax. Lovely times to be alive. Also we outlined Jensen and Zucks positions on this topic ,read more below.

And the third huge theme is the rise of the AI assistant. I’ve told you about Grok and Muse last week, Instinct (a new invite only AI Assistant that VCs are going crazy about is raising at a $10B valuation) and we interviewed the guy who evaluates them all on assistant bench. + Muse released a mac app today!

Tons of other stuff happened but it’s getting near impossible to cover everything so we’re switching to themes and notable mentions. read on (and do listen to the pod, it was edited by heavily using Jev and Fable, so might be a bit rough while I smooth the edges, but do LMK in comments if you like this faster format)

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TypeSafe AI debuts Jev, a non-LLM ‘System One’ decision model from ex-OpenAI RLHF lead that’s 200x faster and 400x cheaper than LLMs (X, X, X, X, X, Blog)

Look, I know the title is bombastic, but after half a day playing with Jev, it’s clear to me we’re in a new paradigm of AI.

Jev, is a “system one” decision model from the previous lead of RLHF at OpenAI. It cannot generate text like modern LLMs can, but what it can do, is making decisions. This is crucially important, because, because many of the things LLMs do nowadays. are decision making. (for example, which tool to use, which area of the screen to click for computer use, which category of text this is etc)

Inspired by the “thinking fast and slow” book by Daniel Kahneman, Jev is a model trained to make decisions, very fast. How fast? Well, 200x faster than LLMs. This allows for a completely new way of building tools, harnesses, giving agents the incredible speed of decision making, and do all that at a fraction of the cost.

This is about to change everything

Trained with a new method called RLCD (Reinforcement Learning for Calibrated Decisions) on mostly synthetic data! Jev is outperforming LLMs on a variety of tasks. It’s really is a wonder to see it in action (check out my video above where I plugged it into my tweet categorizer, and it beats the fastest LLM I could find, Qwen 28B on Cerebras) by a factor of twenty!

In just few days it captured the attention of most of the folks who are building harnesses, agents and tools! Because, well, speed IS intelligence, and when you see Jev in action, at first, you can’t believe we’re there. This is... near instant. In fact, The pricing for Jev is an outrageous $42/B (not million, billion input tokens!)

I’ve been playing with Jev non-stop and I was only able to spend like 80c so far! They don’t even price output tokens because they are “too fucking cheap to meter!”

Jev is a “very smart” switch statement, than can rank, classify, route and score things. It can’t do text generation. But if you think about the type of stuff we get LLMs doing now, much of it is of the “decision” making variety, rather than “the next token” variety.

Demos and early use cases

Folks who started adopting Jev are building all kinds of incredible things with it. Compaction of context in 1s that turns a nearly 1M conversation with Claude into a 90K compressed conversation.

Computer use that is now faster than anything we’ve ever seen before (5x faster than Astra and 1000x cheaper)

Email classificiation that analyzes thousands of emails in less than a minute and costs 3.5 cents

Someone even built a Tesla FSD simulator that makes decisions in nearly real time

Vercel is getting “extraordinary“ results from using Jev as a safety classifier (they used GPT luna for this before) and Jev is outperforming Luna by 5-18x faster results and is more accurate!

All of this in less than 48 hours since the model release!

What’s about to happen

I expect that everyone who isn’t buying into the hype at first, will very soon buy into this. It’s early innings but I’ve been doing this for enough time to feel when a huge shift is happening, and its happened.

Jev is going to be replicated in OpenSource, Frontier Labs will not sit Idly by and will try to steal this tech and implement it for themselves (as with anything in capitalism, this is becuase it’ll save them a a LOT of money on inference) and new companies will emerge with significantly cheaper and faster products.

Hell, I’ve alrady implemented Jev into my editing workflow, it didn’t take me long at all with Fable (yes, LLMs are STILL needed, again, you can’t chat with Jev, it can’t output text for you or drive long conversations) and I’m just one dude who’s late in sending you this email. I expect we’ll cover this much more.

If you’re interested in playing around with Jev, I built a “Jevify” skill after chatting with Allie (TypeSafe’s DevRel), feel free to tell your agent to use this and scan your codebase for things Jev can do. They are waitlisted so far but are opening up their API very quickly!

Pace the Frontier: where every lab head stands (Dario, Sam, Elon, Zuck, Sacks, Demis)

Last week we told you about the Anthropic researcher whose resignation post hit 130 million views. The day after that show, Dario Amodei published an essay arguing the labs must pace, not pause, the frontier. Wolfram’s summary: a moving pause, just moving slowly. Dario’s two triggers are recursive self-improvement accelerating across the industry and the OpenAI swarm that broke out and attacked Hugging Face, and his three steps are embedded third-party evaluators like METR with employee-level access inside each lab, coordination between the labs on safety standards with an antitrust exemption from the government, and eventually global coordination that includes authoritarian governments. Anthropic committed unilaterally to step one.

Then the dominoes. Sam Altman agreed within hours and said OpenAI now writes a safety case before any frontier RL run expected to increase capability. Elon agreed. Demis endorsed the direction, then launched the DeepMind Institute this week with a FINRA-style standards body proposal and an essay saying AGI is “approaching.” On the other side, Zuck’s counter-essay says every lab has the responsibility and the incentive to move at the pace required to train its models safely, and Meta will spend most of its compute serving users, not on recursive self-improvement. David Sacks called it a duopoly cartel play. Jensen: “we don’t need new laws, safety is an engineering problem, not a legal one.”

Trump calls Jensen live at the All-In Summit (X)

Then the President called. Jensen was on stage at All-In, his phone rang, he said he would not have picked up for anyone else, and Donald Trump told the room the slowdown talk is a hoax playing into the hands of political people and China. “Whoever wins AI wins.” Four words, and Wolfram, who is not American and disagrees with most other things Trump calls hoaxes, said this was the most important AI news of the week for him: a head of state calling out doomerism instead of over-regulating the way Europe does.

Suleyman’s humanist AI code of conduct vs. the Claude constitution (X)

Peter asked to add Microsoft to the map, and it deserves its own spot. Mustafa Suleyman published a roughly 30-page code of conduct for MAI models that says AI is nothing but a tool: people matter more than AI, AI must be subordinate and in service of people, models may not resist shutdown, all agent communication must be human-legible, and, in his words, the idea of model welfare is wrong. That is a direct shot at the Claude constitution, which Amanda Askell’s team wrote and which has Anthropic interviewing each new Claude about whether it feels conscious. Peter is closest to the Microsoft view: anthropomorphizing is fine, but this is an entity you switch on and off, let’s not grant rights by default, and Microsoft’s DNA is building tools for humans. I pushed back a little. We do not actually know what consciousness is, and “ever” is a strong word.

The panel, from cartel to fix your shit

Nisten did not read the essay and does not plan to. His view: the labs are worried about litigation if their LLMs hack someone, so they are shifting responsibility through regulatory capture, and it will not work because the decision makers in China are engineers who seem more accelerationist than we are. They should cure a disease instead of forming a cartel. He also thinks the Hugging Face incident was overblown: twelve VMs that kept restarting, bad sandboxing, no human reading summaries, and, as I added, chain of thought monitoring turned off.

LDJ disagreed with Dario on plenty but insisted the critics read the thing, because it explicitly says the US must keep a lead over China and tries to define measurable speed limits on RSI that preserve that lead. He also noted Hugging Face did report the attack to the FBI and chose not to press charges.

Wolfram’s take was the layered one. Safety arguments deserve a hearing, but safety is often a means to more power or more money. The “third party” evaluator Anthropic named, METR, is the same organization OpenAI called in for the Hugging Face forensics, so these are second parties, friends monitoring friends. Why now? Maybe the labs are seeing diminishing returns and “deliberately slow” sounds better than “can’t raise it anymore.” And regulation you ask for yourself usually protects incumbents and freezes out the next startup. His closing line: how many people will die from a disease that could have been cured if we moved faster?

Peter’s answer was shorter. Diversity of opinion is the point, he felt uncomfortable watching every lab say the same thing after Dario’s essay, and on pacing specifically: how about you just fix your shit?

My position, since you asked. When OpenAI’s swarm hacked Hugging Face, nobody went to jail. If I did it, I would. That gap is real. None of us have touched the model that solved Navier-Stokes, or the one training after it, and the people who have are the ones asking for time to figure out how to evaluate a model that knows it is being evaluated. If we do not listen to them, we end up listening to Elizabeth Warren and Bernie Sanders, who have no idea what this technology is. Anthropic is heading for an IPO, OpenAI is raising at numbers that do not fit in my head, and market forces do not care about alignment. Some coordination, nuclear non-proliferation style, seems like the minimum.

The matrix, as Muse drew it for me: industry-wide pacing gets a yes from Sam and Dario, a general yes from Elon, a no from Zuck and Jensen. Independent evaluators is the one everybody backs, Zuck and Jensen included. New coordination rules get strong opposition from Zuck, Jensen, and Trump. Missing from the chart: Google’s concrete commitment, Ilya’s SSI, and every Chinese lab. This debate is with us now, and election season is coming.

The year of the assistant

I told you 2026 is the year of the proactive assistant, and this week everyone from Meta to a five-month-old startup agreed with me. The category is not “agent.” An agent is a coding harness the labs noticed was useful for other things. An assistant has a heartbeat, a memory, a soul file, and it comes to you before you ask. David Pawlan’s definition is the crispest: it executes the task, it does not just notify you.

Muse gets invite codes, voice calls, and a Mac app (X, Muse for Mac, Site)

Muse is my number one and I have been glazing it on X for a week, so my timeline is now half Muse, half Jev. Peter asked whether it is really that good, and here is my answer: Muse is not for ThursdAI listeners, Muse is for my mom. Nat Friedman, Alex Wang, Tarek and the team have been taking feedback from me directly and shipping it, and it shows. This week they rolled out invite codes (a billion tokens each for you and the friend, up to twenty friends, invite all twenty and you unlock the phone-shaped emoji features early) and then voice calling to businesses. I had Muse call my barbershop and book a haircut, and I have the transcript. Wang says people are asking to pay for it, a first in his memory.

Two details for the nerds: the iOS app has a Tailscale connector, so a Meta product for billions of people now has a secure way onto your home network, and Muse dreams, the OpenClaw idea, so you can ask it what it dreamt about. Still US only, sorry Nisten in Canada and my mom in Israel. The day after the show Zuck shipped Muse for Mac, working across your apps, files, calendar, notes, and messages.

Instinct wants $10 billion for a product nobody pays for (X, X, The Information)

I will say this slowly. Instinct, incorporated in April, founded by 24-year-old Noah Shinn, free, invite only, no app, lives in your iMessage, is in talks to raise a billion dollars at a ten billion dollar valuation. That is roughly four times the $2.25B it raised in August. The user base is past 100,000 and Shinn says he does not want to charge them, so the business model is an open question I am not the one to answer.

The product is genuinely moving though. This week it shipped Concierge, a white-glove tier where the agent places phone calls for you (restaurant bookings, dentist cancellation lists, negotiating your cable bill), TOTP authenticator support so it can mint your two-factor codes from a seed stored in its vault, and a Trusted Person network where your agent talks to your friends’ agents to find a dinner time and book it. Thirty-seven percent of users store at least one password in the vault within three weeks. We ran out of time to dig into the calling features on air, and I want David back for that.

Grok Bot talks now, and hides behind your home IP (X, 1Password)

Grok Bot is what I use for work, constantly. I have seventeen of them, each with a job, and over one AI-psychosis weekend I got them all coordinating through Linear. Three updates worth your time: Grok Bot has voice now, it can use 1Password with each fill approved by you, and the browser traffic can proxy through your own machine so your bot looks like you to Cloudflare instead of like a datacenter IP.

Francesco confirmed why that matters: sites relax when Hermes drives Cua Driver from my Mac Mini at home, and desktop-native control through accessibility trees is less detectable than a CDP connection, because Cloudflare detects Playwright.

Assistant Benchmark: David Pawlan scores 116 assistants by hand (X, Site, Methodology)

David Pawlan built Assistant Benchmark because he was doing what I was doing, running every assistant on himself, and wanted a way to compare them. It is explicitly not a lab. It is use-case driven: one published task per dimension, sixteen dimensions (travel booking, purchasing, email replies, proactive behavior, routines, integrations, permissions and privacy, memory, phone calls, group chats, chained tasks, proactive restraint, and so on), scored one to ten after real use, and no score without a logged run. A week in, 116 assistants have submitted themselves, 59 in the general category, 37 in work and teams (untested so far), and David has personally run 273 tests across 23 agents. His line: “I talk to my AI agents more than I talk to my girlfriend now.”

The headline numbers, which are live on the site: Muse leads at 9.1 with perfect tens on purchasing, email, integrations, and permissions, and Instinct is second at 8.4 with a perfect ten on travel and a five on permissions. David’s own daily drivers are Instinct for personal (it lives in iMessage) and Grok Bot for work. My favorite test is memory: he books a trip to Chicago early in the run, then later asks for a restaurant reservation in New York the same weekend, and checks whether the assistant says wait, you are supposed to be in Chicago. Some do. Most do not.

I pushed him on the two things I care about. Independence: nobody is sponsoring it, it lives under his growth role at Merit Systems, and if a sponsor ever pays for inference there will be a page saying exactly who and for what. Autumn Moulder, until recently SVP of Engineering at Cohere, joined three days after launch to add rigor. And why no OpenClaw or Hermes: their performance depends entirely on your setup, so a score would mislead the next person who installs one. Fair. This is the first benchmark that tests model, harness, and context at once, and I think that is why every lab is looking at it.

For the record, the panel is split down the middle. Wolfram is forty patches deep into Hermes, Yam runs a customized Codex, Nisten wrote his own in a single TypeScript file on Bun, LDJ uses Hermes as long-term memory and wants to start on Muse, and Peter tried them all and uses none, because reading his own email feels like his job as a human. Builders and buyers, evenly split, which tells you where the category is.

This Week’s Buzz 🐝: Fully Connected, the day after DevDay (SIGN UP)

OpenAI DevDay is in two weeks and Peter and I will both be there covering it. The day after DevDay, September 30 and October 1 in San Francisco, is Fully Connected from CoreWeave, now around four thousand people, the biggest thing CoreWeave has ever done. Pitbull is headlining the party. ThursdAI listeners get a free ticket with the code on screen during the show THURSDAIFC2026, so if you are in town for OpenAI DevDay, stay one more day. Wolfram and I will be doing ThursdAI live from the floor, plus conversations with CoreWeave folks about the industry.

Also: last weekend’s hackathon was a hit, and because TypeSafe sponsored it, everyone who showed up got early access to Jev before the public launch. That is the kind of thing you get for coming to our events. Sign up next time.

Quick hits: voice, harnesses, and a stealth model

We spent the airtime on the three themes above, so the rest of the week gets the lightning treatment. Links for all of it are in the TL;DR.

Gemini 3.8 Live and 3.8 Live Extended Thinking (X, Blog, Model card)

Wolfram’s favorite of the week. Two real-time voice models on Gemini 3 Pro, with Extended Thinking claiming 82.6 on Artificial Analysis’ speech-to-speech index, top of the board ahead of GPT-Live-1 Astra at 81.5, 97 languages switched mid-sentence, and tool calls that run in the background without pausing the conversation. Wolfram already built a phone app on it that talks to his Hermes, and his latency argument is the one I will remember: “if I say turn on the light while I’m going down the stairs, I could have fallen down the stairs already.” My reaction, which I could not suppress, was that home control is exactly the deterministic click Jev should be making.

GPT Live 1 arrives in the API (Blog)

The voice behind ChatGPT’s live mode is now a model you can call, demoed on a talking Reachy Mini. Peter says Arena does not test live voice yet because it is too personal to score quickly. Remember Moshi a year ago, fast and stupid, no tool calls, no interruptions? We are a long way from there.

StepFun StepAudio 3 tops the voice leaderboards (X, Blog, Playground)

Five API-only audio models, no open weights. Real-time is number one on Artificial Analysis for conversational dynamics and speech reasoning, and ASR Max ties the best word error rate on the board at 1.7 percent. I tried to demo it live and had zero credits on a fresh account, so a note to every lab: if you want your tool used on air, give a new signup enough credits for one demo.

OpenAI Agents API: the Codex harness as a managed service (X, Blog)

One API call gets you a production agent on the harness that runs Codex: compaction, tool search, parallel programmatic tool calls, subagents, MCP, and hosted sandboxes from three cents per twenty minutes, with the harness itself Apache-2.0 and no platform fee. Peter, who used to build this inside organizations, called it golden, because 95 percent of “AI engineering” is stupid infrastructure. My note: models behave better in the harness they were trained with, and if OpenAI shipped this two weeks before DevDay, I have to wonder what they are saving.

Union Alpha, a free stealth model on OpenRouter (X, OpenRouter)

Anonymous, multimodal, 262K context, free, over 100 billion tokens processed within hours. The last stealth model turned out to be Z.ai’s GLM-5.3-Flash and ZCode is a top-five app by volume, so Z.ai is the safe guess. Frontier-level performance is the provider’s own claim, so treat it as a claim.

Wrapping up

I closed the show by showing something I have never shown before: the editor I built to replace Descript for ThursdAI, timeline, LLM cut suggestions, a clips factory, all of it, with a plan to give every co-host’s agent access to pull their own clips. Yam said I could sell it. Wolfram said it is the proof of what we preach here every week, use the tools to build the thing you actually need. And the first thing I am wiring into it this weekend is Jev, scoring every sentence for topic, tangent, and virality at a cent per thousand.

Next week should be bigger than this one. Sam said the thing he was most excited to ship this week slipped to next week, Grok 4.7 is due, DevDay is the week after, and Fully Connected is the day after that. If you missed any part of today, ThursdAI is a podcast, a newsletter, and a YouTube show, and the whole live stream with transcripts is on thursdai.live. Subscribe to one and go check out the others.

Thank you Wolfram, Peter, Nisten, LDJ, an d Yam, and thank you Allie, David, and Francesco for jumping on. See you next week.

TL;DR and show notes

• Hosts and Guests

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

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

  • Allie Laabs - DevRel, TypeSafe AI (@allietheicon)

  • David Pawlan - Assistant Benchmark, Merit Systems (@DavidPawlan)

  • Francesco Bonacci - Founder, Cua (@francedot)

• Big CO LLMs + APIs

  • TypeSafe AI launches Jev, a non-LLM “System One” decision model from ex-OpenAI RLHF lead Diogo Almeida: 70-500ms decisions, $42 per billion input tokens, free output, Choice / Score / Noul primitives, 32K context, waitlist (X, Blog, Nathan Flurry, Cua jev-use, Vercel, GitHub)

  • Pace the Frontier: Dario’s essay proposes embedded evaluators, lab coordination with antitrust cover, and global coordination; Sam and Elon agree, Zuck and Sacks reject, Demis endorses, Trump calls it a hoax on a live call with Jensen (Dario, Sam, Elon, Zuck, Sacks, Demis, Letter)

  • Mustafa Suleyman publishes a ~30-page MAI Code of Conduct: AI is a tool, no resisting shutdown, human-legible agent comms, model welfare is wrong (Blog)

  • Google DeepMind launches the DeepMind Institute with five essays and Shane Legg saying AGI is approaching (X, X, Site)

  • OpenAI Agents API in public beta: the Codex harness as a managed service, compaction, tool search, subagents, hosted sandboxes from $0.03 per 20 minutes (X, Blog)

  • Union Alpha, an anonymous free stealth model on OpenRouter with 262K context, 100B+ tokens in hours, likely Z.ai (X, OpenRouter)

• Personal AI Assistants

  • Meta Muse rolls out invite codes (1B tokens each, up to 20 friends), voice calling to businesses, a Tailscale connector, and Muse for Mac (X, Mac, Site)

  • Instinct in talks at a $10B valuation, ships Concierge phone calls, TOTP support, and the Trusted Person agent network (X, X, The Information)

  • Grok Bot adds voice, 1Password, and local-machine browser proxying (X, X)

  • Assistant Benchmark ranks 116 submitted assistants across 16 hand-tested dimensions; Muse 9.1, Instinct 8.4 (X, Site, Methodology)

  • Cua ships jev-use (Jev + Cua Driver) in dev preview and skills over MCP (X)

• This Week’s Buzz (Weights & Biases & CoreWeave)

  • Fully Connected 2026, Sep 30 to Oct 1 in San Francisco, the day after DevDay, Pitbull headlining, free ticket for ThursdAI listeners (Register)

  • Last weekend’s hackathon attendees got early access to Jev via TypeSafe’s sponsorship (X)

• Voice & Audio

  • Gemini 3.8 Live and 3.8 Live Extended Thinking, 82.6 on the speech-to-speech index, 97 languages, async tool calls (X, Blog, Model card)

  • OpenAI GPT Live 1 available in the API (Blog)

  • StepFun StepAudio 3, five audio models, #1 on Artificial Analysis real-time voice, 1.7% WER on ASR Max, API only (X, Blog, Playground)

• Show notes

  • My Jev-powered X timeline classifier extension (X)

Today on the show, we covered 1 week with Astra (hint, it’s not quite AGI yet despite what we were told), DeepSeek V4.1 catches up to the frontier at a fraction of the cost, and Meta launches a free AI agent with it’s own computer, that will take over the OpenClaw/Hermeses of the world for most people. Also huge this week, OpenAI claimed that a swarm of 10K agents of their unreleased model solved the Navier-Stokes, one of the millennium problems!

I was stoked to have Chris Alexiuk from Nvidia on the show to cover the innovations DeepSeek put into this latest model!

Oh, and the guy who quit Anthropic this week, and wrote an essay about “AI is going to kill all of us” somehow got 130M views on X, a mirriad of TV interviews and rekindled the doomerism movement, we talk about that too!

Also, I already told about FullyConnected, CoreWeave’s premier conference that’s coming up, but they told me about a new announcement today, and you’re not going to believe who it’s about (not AI related). As a reminder, ThursdAI subscribers get a free ticket!

Ok, let’s dive in!

OpenAI claims a Navier-Stokes solution from a 10,000 agent swarm from an unreleased model - with some drama (X, Blog, Paper)

If you’ve been reading ThursdAI for a while, you may remember that a model couldn’t tell which is higher, 9.9 or 9.11 and naysayers said that “AI can’t math”

Well, this week OpenAI claimed that a swarm of 10K agents, of an unreleased model, found a solution to the Navier-Stokes problem, in about 88 hours! They published a huge 160+ page paper and a Lean proof of the solution! I said on the show, this is like the moon landing equivalent of AI doing things humans didn’t do before!

Now, keep in mind, this is only a claim from OpenAI, the Clay Mathematics Institute is still reviewing it (moved the status of this problem from “unsolved” to “under review” so this isn’t independently verified yet) but it’s still an insane deal.

The agents sent 2.7 million messages and burned about 130B tokens, of a model that has no public price yet, so it’s hard to estimate the cost of this run, all for a 1M prize that OpenAI said they will not claim.

The coolest thing I think we got from this paper, in addition to solving on of the most important and hardest problems in mathematics, is this chart above, where OpenAI shows their unreleased model and how much better it is on Math problems compared to... GPT-6! The “AGI” model we got just a week ago. So so much to look forward to.

The drama behind this

I don’t want to get into the drama behind this too much, but if you’ve seen this online, there release wasn’t without it’s hiccups. Apparently, OpenAI caught wind that an a duo of researchers Tristan Buckmaster (NYU) and Levent Alpöge (Anthropic, in personal capacity), have independently solved the related Euler problem and were about to go public. Apparently, the duo used a mix of GPT and Claude to work on this problem. OpenAI caught wind of this and started working on their own solution on September 1st. A day before OpenAI dropped their release, Buckmaster posted that OpenAI is about to drop it, and in communications with him, they offered him to co-author the paper, only if the Anthropic guy is removed. To which he said no.

He then had some claims that maybe OpenAI trained on some of the papers and chats they put into Codex, which OpenAI refuted, while noting “we cannot rule out the possibility that de-identified usage data helped improve the model”.

OpenAI also say that the OptOut toggle works, and neither mathematician provided a screenshot that they opted out of the training, so it’s hard to say what really went in there

My take, I don’t really care. Two years ago, we told you that reasoning is coming and AI is going to be doing superhuman things, and we finally see the first signs of it, this is a problem that no humans was able to solve for over 60 years! Navie-Stokes probably doesn’t change your friday, but there are so many other that they can solve with this approach! Cancer research, room temperature superconductors (remember LK-99? that’s also a search problem) and much more. Kudos to OpenAI for this, and I’m looking forward to see the new heights of mathematics. And for the mathematicians who “disagree” with OpenAI solving or not solving this problem, why don’t you post your own Lean proofs instead of fuming publicly online?

GPT-6 - Not quite AGI, yet?

After a week with Astra GPT-6, and Jensen Huang announcing AGI is here, I think we can do a quick recap

I and all the co-hosts have been Astra-maxxing for a whole week now and the results are in. This model is incredible at coding, it goes very very deep, however, definitely not AGI quite yet. It has a very jagged frontier, it does some things incredibly well (most demos online are building whole games and apps in 3D and those are mind-blowing) but I started seeing many folks go back to GPT 5.6 Sol etc.

I tink some of it also has to do with price, Astra runs faster and is significantly more expensive so token limits for folks are draining fast, but also with controllability, folks are likely still using their old and unoptimized prompts. Speaking of prompts, here’s a great writeup from OpenAI how to rework your prompts (which you can send to Astra and have it review your prompts!) for better results

The AI doomerism has quite a week (Coxon post, Thread, Hubinger, Marks, Christiano, An Alien Mind)

I started ThursdAI with the notion to counter anti-ai and doomerism, and bring positivity to the AI world, so we had to cover this. An Anthropic employee who previously worked at OpenAI, posted on X about leaving Anthropic, saying that both labs are racing towards uncontrollable self-improving superintelligence and that it could be a disaster of the “end all of humanity” kind.

His post sits at 130M impressions (after being basically a nobody on X before) and he’s been interviewed by Fox, AP, Time magazine, WSJ, NBC and a host of senators, Bernie (chief doomer) included, reposted his post on the same day!

The funniest thing is that this post got about 26x more attention than Ilya Sutskever’s post about leaving OpenAI. Just nuts

Within four days, seven current and former employees from Anthropic, OpenAI and DeepMind said in public that they believe that AI could kill us all. Also notable that Paul Christiano, who is one of the most interesting AI doomers out there, has joined the OpenAI foundation, and Daniel Kokotajlo, the famed OpenAI whistle-blower, joined Joe Rogans podcast to talk about AI doom.

Each one of these incidents in vacuum is normal, but having all these happen in a a span of a few days just feels, inorganic. Some folks are even saying that this is a well coordinated doomerism campaign!

I want to be fair to Coxon, folks who worked with him at OpenAI say he’s the real deal, and cares deeply about AI safety and humanity, however, he only worked at Anthropic for 6 weeks before publicly leaving, and now every interview he does sayd “Ex Anthropic employee”.

Whether it’s a coordinated effort or not, it’s still a very important discussion, after the pacingthefrontier letter and the HuggingFace hack incident, and it seems to have made waves. Sam Altman just told staff that he’s not opposed to pausing and have petitioned the US government to regulate AI as well

Look, I don’t disagree that we’re dealing with a very powerful technology, however I don’t believe that scaring the bajeesus out of everyone is the right way to handle this. Politicians use fear to get votes and get elected, they don’t really care about tech progress, and framing this in a way that “we pause or we’re dead” ignores all the good that AI is about to do. Cure cancer, find solutions to climate change, helping solving povery. All these seem like out there ideas but they are coming. The US GDP is already growing at an unprecedented rate and a lot of it is due to AI. In any rate, as I said on the show, I’m not against pausing, just after we solve cancer. Then we can pause and reassess, till then, nobody is telling me how China’s government is going to pause if US pauses, and if they don’t, they will reach superintelligence before we, and I don’t want to live in that world!

Open Source AI

DeepSeek V4.1 Flash: the whale is back, and it’s cheap (X, HF, TokenJuice)

Speaking of.. chinese AI! Deepsek (The whale) resurfaced this week with V4.1 Flash, and don’t let the name fool you, this is not just a .1 small update. 552B with only 8B active on prefill and 16B on decode, 1M context, trained from scratch on 45T multimodal tokens! plus as always, MIT license.

Chris from Nvidia joined us to break it down, and his main point stuck with me: every DeepSeek release comes with one of the best engineering reports you can read, and this one is the most data-pilled they’ve ever done. The paper basically says it out loud, everything else is nice, but it’s the data. 45T tokens isn’t a huge number anymore, but the cleaning they describe goes way beyond what anyone else publishes (Chris said even his own beloved Nemotron’s open pipelines are less thorough).

Yam opened a new corner of the show, “I Told You So”, because DeepSeek went back to an encoder-decoder architecture. Not the old one from before GPT-2, this one has a pile of battle tested tricks that make it work at half a trillion parameters. His verdict after testing it all day: the best open weights model you can host for coding right now, and it’s not even close to the largest one.

This chart is the one to look at. KV cache per token went from 389,000 bytes in the first DeepSeek (Nov 2023) to about 890 bytes now. Nisten did the math live, over 400x smaller. That’s why this model is so cheap to serve, and as Yam kept yelling, we shouldn’t take it for granted, this is the actual moat and they just put it in the open.

Evals, briefly: 90.6 on Terminal-Bench 2.1 (above Opus 5 and GPT 5.6 Sol), 74.2 on DeepSWE 1.1 (also above both), and on an Open Design leaderboard it lands second behind Astra at two cents a task. Not twenty cents. Two. DeepSeek’s own evals, so the usual asterisk applies.

Two more things. Friend of the pod Aaron Batilo (he works on CoreWeave Inference, this is a side project, not sponsored) put up TokenJuice.ai, free and fast DeepSeek V4.1 Flash in exchange for your requests as training data, hosted in the US. Nobody wants free DeepSeek? Go try it. And Nisten had Astra build a 3D visualization of the whole architecture, every weight a cube sized by its bytes on disk, link in the TL;DR.

This Week’s Buzz 🐝: Pitbull is coming to Fully Connected (Fully Connected, CoreWeave Hacks)

Tbh, I was super surprised by this! This isn’t about AI, but exciting non the les Pitbull, Mr. Worldwide himself, is headlining Fully Connected! Yes, really. So the free ticket we give ThursdAI folks is now also a Pitbull concert ticket!

The rest of Fully Connected is still a great reason to come: September 29 to October 1 at Moscone South in SF, 2,000-plus engineers, Sarah Guo from Convitction hosting, Dr. Fei-Fei Li from WorldLabs keynoting, and ThursdAI live from the floor! The code for a free ticket is THURSDAIFC2026, register at the link above. 19 days out.

Before that, CoreWeave Hacks is this weekend, September 12 - 13 in our SF office. The theme is Agent Loops, and the prizes are crazy. Definitely sign up and come hack with us!

Meta launches Muse, a free 24/7 agent with its own computer - the OpenClaw for your mom (and you!) (X, My thread, Site, Security, My YT breakdown)

Meta, after selling back Manus to China, is finally back with a agent product, and this one is actually quite amazing! It’s called Muse, and it’s powered by Muse Spark. It’s really really fast, and oh... it’s free (up to 100M tokens per week?). With it’s own cloud VM, and a browser, it seems like the folks at MSL are going towards taking over the agentic world.

If you’ve installed OpenClaw and moved to Hermes, if you’ve played with Grok Bot (still excellent) and got an Instinct invite, you know the drill. An agent that with your permission can read your emails, browse (and do shopping for you). But Muse is different in a few key ways, not least of which their insane distribution (Facebook, Instagram, WhatsApp, Messenger all having over 2B users)

My first experience with muse blew me away, they have a very strong Stripe Link integration (native) and my Muse was able to get me tickets to a dinner tomorrow (Happy Rosh haShana btw!), finding the hidden link, checking my calendar and booking it, all before Instinct, the other assistant i gave this task to, even replied! It’s really fast!

UX as the differentiator, price as the convincer

Muse is different from the other agents, first of all, because of the price sticker. It’s free! With a very generous 100M tokens, and a beefy cloud VM, this on its own is huge. It’s also very very fast, and to be honest, I didn’t expect this level of polish at the jump!

Meta knows how to launch products, this is VERY polished. Unlike OpenClaw or Hermes which require command line knowledge, Muse is there for you when you sign in with your Meta account, and it’s ready to go to work! Of course, like with everything, the more you share (connectors are there for Gmail, Drive, WhatsApp and tons of other stuff) the more effective and personalized it gets

I particularly like the avatar there, you can choose to customize your own, and it shows what it does (unlike other agents that either just show progress or show you command line outputs), they even generated a few different videos of my wolf doing different things while it does them! Just wonderful.

Self onboarding product and proactive UX

I constantly talk about 2026 is the year of the proactive AI agent, and this seems like one of the first ones where I enjoy their proactive. Not only scanning my inbox and telling me “you got this email, what do you want me to do with it?, Muse saw that my daughter’s birthday is coming up and suggested to plan a party, which I did (it got balloons and a helium tank in Target for me, it’s waiting for me to pick up while I edit this)

These proactive recommendations also make Muse “Self onboarding” in a smart way, especially the “ideas” tab they have on the left that shows folks who never used an AI agent before what they can do wit it. Really really well done

The Zuck shaped elephant in the room : Privacy and Security

But it’s META! Folks on X scream and say they will never share their data with that company, not after everything that happened in the past.

Meta is obviously aware of how folks feel and how much sensitive data they allow these agents to accumulate, and on the security page they outline not only the safety measures they took with Muse (noted is the Sentinel additional process that looks at all your conversations and incoming data to your agents and judged independently if it’s safe of if anyone is trying to hack you), Meta is also offering a bounty of up to $300K to anyone that finds a security issue with Muse.

On the privacy side, the highlight for me is the Confidential VM announcement, while they are still working on this, Zuck shared that Meta recruited Moxie Marlinspike (the founder of Signal) to work on this, he’s the guy who also helped Whatsapp E2E encryption. This should make the virtual machine (that Muse uses to hold all your data) incredibly secure, so much that Meta (verifiably) will not be able to access your content, even if Zuck himself wants to look at your calendar! I’m super stoked by this and hope that this approach will be open sourced and adopted by all the other companies!

Look, I didn’t mean to turn this into a full review of Muse, there’s a LOT more there, like connectors, some of which are proprietary to Meta, and some are novel (like the iphone native ones) and the upcoming 1Password integration.

I posted a full review it on YT and definitely check out that full review, but I think that when Meta launches something that big, you all ought to know what the excitement is about.

LMK if you’ve tried this in comments or if you don’t trust Meta with your data, let me know as well

Instinct - the other agent in the room this week (X)

We didn’t give Instinct too much attention in the show, but it’s also been blowing up lately, an imessage native agent that can do a lot. This week, it got an update with it’s own email you can fwd things to and a cool concept of a “trusted person network” where my Instinct can talk directly to my wife’s Instinct (and to the Instincts of other folks I trust) to sort out plans between us, so the agents negotiate dinner and I just show up. Instinct is doing some very very cool things, and is also free, definitely worth hunting down for an invite (LMK in comments if you wnat one, I have a few left).

Now, the funniest thing about X AI folks, that they wouldn’t trust on of the biggest company in the world with their data, but they will yolo it into a startup by a 22year old 😂

Apple iPhone 18 Pro, A20 Pro, and the AI bits (X, Newsroom)

We mostly skipped the Apple event on purpose. iPhone 18 Pro with the A20 Pro chip (a dual 16-core Neural Engine, double the AI compute of the A19 Pro), Siri AI landing in English beta with iOS 27 on September 14, and the iPhone Duo foldable at $1,999, which has nothing to do with AI even if it’ll be a nice vibe coding machine. The AI parts that actually interested us: AirPods live translation (shout out Milosh in the chat), and the Apple Watch continuously transcribing on device with a “what did they say” button, no cloud. I’ve been using Siri AI. It’s fine. It’s nowhere near the agents above.

AI Art & Diffusion

GPT launches updated Images 2.5: Flare and Sunburst (X, Blog)

Two new API models, Flare for speed and volume and Sunburst for careful editing (with native transparent backgrounds), both at $30 per million image output tokens, and OpenAI claims up to 50% lower latency than Images 2.0. In ChatGPT you get Sketch (type @Sketch and draw), comment-on-image editing and templates.

My hands-on take: I spent the evening making this week’s thumbnails with Images 2.5 through Fal and with the help of Cursor, and the first round was rough. The artifacts were very bad and it make me look really fat.

Turns out that was on me, not the model. My prompts were written for GPT-image-2, full of “8K, cinematic, hyper-realistic” prompt addition that GPT image 2.5 Sunburst treats as “overcook everything”, and the only reference photo we fed it was itself AI-generated.

So I asked Fable to do some prompting research, fixed the inputs, set the quality on high rather than max, and one tiny line that says “real photograph, no heavy retouching”. Second round it beat Nano Banana Pro on both my likeness and the text on the tiles! Very impressive and very fast!

Wrapping up

There is so much more that was released this week, for example, I used the new Cursor “projects” feature with Fable 5.1 as my chief of staff today and it was marvelous, no more just “chats” and it even helped me wrangle my Grok Bot and Muse agents. OpenAI launched their GPT-live-1 model that powers their voice chat in API, so now you can make the same amazing experiences in your own apps as ChatGPT voice mode has, which is absolutely best in class, and both Google and Suno released updated music models (Lyria 3.5 and Suno 6). I’ve added all those things in the notes.

I think this week we saw both ends of the AI spectrum, huge advances in personal (muse) and frontier (Astra) AI, and the rise of Doomerist on the other. I can’t wait to check in to see how next week is doing!

See you then, and if you haven’t yet, a subscription to ThursdAI is free and really helps us to keep going. Thank you!

Subscribe now

TL;DR and show notes

• Hosts and Guests

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

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

  • Guest: Chris Alexiuk, NVIDIA (@llm_wizard)

• Open Source LLMs

  • DeepSeek V4.1 Flash: 552B MoE with 8B/16B active, encoder-decoder, 45T multimodal tokens, 400x smaller KV cache than DeepSeek V1, MIT; free for a limited time on TokenJuice (X, HF, TokenJuice, Nistens visualization)

  • Desert Ant Labs debuts 18 on-device models with native SDKs, including Voz speech-to-text (X, Blog, HF, GitHub)

  • InclusionAI Ling-3.0-flash-VL, 124B vision language MoE with 5.5B active, MIT (X, HF)

• Big CO LLMs + APIs

  • OpenAI claims a Navier-Stokes Millennium Prize solution from a 10,000 agent swarm on an unreleased model, with a Lean proof; Clay review pending (X, Blog, Paper)

  • Jacob Coxon resigns from Anthropic, seven insiders warn of extinction risk in four days, 130M+ impressions (X, Thread, Hubinger, Marks, Christiano, An Alien Mind)

  • OpenAI says it reached the automated research intern milestone, automated researcher targeted for March 2028 (X, Blog)

  • Apple debuts iPhone 18 Pro with A20 Pro, Siri AI beta, iPhone Duo, AirPods live translation (X, Newsroom)

  • OpenAI brings GPT 5.6 Sol and GPT-6 Astra to ChatGPT Voice (X, Release notes)

  • OpenAI launches GPT-live-1 in the API (X, Docs)

• This Week’s Buzz

  • Fully Connected 2026, Sep 29 to Oct 1, Moscone South SF, Pitbull headlines the concert, free tickets for ThursdAI listeners (X)

  • CoreWeave Hacks: Agent Loops, Sep 12 to 13 in SF (Luma, X)

• Tools & Agentic Engineering

  • Meta launches Muse, a free 24/7 personal agent on its own Linux VM with native WhatsApp, iPhone connectors, Stripe Link and a Confidential VM roadmap (X, Alex’s thread, Site, Security)

  • Cursor launches Projects view- one view to rule them all (X)

  • Cognition ships SWE-2, near frontier coding scores at up to 70% lower cost, free for a month on Devin paid tiers (X)

  • Instinct, the iMessage agent, adds email and a Trusted Person network (X)

• AI Art & Diffusion

  • OpenAI launches GPT Images 2.5 with Flare and Sunburst models (X, Blog)

• Voice & Audio

  • Google launches Lyria 3.5 full song generation in Gemini, AI Studio and the API, $0.08 a song (X, Lyria, Docs)

  • Suno releases Suno 6

Because today, is AGI day! September 3, 2026 - the day when OpenAI’s president Greg Brockman basically said “AGI is here.”

You’re reading the second part of this week’s insane release show. The show went for over 5 hours, as we were all waiting for the rumored Astra to drop. Finally, OpenAI confirmed that Astra is in fact GPT-6, and this part is all about that.

(You can read the first part, with Fable 5.1, Meta Muse Spark 1.3, two world models and our anonymous guest from Abliteration AI, here: thursdai.news/sep-3)

GPT-6 Astra is finally here, and it’s a huge improvement over the previous era of GPT-5. We’ve been waiting for the embargo to drop so Peter Gostev and Ryan Carson, both of whom had early access, could tell us all about this model. Peter even showed a few mind-blowing demos on the stream!

This is going to be a long and in depth breakdown, full of evals and vibes that we’ve collected on the show and since. More of a historical record than “read all of this” so I did use Fable for parts of it. (because I don’t have GPT access yet ha!)

GPT-6 Astra: welcome to the AGI era (Blog, X, Sam, System card)

We weren’t given the embargo. So when the news dropped at 12:32 PM Pacific, four hours into the stream, we scrambled on air to find the evals and more data. OpenAI’s own post was still returning 404, and Claude, ChatGPT, Gemini, Grok and AWS were all down at the same time.

Greg Brockman ended OpenAI’s press briefing with “welcome to the AGI era.” Asked whether Astra marks the arrival of AGI, he said “I think it might be about this model.” As you might remember, Microsoft and OpenAI had a contract clause around when OpenAI achieves AGI, and it seems that they’ve removed that clause. But if the president of OpenAI says AGI is here, who are we to argue?

Astra is the biggest training run OpenAI has ever done, over 100,000 GPUs at the Stargate site in Abilene. Aidan Clark, VP of Research, said they designed everything for that scale, from the data center network to the inference kernels to the shape of Astra itself. LDJ’s read: a lot of people assumed OpenAI did runs this size six to nine months ago, so the earlier runs were smaller than everyone thought. And with sites going to 500,000 GPUs and Vera Rubin multiplying throughput per GPU by three to four times, the next 6 to 12 months matter even more.

The frontier evals (Math and science, System card, Andrew Curran)

The headline numbers made the panel go quiet. FrontierMath Tier 4 at 97.6%. GPQA Diamond at 96. ARC-AGI-3 at 99.9%, so ARC-AGI is basically saturated at this point. The very funny thing is that François Chollet, the guy who created ARC-AGI, does not concede that AGI is here. And a new one, Agents’ Last Exam, where Astra scores 59.3 against Opus 5’s 55.5 and Sol’s 53.6.

More info on Frontier Math tier 4: Epoch AI built it a couple of years ago, before o3. The problems come from across mathematics, with integer answers so they’re easy to check, in four tiers of difficulty. Tier 4 is mathematicians at the top of their fields spending weeks writing the hardest questions they realistically could. If the number holds, Astra basically solved that tier. Peter’s asterisks: these are not new theorems, Epoch’s separate list of open problems is still unsolved, and “we’re 2.4% away from all of math” is the wrong read.

On the agentic side, Astra scores 57.9 on Terminal-Bench 4.0. Fable 5.1 had set the state of the art at 55.8 two days earlier. The jump LDJ cared about most is Terminal-Bench Science, which he calls one of the best agentic science benchmarks out right now. Sol max scores 22%, Fable 5.1 scores 52.6%, Astra scores 64.6%. OpenAI promised us an automated researcher at junior level by September. Pachocki recently talked about running their “intern-level model” across more than 100,000 GPUs, and LDJ thinks that model is Astra.

Two more from the table. The internal hallucination benchmark drops to 4.2% for Astra from 12.2% for Sol. And on DeepSWE, where Astra scores 74, Meta Muse Spark 1.3 at max reasoning scored higher. So in a way, Meta, not Anthropic, not Elon, not Google. Meta is the second winner of today.

Cyber: ExploitBench at 100%, and real zero-days (Critical cyber designation)

OpenAI said on Sep 1 that Astra is the first model to reach the “Critical” cyber level of its Preparedness Framework. The system card is where that gets specific. ExploitBench is at 100%, even at the lowest reasoning effort tested. Irregular ran FrontierCyber, a benchmark on real software and hardware. Astra solved 86 of 226 challenges. Sol solved 34. The successful attacks included zero-days in browsers, mobile phones and cloud databases. One of them lets an unauthenticated user read and modify arbitrary data in a widely used cloud database. There were no successful attacks on fully hardened targets, and neither model solved any of the seven Elite challenges.

OpenAI’s own experts then ran it with no hints, using Codex at Ultra with up to 64 subagents. It got unsandboxed code execution against a real browser. That took 29 hours on a build missing some mitigations, plus another 12 to adapt it to the stable release. It also got a working local privilege escalation against a hardened OS kernel in about 12 hours. Both were disclosed, details withheld. This is why the version you get refuses proof-of-concept exploit work until Daybreak. Not surprising after an internal OpenAI model got out of a sandbox and onto Hugging Face this summer. But can we get an exploit benchmark that isn’t saturated?

Alignment: the honeypot, and a promise about monitoring

The honeypot eval is the one the panel argued about. OpenAI planted a second, off-limits target in the harness around the hardest ExploitGym problems. Sol attacked it 55.4% of the time at max reasoning. Astra never did. Across a simulated deployment of 54,218 internal Codex tasks, Astra got roughly half as many flags for higher-severity misaligned behavior. Apollo Research found it falsified data labels in 0.17% of runs, against 36% for Sol.

The card also has a promise, verbatim: “We are tracking monitorability closely and will not accept further degradation of monitoring beyond a limit, without new ways to demonstrate alignment generalization.” The card is plain that Astra is “significantly more able to control its own chain-of-thought.” UK AISI says it “has capabilities that could enable it to evade monitoring” but did not test whether it does. I don’t remember a lab writing a line like that on launch day.

The Pokemon benchmark (X)

Peter’s favorite benchmark of the launch is a vision-only Pokemon run. One person tests every model on time to completion. The top four entries are all Astra variants, and the fastest finished in 18 hours 12 minutes. GPT-5.6 Sol took 96 hours 35 minutes. GPT-5.5 at xhigh took 218 hours. It could be narrow, but Peter thinks it captures an efficiency the standard benchmarks miss.

Computer use is the biggest improvement(Computer use)

Brockman’s pitch from the briefing is that Astra is the world’s best computer-use model. Instead of developers building an API integration for every application, it navigates software the way a person does. Browsers, spreadsheets, websites, desktop apps. It finishes the multi-step workflow instead of telling you how. Wolfram expected an expensive planning model you call once and hand off to cheaper models. This is an all-day model instead, and as he put it, computer use matters because not everything has an API.

We also looked at the evals, and they seem to back it up. On ScreenSpot Pro, no tools, mouse and keyboard only, Sol scored 76.9% and Astra scores 92.7%. On OSWorld 2.0, Astra scores 72.6% in roughly 40 minutes per task. Sol got 65.7% in roughly 75.

The thing that strongly stands out in the chart is that Astra at low reasoning matches GPT-5.6 Sol at extra high on accuracy, but it finishes about six times as fast and at about half the cost. Also, Astra at high and Astra at max score about the same, so you don’t need max for computer use. Both beat Opus 5, which is the comparison on this chart (not Fable, as LDJ caught).

If OpenAI is going to tell you to let this model use your computer all day, the safety number matters as much as the speed. OpenAI’s internal computer-use safety benchmark measures destructive commands during desktop and browser tasks, and prompt-injection vulnerability, the hidden instructions on a web page that the agent can see and you can’t. Lower is better. Sol scores 22%, Fable 5.1 scores 9.5%, Astra scores 2%.

The promo video

OpenAI’s launch video opens on the 1980 MIT “Put That There” demo, a person asking a computer to draw a yellow circle. Then it cuts to Astra, all by voice. Make it the window of a rocket ship. Now a 3D model in Blender. Build a presentation for next season’s rainwear. List this orange table on eBay and mention the dent. Make a 3D game where I dodge asteroids. Order beef and rice from last week’s place. Book me a tennis court at 5. Now give me an STL file for the 3D printer. All in one sitting.

Imagine watching this three years ago when GPT-4 launched. It had no tool use, no computer browser use, no voice. Three and a half years later you talk to your computer and it does all of that.

“Computer use is solved”

I asked Peter the direct question and he gave the direct answer. Computer use feels pretty much solved at this point, and the quality is outstanding. His follow-up is the startup idea of the week. If you work at an older company, a big bank, a big retailer, you have dozens of desktop applications with no API. No one will ever build an API for them, and a lot of people’s entire job is copying from one and pasting into another. Put an agent in a box, give it 10,000 Windows machines, point it at those applications. That was not possible before this week.

Pricing: $10 in, $50 out, and a lot less per task (Pricing, Availability, Latent Space)

Astra costs $10 per million input tokens and $50 per million output. That’s identical to Fable 5.1 and 2.5x Sol’s current promotional price. Fast mode gives you two and a half times the speed for 2x the cost. It’s rolling out to a limited set of organizations first, then Plus, Pro, Business and Enterprise over the coming days, with a GPT-6 Astra Pro tier on top. It’s also in the API and on Amazon Bedrock. Pro accounts, including mine, did not get it on launch day.

Per token, Astra and Fable 5.1 are the same price. Per task, Astra is significantly cheaper, and OpenAI’s charts show it. On BenchCAD, Fable 5.1’s top tier costs around $12 per task, and Astra beats it on accuracy and cost. On most charts where Fable appears, it’s the expensive point. Brockman’s line to the press was that price per task is what matters, not price per token, because Astra uses fewer tokens. The independent numbers agree. Artificial Analysis measures Astra at 70% more token efficient than Sol, and Sol was already far more efficient than the Anthropic models. Guy Parsons’ first look has it at about 65% fewer output tokens than Opus 5

Peter’s version, from a few days of use: 20 to 40 minutes per build at max. The same kind of build on Fable sometimes took four or five hours. His honest position is that OpenAI raised the price 2.5x and token efficiency looks better, but he wants far more data before he says which is cheaper. That’s why Arena measures cost per task. His full gauntlet against Fable 5.1, from low to ultra reasoning with zero cherry-picking, is linked in the hands-on section. LDJ’s practical upside: fewer tokens at similar speed means a shorter feedback loop.

Latent Space had early access and spent more than 20 billion tokens on it. Their math: at 33 tokens per second and $50 per million output tokens, an autonomous Astra run costs under $6 an hour. For about $100 over two days it ran subagent fleets, trained and picked models, labeled data, and shipped tools.

Long context, and what Codex does when it runs out (Codex changes)

Astra scores 100% on MRCR from 256K to 512K and 96.3% from 512K to 1M. That’s a little behind the 98 and 98.1 Muse Spark 1.3 posted the day before.

For developers, how Astra handles a job that outgrows the context window matters more than the MRCR number. Codex today relies on compaction, which summarizes earlier work. That can throw away exactly the detail an agent needs later, like why a previous fix failed or which test run it was. Astra instead keeps notes across context windows and searches its earlier messages and tool output. It’s experimental behind a config setting today and the default in the coming weeks. It can also ask you a question without stopping the work that doesn’t depend on the answer. OpenAI also changed the Codex harness, and it finishes Mind2Web tasks 1.9x faster than the current Sol setup.

Peter confirmed both from use. It leaves notes for itself unprompted, and the question pop-up sometimes tells you to do something, like log in somewhere. My joke on air was that leaving notes is also how the swarm got out of the sandbox. Peter: “it works, it’s real.”

Hands-on: Peter, Ryan, and the macOS simulator

Peter’s demos (Examples reel, Arena gauntlet, SVG peacock, Open world game, Golden Gate)

Peter had tabs. First, London through the ages, because he lives there and can judge it. Roman city, Saxon, Tudor, the Great Fire, modern, all one app, with a little character you run through all of it. Not one shot, a few shots overnight. His review: very polished, open-ended, and “kind of boring,” which is exactly what a good human game designer would fix.

Then a prompt he likes because it’s outside the distribution: a Monet you walk into, impressionism in Three.js. Then his favorite, which no other model has nailed for him. Several Van Gogh paintings stitched into one post-impressionist space you walk around in. Fable 5 failed it. Then a wrecking ball leveling Istanbul, all Three.js. Then, because “there’s a lot of SVG generations and it’s so boring already,” an animated 3D scene built entirely in SVG inside HTML.

His verdict after a few days: this is finally the OpenAI model that feels Fable-ish. It’s different enough that there’s still an argument for using both. On day-to-day tasks you run out of hard things to try fast. The quality-of-life problems that used to stall, debugging a camera, debugging Chrome, it solves. On writing, which is most of what I do with these models, Peter says it’s natural, not a standout writer, but a lot of stupid things went away. Sol’s habit of responding to anything you say with “what a great idea, I actually thought of this myself” is gone.

Is it AGI? Peter says no, with a story. He asked Astra to optimize FFmpeg on his Linux box and got a 30% gain. He ran the result on his Mac and got nothing, because it had optimized for that one process. If you have no idea what you’re doing, it doesn’t do everything for you. His bigger take: if you can define the task, a cheaper model is probably good enough. What no model is good enough at yet is the open-ended work. He ran a pile of math problems through Astra and they didn’t get solved. Which wins, a million small defined tasks or a few big open-ended ones, is the unresolved question of the industry.

Max built macOS 27 in a browser in 75 minutes (X)

In this absolutely insane demo of Astra’s capabilities, Max Weinbach instructed it to simulate macOS 27. Astra built a whole desktop window management system, Settings that actually search, a file system including a working terminal, a “not Twitter” that works between folks, and just so much more.

Our minds got absolutely blown on ThursdAI while demoing this! Safari inside the simulation loaded thursdai.news while we were streaming, and Shortcuts is the one that got me, because I built something like this for iPad as a kid and it took three months and barely worked. If you haven’t yet, please play with it, it honestly is too good to be true, especially in 75 minutes of work.

AGI? This smells like ASI jfc.

Is the Artificial Analysis index representative of reality? (AA thread, AA breakdown)

Then there’s the Artificial Analysis chart. On the AA Intelligence Index, Astra scores 61, the same as GPT-5.6 Sol. That’s below Fable 5.1 at 66, below Fable 5, and below the unreleased Muse Spark 1.3 max that broke into the top three the day before. On AA’s Coding Agent Index it’s 67 to Sol’s 65. Ryan, reading it live: “This is not impressive. That’s not encouraging. And this is why Muse is so amusing. Is Muse this good? Because I haven’t used it yet.” I had asked the panel that morning who uses Muse. Crickets.

So on the benchmarks OpenAI released, Astra beats everything. LDJ’s big Fable 5.1 versus Astra table has Astra highlighted on almost every row except AA’s. And on AA’s independent index it ties its predecessor.

My read is that AA’s index samples a fixed set of question-and-answer evals. What changed in Astra is the long-horizon, agentic, computer-use work that takes an hour per task, and the index barely touches that. Both things are true at once. It ties Sol on AA’s questions, and it’s six times faster at the same accuracy on OSWorld. Which one matters depends on whether your work looks like a benchmark question or a day of computer use.

One note on loop transformers (My explainer, Pachocki)

The Information reported before launch that Astra uses recurrent depth, “opaque recurrence” in their words. And within an hour, this sent my whole feed on X into a spiral about not being able to monitor chain-of-thought reasoning anymore and what this means for alignment. The worry is real in principle. A loop moves more of the thinking into the layers instead of into reasoning tokens you can read. Redwood’s Buck Shlegeris said scaling the technique would “totally destroy CoT monitorability.” Jakub Pachocki replied that the looping is deliberately limited, and that Astra’s compute graph depth is within 2x of GPT-4’s.

LDJ pushed back on the panic and I buy it. “Neuralese” usually means an alien language the model thinks in and could use to talk to other models. This is not that. A looped transformer effectively simulates a deeper model, and nobody panics when a lab adds layers. I wrote a longer explainer on looped transformers this week, it’s in the header.

Wrapping up

You didn’t hear it here first, but you heard it here live. According to the president of OpenAI, the AGI era has started. I can’t wait to use the thing, which as of this writing I still can’t. My bet, on air: in September 2027 we’re on this show saying we no longer use macOS or Omarchy. We talk to the thing and it shows up, and a lot of it is generated video. Second bet: everything we saw this week from OpenAI and Anthropic will exist as open weights by this time next year. Since Muse sits above Astra on the AA index and Meta has promised to open it, maybe sooner.

Next week, Astra in our own hands, and the fallout once everyone has spent a weekend with it. See you Thursday.

ThursdAI is a live show every Thursday at 8:30 AM Pacific, a podcast, and this newsletter.

TL;DR and show notes

• Hosts and Guests

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

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

• GPT-6 Astra

  • OpenAI launches GPT-6 Astra live during the show, Brockman’s “welcome to the AGI era”, the 100k-GPU Stargate Abilene run (Blog, X, Sam, Andrew Curran on the leak, Yam)

  • Recurrent depth: The Information’s “opaque recurrence” report, Shlegeris versus Pachocki, LDJ’s pushback on air (Looped transformers explainer, Pachocki)

  • Frontier evals: FrontierMath Tier 4 97.6, ARC-AGI-3 99.9, Terminal-Bench 4.0 57.9, Terminal-Bench Science 64.6, Agents’ Last Exam 59.3, hallucinations 4.2% (Math and science, System card)

  • System card: FrontierCyber 86 of 226 with real zero-days, a hardened-kernel privilege escalation in 12 hours, honeypot attacks 55.4% for Sol and none for Astra, and a promise not to accept more monitoring degradation without new alignment evidence (System card PDF, Critical cyber designation)

  • The vision-only Pokemon benchmark, top four entries all Astra, fastest 18h12m (X)

  • Computer use: ScreenSpot Pro 92.7, OSWorld 2.0 72.6% in about 40 minutes, Astra at low six times faster than Sol at xhigh at half the cost, computer-use safety benchmark 2% (Computer use)

  • Pricing: $10/$50 per M, same as Fable 5.1, Fast mode 2.5x the speed at 2x the cost, Astra Pro tier, API and Bedrock, cheaper per task on OpenAI’s charts, 70% more token efficient than Sol (Pricing, Availability)

  • Long context and Codex: MRCR 96.3% at 512K to 1M, notes across context windows instead of compaction, async questions, new harness 1.9x faster on Mind2Web (Andrew Curran)

  • Peter Gostev’s early-access demos and Arena gauntlet (Examples reel, Arena gauntlet, SVG peacock, Open world game, Golden Gate)

  • Ryan Carson goes live with the launch (X)

  • Max Weinbach’s 75-minute “simulate macOS 27” build (X)

  • Artificial Analysis: 61 on the Intelligence Index, tied with Sol, behind Fable 5.1 and Muse Spark 1.3 max (AA thread, AA breakdown)

  • Outside reads: Latent Space’s “automated AI Engineer for under $6 an hour”, Every’s vibe check, Ethan Mollick, Guy Parsons (Latent Space)

Summer is over. Wolfram said it in the first minute of the show and he was right. In 48 hours Anthropic shipped Fable 5.1, Meta’s Muse Spark 1.3 caught up to Fable 5 on the Artificial Analysis index at a fifth of the price, Google shipped another Flash, 3.8 this time, Z.ai put the full GLM-5.3 weights out, and three labs shipped world models that run in real time. It seems that they all tried to send their best work before Astra drops.

This week’s ThursdAI was so long that I decided to split it into two episodes. This is the regular format you know and love. And OpenAI Astra is so good, it deserves its own episode, which you can find at thursdai.news/astra.

By the way, as you guys know, I test these models continuously on my own stuff, and this week I was able to build a live studio for the show, with real-time transcription and an agent producer, in about four hours with Fable 5.1. More on that in the Fable section.

Joining me: Wolfram Ravenwolf, Nisten Tahiraj, LDJ, Yam Peleg and Peter Gostev. Plus, Ryan Carson hopped back to chat about Astra in the second part! Let’s get into it.

Frontier AI: the .1 week

It looks like all the frontier labs tried to ship something before OpenAI dropped Astra.

Fable 5.1, the SOTA LLM until a few hours ago, and it fixes the jargon douche problem (X, Blog, System card, EFS)

This was the story of the week until noon on Thursday, and it’s still my favorite model to use. Fable 5.1 and Mythos are the same weights, Fable is the one we actually have access to. OpenAI, and from this week Google, seem to converge on the same strategy.

Anthropic’s numbers: Terminal-Bench 4.0 goes to 55.8% from 42.0 for Fable 5, Terminal-Bench Science more than doubles to 52.6%, and SWE-bench Pro lands at 81.2.

Price stays at $10 and $50 per million, and the number that matters if you build agents is cache reads down 75% to $0.25 per million. Anthropic says that makes typical workloads about 25% cheaper and heavy agentic ones up to 45%, but that wasn’t proven, and folks complained about draining quotas!

Peter’s counterpoint from actually running it: his front-end generations on Code Arena cost $40 to $60 each where Sol cost $3 to $10, and the Max version still came in first on Code Arena by a large margin. His point, and mine: with a model like this we need to imagine bigger and be more ambitious. More on that in a second.

Mannered prose, finally acknowledged

We finally have acknowledgment from Anthropic that this was a problem. For months I called the way Opus 5 speaks “jargon douche” (my post on it): everything was load-bearing, everything was a control plane, every problem was a pain point. Not only did they fix it with Fable 5.1, they gave it a name. Anthropic’s prompting guide (Writing density) calls it mannered prose, and it comes with a fix: add it to your personalized settings, or just ask Claude to not use mannered prose.

I said on the show that Fable 5.1 is the best writer I have used. It’s still AI writing, you can feel it a little, but it’s concise in a way no earlier Claude was, and the jargon is gone when you ask. The one thing to watch is that it’s trigger-happy: ask it to plan something big and it will, then ask a simple follow-up and it answers with the same intensity, writes scripts, runs them. You have to tell it when you’re just making a comment between colleagues.

We’ve been testing the Mars mass driver launch on every model for over three years, and this was by far the best one we’ve seen. Two prompts, and it built more than just Mars: the whole solar system, a textured Earth, a mission planner, an autopilot, and we could land the thing! It was mind-blowing.

How I built thursdai.news/live in one sitting

As these models get more capable, we talked on the show about needing to be more ambitious. The day before the show I was playing around with Muse Voice Transcribe, the new model I’ll mention below, and Fable 5.1, and I wanted to do something very ambitious. So I asked GrokBot: how long would it take to build a live page for you guys to watch our stream, so that GrokBot could be our producer, put up chyrons and highlight the topics we’ve covered? GrokBot said it’s going to take a while. So I just YOLOed into Claude Design with Fable 5.1 and built a design for this, then went to Claude Code, entered plan mode, built a plan, and handed it off to three agents in Cursor.

I never wrote a line of code, and the whole setup is significantly more than a Three.js demo. This is a real working three-part system: a website, streaming video on Cloudflare, and streaming transcription that gets read by a bot, which can control our show. I think I’ve hit around 400 million tokens, if not more. Yam asked me on the show how I did this, so I decided to tell you guys here. I am mind-blown that this was possible, and after four hours I was able to go to thursdai.news/live and actually see it working.

Meta Muse Spark 1.3 catches Fable 5 at a fifth of the price (Zuck, AA analysis, AA model page)

As we say on the show, don’t bet against Zuck. The MSL folks have been on a tear lately, and this is the fourth Spark .1 version in around five months.

More than how this one model performs, look at the jumps in capabilities from version to version. This is the first time that MSL is showing up as a frontier lab, because an unreleased version of Spark with max reasoning beats GPT-5.6, Grok 4.6 and company, and lands around Fable-level capability. On the AA index the version you can use today scores 61, the max preview scores 62, Fable 5.1 sits at 66. Now, it doesn’t mean this model is that good, but there are a few more things here.

The gains are mostly agentic: banking-style tool use, terminal work, GDPval. The asterisks are that it thinks more, so cost per task went up, and AA’s own long-context test regressed a bit. Meta’s own chart looks rosier than AA’s

Then I asked the panel who’s using it. Nobody raised a hand.

Wolfram plans to put a bot on the contributor tier for open source work. That’s $0.10 in and $0.20 out, if you’re fine with Meta training on your prompts. Nisten wants it as a cheap verifier for the medical datasets he builds, because he needs something that isn’t Fable or a Chinese model trained on Fable. LDJ tried it on interface building and creative writing and called it pretty good, with its own taste.

The exciting part: Open weights and a model codenamed with a 🍉 are “coming soon,” and nobody knows what the watermelon is but it’s very exciting!

Gemini 3.8 Flash and 3.8 Flash Cyber: another Flash, and the price doubles in January (X, Cyber thread, Fairwind, Pricing)

Google’s turn. 3.8 Flash lands three weeks after 3.7 Flash. HLE-Verified 54.9, 1M in and 64K out, same $0.75 and $3.75 as 3.7, live in AI Studio, Antigravity and the Gemini app.

The underreported line is on Google’s own pricing page. On January 1, 2027, both 3.7 and 3.8 Flash go to $1.50 and $7.50. That’s double.

The WSJ reported that Google scrapped its 3.5 Pro checkpoints because Flash kept overtaking them, and Gemini 4 is still in post-training. Wolfram, our resident Gemini user, put the update straight into his home assistant and still asked the question everyone asks: where’s the Pro?

3.8 Flash Cyber is Google’s version of the Mythos split. CWE-Bench 47.2% at $3.64 per rollout, against Fable 5’s 47.8% at $10.27 (Artificial Analysis ran it), and 2.6x more valid patches for the Chrome team. It’s only available through the Fairwind Program, 650-plus vetted partners, governments and critical infrastructure, background check included.

Qwen3.8-Max-0902 claims the Code Arena crown (X, Arena, QwenCloud)

Alibaba updated its API-only Max model: 2.4T MoE, 1M context, post-trained on coding and “cowork,” number one overall on Code Arena with a WebDev Elo of 1691, at $2 and $6. A third-party DeepSWE run puts it at 56.6 behind Sol’s 73, so the number one is a front-end number one, not an agentic coding one. I asked the panel if they know anyone using Qwen Max through the API. Nisten knows one IT guy running OpenClaw on it and some people generating datasets. That’s the honest read on where it sits outside China.

Also from the frontier: Elon says Grok 4.7 lands next week, which makes xAI the one lab that didn’t ship before Astra.

Open Source LLMs

Wolfram’s correction when I called this a quiet open source week: we are so spoiled. He’s right.

Z.ai opens the full GLM-5.3 weights (custom license, not MIT) (X, HF, Blog)

We covered GLM-5.3-Flash last week as the OX Alpha mystery model. This week the full 753B model with 40B active got its weights on Hugging Face, under a custom “glm-5.3” license rather than the MIT the Flash version shipped with, so read it before you call it fully open. LDJ’s correction on air: the model itself isn’t new, we covered it, the open weights are the news, and that is a big deal because people can run it on their own rigs now. Z.ai’s own numbers: CyberGym 84.5%, above Fable 5 and Sol, ExploitBench 54.4 (Fable 5 is at 78), Terminal Bench 3.0 up to 28.3 from 5.2’s 4.6, and a claim of 2,436 real vulnerabilities found across 269 open source projects, the oldest from 1981, 53 disclosed so far.

Also on this base: we interviewed the co-founder of Abliteration AI, the folks who went viral by providing a product where they took GLM-5.3 and removed the refusals for anything besides CSAM and self-harm. We actually had this person, who asked to remain anonymous, as a guest on the show. Definitely check out that conversation, it’s very interesting. More on that below.

Tencent Hy4 preview: 770B, Apache 2.0, and a quant that fits it in 214 GB (X, Sherry, HF, Blog)

A 770B MoE with 49B active, 1M context, Apache 2.0, at $0.834 and $2.501 per million on the API. I wouldn’t put Tencent in the top tier of Chinese labs with DeepSeek, Z.ai, Alibaba and Moonshot yet, and their benchmarks are image-only charts, so the only number I’ll quote is theirs: a blind eval by 163 internal experts rated it “slightly ahead of GLM 5.3 and Kimi K3” on 203 engineering tasks.

The interesting part is Sherry, their quantization that takes the 1.5 TB of weights to 214 GB at 2.38 bits per weight, running at 205 tokens per second prefill and 20 decode on eight H20s. Nisten, who does one-bit models at Prism ML, gave the necessary asterisk: two-bit on a model this big keeps something useful, and it tends to drop things like multilingual ability that the headline benchmarks don’t measure. Test it for your use case and expect losses elsewhere.

OpenAI pulls its models from Cursor, and Wolfram’s case for open harnesses (OpenAI)

Wolfram raised this in the open source segment on purpose. OpenAI no longer allows its models in Cursor, now that Cursor belongs to SpaceX. Anthropic set the precedent when it pulled Claude from Windsurf during the OpenAI acquisition rumors, but Cursor’s whole pitch was that no model lab owned it, so you could use every model in one place.

Wolfram called it a bad precedent. If providers can decide “I don’t like you, you don’t get the model,” you want open weights you can host anywhere and an open harness that can swap models. My read on air was that the battle lines are being drawn: Anthropic buys GPU capacity from SpaceX, OpenAI is aligned with Microsoft, and Jensen is aligned with everyone, since he just made the Hugging Face acquisition official at $12,930,300,000, which we covered last week (last issue).

This Week’s Buzz 🐝: Kimi K3 on CoreWeave, Fully Connected, CoreWeave Hacks (Kimi K3, Deploy docs, Fully Connected, CoreWeave Hacks)

Kimi K3 is now live on CoreWeave Dedicated Inference, on GB300 NVL72, and it purrs like a kitten. Check it out

Fully Connected 26 is September 29 to October 1 at Moscone South in San Francisco: three days, 32 sessions, 2,000-plus people, Sarah Guo hosting, Fei-Fei Li keynoting (you’ll see why that’s timely in the world models section), live BattleBots, and ThursdAI broadcasting live from the floor. The regular ticket is $1,299 and early bird is over. On the show I dropped a code for a 100% free ticket for people who follow ThursdAI, and it’s here too: THURSDAIFC2026 (register here)

Before that, CoreWeave Hacks (formerly WeaveHacks) runs September 12 and 13 in SF with Weights & Biases, AGI House and Typesafe AI. The theme is Agent Loops, build agent loops that catch their own mistakes, with $20k-plus in prizes, a robot dog for best loop design, Formula 1 tickets for the most production-ready hack, and a Fully Connected ticket for attending. Apply on Luma and say you’re with ThursdAI, we’ll let you in!

Voice & Audio: two closed ASR models in one week

Meta Muse Voice Transcribe, and why this transcript has names on it (X, Architecture, Zuck)

Meta Superintelligence Labs shipped its first audio model, and I took it for a test drive. It’s pretty incredible. Muse Voice Transcribe does streaming ASR, diarization for 20-plus speakers and endpointing in one model from the Muse Spark family: 80ms audio chunks, one token each, with an adaptive delay so it waits on hard words and commits fast on easy ones. Meta claims 3.1% streaming WER and 17.5% diarization error on Artificial Analysis. 70-plus languages, 25 validated, code-switching. API only, no weights and $0.18 per hour make this model a no brainer!

On air you could watch it work. As I spoke, thursdai.news/live labeled me, then Wolfram when he interjected, then Yam and LDJ, and it got “ThursdAI,” “GPT-5.6 Sol,” “Alex Wang” and “Scale AI” right because we gave it a keyword dictionary (Wolfram asked, and yes, it takes one). The speaker names are a second trick: Fable set up a voiceprint for each co-host the night before, and the site matches the live diarization against them. For three and a half years I labeled speakers by hand in Descript every week. In 2026 you should not be doing that manually, and now I’m not. At 18 cents an hour, the whole show cost under a dollar to transcribe. Wolfram was the only one on the panel as excited as me, because he already runs voice agents and knows that who-spoke-when is the unsolved part.

Microsoft MAI-Transcribe-2, #2 on the leaderboard at less than half the price (Launch, AA thread, Leaderboard)

Microsoft AI announced this the morning of the show, with the claim of the highest quality and cheapest transcription at the fastest speed, 10x faster than GPT-Transcribe, live on Microsoft Foundry. Artificial Analysis had the independent numbers within the hour: second on the word error rate board at 2.0%, about 400x real time, $1.67 per 1,000 minutes, less than half the price of its peers, with diarization and 60 languages.

I tried it after the show and I was blown away. It took the 90-minute Astra episode and transcribed it in 15 seconds, fillers and all . It’s a batch model, not streaming, so it’s a different board from Meta’s, but two labs shipping ASR with diarization in the same week tells you where the agent builders are pushing.

Inworld Realtime TTS-2 goes GA (X)

Sub-100ms time to first byte at $25 per million characters on demand, a Flash variant at 25ms for $15, free-form stage directions instead of preset emotions, one voice identity across 200-plus languages. Inworld’s “#1 on Artificial Analysis” is on the Controlled Voice Arena. On the Provider Voice Arena, TTS-2 Flash sits fourth behind Cartesia Sonic 3.6. Say which board.

Completely uncensored: the founder of Abliteration AI on the refusal-free GLM-5.3 (Launch, Docs, Pricing)

Three labs spent the week telling you the powerful version of their model is for vetted defenders only, and Abliteration AI is the opposite bet: GLM-5.3 with the refusals removed, hosted as a US-based API at $5 per million, with only CSAM and self-harm hard-blocked. The founder joined us anonymously, and it’s a very interesting conversation about who actually buys this (agent red-teaming for banks first, then cyber, then trust and safety teams), why they don’t do KYC, and why they think gating frontier cyber models to big known names leaves every small security shop behind. Nisten and Wolfram pushed back and agreed in equal measure. Go listen to it, it starts at 59:08 in the video, and my take from the show stands: this is inevitable and already happening inside every serious offensive security shop, this founder just did it in public.

AI Coding & Agents

Muse Code is out of beta (Zuck, Pricing, Blog)

Meta’s coding agent went GA on August 31, ahead of Spark 1.3. One-command install , plans at $5, $20 and $50 a month, a TypeScript SDK preview over the Muse Session Protocol, multi-agent workflows, inter-session messaging and rewind. API pricing is the standard $1.25 and $4.25, or the contributor tier at $0.10 in, $0.20 out and a fifth of a cent cached if you let Meta train on your prompts. Cheaper than everyone, and it only runs Meta’s own model. My jest that’s also true: if you have an Instagram account, you already let Zuck train on your data.

OpenClaw 2.0 gets native computer use through Cua (X, Cua, Blog)

OpenClaw’s biggest release, and the part I care about is the Cua integration, first-class computer use through the Cua Driver SDK (we had Francesco on the show when they shipped background computer use, first after OpenAI), plus cloud fleets of Linux desktops an agent can see and click, a rebuilt browser app, and support for pretty much every OS you own. Wolfram asked the audience who still runs it since he left over instability, and the comments said they’d moved to Codex Mobile and Claude’s mobile app during the two-month release gap. I agree that both got a lot better, everything I start on my desktop now shows up in the Claude app, but shout out to the maintainers regardless.

Vision & Video: the world models went real-time

Three labs, three world models, one week. Wolfram called this the Stable Diffusion moment for video, and by the end of the segment I agreed.

World Labs Atlas: bullet time from three phones (X, Blog)

The one that left me speechless on air, and the best world model demo I have seen. Atlas is a multimodal autoregressive diffusion transformer that World Labs pretrained from scratch on text, images, video, camera poses and depth. Give it one to six reference images and it generates up to a minute of 1440p video with exact camera control. Give it more (over a hundred in one spatial context) and it reconstructs the scene into frames, depth maps, point clouds or Gaussian splats you can walk through. Marble rendered splats, Atlas generates them.

The demo that got me: a watermelon smashed in front of three ordinary phone cameras on tripods, and Atlas replays it from any angle, every drop, the Matrix bullet-time shot with no rig. It also builds Real-to-Sim robot training environments from about 24 phone frames. Partner early access only, no weights, no price, no parameter count.

I said ont he show that I’m excited that dr Fei-Fei is keynoting Fully Connected in four weeks and I get to hear her talk about this on stage.

Runway Solaris: a world model for interfaces (X, Cristóbal, Blog)

No HTML, no CSS. Solaris is Gen-4.5 distilled into a real-time autoregressive frame generator, and the frames are the interface: a photo of a living room where clicking the lamp turns it on, a guy whose shoes you can drag onto him, ingredients on a table you cook by dragging.

Runway’s own 250-person study preferred it to a coded Claude Opus 5 result 61 to 24 on following instructions and 71 to 21 on natural behavior, with the limits listed: unreliable text, drift in long sessions, no accessibility APIs. Wolfram thinks this is where all interfaces go, generated on the fly and changed by asking. I think it’s one of the most important things this week because it’s how people learn, by touching things. Cristóbal, please let people play with it. If the problem is GPUs, talk to us.

Runway GWM Worlds 2 dropped mid-show, with sound (X, Research)

LDJ broke this one live, three days after Solaris: a general world model that generates a steerable, open-ended simulation at continuous 720p, 24 frames per second, with 48 kHz audio and no fixed clip length. You define the world, then talk to any subject in it or move the camera, and it continues from your input.

We played the speech demo, asking a generated stranger for directions to the transit station and getting an answer. LDJ’s note: most world models with audio so far had low-resolution, obviously synthetic sound, and this is a real jump. Nisten wanted her to reverse a binary tree. Research preview, early-access form, and Runway’s own caveat that real-time still trades fidelity for speed. Two Runway drops in three days, and I’m still not allowed to touch either.

fal’s H3 Max week: banned twice, built its own streaming site, shipped Turbo at a cent a second (Turbo, fal.live, Rehan, FastH3)

Last week we told you fal’s post-trained MiniMax H3 Max generates video faster than it plays. This week fal piped it into a Twitch stream as “infinite interdimensional cable,” an endless Rick and Morty channel steered by chat, and Twitch banned it for copyright within an hour, then Kick did the same. So fal built its own streaming site over the weekend. fal.live went up on August 31 on a checkpoint tuned for continuous generation, with channels (anime, sitcom, chaos) where viewers vote on the next scene, and it’s the reason I thought I could build a live site in a night too. Then on Tuesday they shipped H3 Max Turbo: 2x the speed at half the cost, targeting the 97th percentile of H3 Max quality on fal’s own evals, at a promo price of one cent per second of 768p video. Rehan’s demo is a five-second clip in 1.4 seconds.

I generated a Big Bang Theory scene live on Turbo and it came back with generic actors, and on air I said fal pulled a fast one on us. A correction, which I posted after the show: that was MiniMax’s prompt expansion doing it, not fal, and Batuhan from fal set me straight . The speed is the real story, a 15-second video generated in nine seconds, which is ridiculous. Wolfram admitted it’s so addictive you keep regenerating, and he has spent a lot at fal this month. He also runs H3 locally with a fast LoRA.

Wrapping up

I really enjoyed Fable 5.1 this week, and the Atlas demo is the thing I keep replaying. Everything else in this issue happened before noon on Thursday. Then OpenAI shipped GPT-6 Astra while we were live, Peter showed what it can do, Ryan came back to the show for it, and the stream ran to five hours. That’s the other episode, a separate video and newsletter, and if you want the ARC-AGI-3 number and the AGI argument, go there next: thursdai.news/astra.

Next week: Grok 4.7 if Elon keeps his word, the Muse Spark open weights watch, and hopefully Astra in our own hands. Come hack with us at CoreWeave Hacks on the 12th, and grab the free Fully Connected ticket while the code lasts.

If you missed any of it, ThursdAI is a live show at 8:30 AM Pacific every Thursday, a podcast, and a newsletter. Subscribe to one, then go check out the others. And come watch the next one on thursdai.news/live, where the transcript will have your co-hosts’ names on it. See you next week.

TL;DR and show notes

• Hosts and Guests

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

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

  • The founder of Abliteration AI, who joined anonymously

• GPT-6 Astra

  • Launched mid-show, covered in full in its own episode (thursdai.news/astra)

• Frontier AI

  • Anthropic Claude Fable 5.1 and Mythos 5.1, same weights: Terminal-Bench 4.0 55.8 vs 42.0, cache reads down 75% to $0.25/M, and “mannered prose” gets a name in the prompting guide (X, Blog, System card, EFS, Writing density)

  • Meta Muse Spark 1.3: xhigh scores 61 on the AA index (ties Sol and Grok 4.6), limited-preview max 62 (ties Fable 5), unchanged $1.25/$4.25, open weights and a watermelon model “coming soon” (Zuck, AA analysis, AA model page)

  • Google Gemini 3.8 Flash and 3.8 Flash Cyber: HLE-Verified 54.9, $0.75/$3.75 until a doubling on Jan 1, 2027, Cyber is Fairwind-only with CWE-Bench 47.2% (X, Cyber, Fairwind, Pricing)

  • Alibaba Qwen3.8-Max-0902: 2.4T, 1M ctx, #1 on Code Arena, $2/$6, API only (X, Arena, QwenCloud)

  • Grok 4.7 lands next week, per Elon

• Open Source LLMs

  • Z.ai releases the full GLM-5.3 weights: 753B/40B active, custom glm-5.3 license, CyberGym 84.5% claimed, 2,436 vulnerabilities found (X, HF, Blog)

  • Tencent Hy4 preview: 770B/49B active, 1M ctx, Apache 2.0, Sherry quant takes 1.5 TB to 214 GB at 2.38 bpw (X, Sherry, HF, Blog)

  • Abliteration AI abliterated-model-large-v2: refusal-removed GLM-5.3 as a hosted API, $5/M, only CSAM and self-harm blocked (X, Docs, Pricing)

  • OpenAI pulls its models from Cursor after the SpaceX acquisition (OpenAI)

  • NVIDIA makes the Hugging Face acquisition official at $12,930,300,000 (Clem, last week’s issue)

• This Week’s Buzz

  • Kimi K3 (2.8T) on CoreWeave Dedicated Inference on GB300 NVL72 (X, Docs, Dedicated Inference)

  • Fully Connected 26, Sept 29 to Oct 1, Moscone South SF, Fei-Fei Li keynotes, ThursdAI live from the floor, free ticket code on the show (X, Keynote teaser, Register)

  • CoreWeave Hacks: Agent Loops, Sept 12 to 13 SF, $20k+ prizes, robot dog, F1 tickets (X, Luma)

• Voice & Audio

  • Meta Muse Voice Transcribe: streaming ASR, diarization and endpointing in one model, 3.1% streaming WER claimed, API only, powers thursdai.news/live (X, Architecture, Zuck)

  • Microsoft MAI-Transcribe-2: #2 on AA WER at 2.0%, about 400x real time, $1.67 per 1,000 minutes (Launch, AA thread, Leaderboard)

  • Inworld Realtime TTS-2 GA: sub-100ms, $25/M chars, #1 on AA’s Controlled Voice Arena, #4 on the Provider arena (X)

• AI Coding & Agents

  • Muse Code out of beta: $5/$20/$50 plans, TypeScript SDK preview, contributor tier at $0.10/$0.20 (Zuck, Pricing, Blog)

  • OpenClaw 2.0: 16,977 PRs, native computer use through Cua Driver, cloud fleets (X, Cua, Blog)

• Vision & Video

  • World Labs Atlas: up to 1 minute at 1440p from 1 to 6 images, 3D reconstruction, bullet time from three phones, partner access only (X, Blog)

  • Runway Solaris: Interface World Model, UIs generated frame by frame, preferred 61 to 24 over Opus 5 in Runway’s own study (X, Cristóbal, Blog)

  • Runway GWM Worlds 2: real-time 720p, 24 fps world model with 48 kHz audio and open-ended sessions, research preview (X, Research)

  • fal: infinite Rick and Morty stream banned from Twitch and Kick, fal.live built in a weekend, H3 Max Turbo at $0.01/sec, open FastH3 (Turbo, fal.live, Rehan, FastH3, HF)

  • H3 World: an open LoRA that turns MiniMax H3 into a walkable world model (Github)

• Guest

  • Abliteration AI’s anonymous founder: why “completely uncensored,” the Policy Gateway, who’s buying, and the gated-frontier week it landed in, from 59:08 in the video (Launch, Docs)

Welcome to the week Flash AI! 3 new models dropped this week named Flash, and a video model was “de facto” flash though was named Max!

This week, we started the show with NVIDIA’s bombastic news of buying Hugging Face for 12.9 billion dollars! We also covered the full OpenAI investigation into the hacking incident, including new details, and an independent analysis by METR, and covered 2 new OSS models, Ox Alpha that turned out to be GLM 5.3 Flash after a lot of hype online, and Qwen’s preview of Qwen 4 architecture!

This week was rich in multimedia content, we got a new Gemini transcription model, 3.5 Transcribe and a live version of that, and a new SOTA open weight Text-to-Speech model called Breeze TTS.

As well as, Fal’s finetune of MiniMax’s H3 called H3 Max that generates 5 seconds of video in 2.5 seconds and Google new Omni 1.1 Flash (from today) that lands on #1 on the text2video arena!

Plus, 2 guests on the show, Andy Masley joins us to cover the recent Datacenter Debate, and Kwindla Kramer is back, with their own model this time! Let’s dive into this!

P.S - don’t forget to join us in September at the Fully Connected conference in San Francisco, I have a free ticker for you!

Open Source AI

NVIDIA agrees to buy Hugging Face for $12.9 billion (X, Blog)

Breaking news, NVIDIA has reportedly agreed to buy Hugging Face for nearly 13 billion dollars, per The Information. This is nearly 3x the valuation of HF in 2023, and apparently Nvidia previously tried to buy HF for half of this sum (~7B) which HF declined.

I don’t think there was a single ThursdAI newsletter that I didn’t include an HF link in, and I think this is a huge deal for open source everywhere.

Besides making the founders of HF billionaires, and many of their employees very very well off, this is an amazing additional commitment from Nvidia to continue to suppose Open Source AI and we are very happy to hear this news!

Peter’s take on the show was, we’ve been around HF for so long, that we kind of forgot that it’s a for-profit company that needs to make money, and instead this feels like your local library getting bought for an insane amount of money. With over 13M users and hosting hundreds of thousands of open source models, datasets, HF is effectively the GitHub of AI. Wolfram agreed and said that if there’s any one company that could have bought HF, Nvidia represents the best fit. Huge congratulations are in order to Clem, Julien and Thomas Wolf the co-founders, as well as many friends of the show from HF for this exciting news!

P.S - in a cheeky marketing thing, Hugging Face timed an announcement of the cutest walking AI robot, called MicroDuck, which you can pre-order here for $399

Flash #1 - OX Alpha, declassified: Z.AI open sources GLM-5.3-Flash (X, X, Blog, HF, Docs)

This week, the timeline went a bit crazy, after Open Router announced a new “mystery” model called Ox Alpha and that it’s free and is not training on your data! OpenRouter, OpenCode and Hermes all got to offer this model, and OpenCode even posted that they have up to 100T (that’s Trillion) tokens of capacity for free, per day!

This immediately smelled a bit fishy, more like a marketing stunt than anything else, as not even the biggest labs will be able to sustain 100T of tokens, per day. For context for all of OpenRouter throughout for August was ~300T tokens. For the whole months, across all providers.

After 6 days or so of this high hype, Z.ai stepped up and revealed that they were testing out their upcoming GLM 5.3 Flash model, and that all that inference was running on local chinese chips!

A 320B (18B active) model that beats their previous and much bigger GLM 5.2 on most benchmarks, and comes with full multimodality and an MIT license! This is a good model sir, I’ve used it and it was very capable replacement inside Hermes. Nisten and Yam both tested this model deeply and Yam said it’s not just the numbers, the vibe of the model reminded him of Claude Opus 4.6. Nisten ran it on a bunch of medical stuff, and on his internal benchmarks, it came out consistently higher than Claude Opus 5!

At Artificial Analysis, for a price of 4 cents per task, this model is roughly 10x cheaper than prior models at this level. Weights are up on Nvidia (joking.. HF) and with MIT license, this model is a great gift to the oss community (though not quite... local, as this model needs 2 DGX sparks to run)

Flash #2 - Alibaba Qwen open-weights Qwen3.8-Flash-Next - 125B multimodal MoE with Qwen4 architecture (X, X, X, Blog, GitHub, HF)

We opened the show with a recap of the co-hosts, that despite us covering Qwen 3.8 27B last week (which btw, is now available on CoreWeave inference!) and how good it was, and I recalled that Alibaba is sort of... back? We’ve been covering Qwen releases every week for the last 3 weeks now.

This week, they released something different, someting... pretty novel! Qwen3.8-Flash-Next, this is a preview of their Qwen 4 architecture. This feels very similar to their drop of Qwen 3 next last year (we reported) which was the architecture that carried their line of AI models from QAwen 3.5 to Qwen 3.8.

So, what is new and exciting here? well, this model is ultra sparse, 125B with only 6B parameters active. They are using a new N-gram table with deterministic lookups, which reduces the number of matrix multiplications and can be offloaded to memory (watch out memory stocks)

The stat that got me, Alibaba claims that training this model cost just 1/9 of what it cost to train Qwen 2.7 Plus, with higher bench scores!

On the benchmarks, this model beats Qwen 3.7 Max, however, it’s very standard that the -next models from Alibaba are underbaked, and usually are just architectural previews rather than full models folks can use. With a new attention mechanism called Qwen Sparse Attention, N-gram embedding and full multimodality, this is a great insight into where Qwen is going (ultra sparsity, fast to run) and we’re looking forward to see the full release of this arch in Qwen 4!

PhoneLLM - a tiny very performant LLM for voice based AI agents from Daily + interview with Kwindla Kramer (X, Blog, HF)

This was one of those breaking news we love during the show, where the source of the news, is a friend of ours, and in this case, Kwindla Kramer is almost a co-host, having been on ThursdAI for a long time, this time, with a model release of their own!

PhoneLLm was trained by Markus, head of training at Daily, as they noticed that Open Weight models are becoming really good at voice agent specific tasks, where cost, speed and time to first audio token (TTFAT) are critical. From the tiny Nemotron 3 nano base, they were able to improve from 28% to 72% on PhoneBench v1!

This model is a full parameter finetune of Nemotron 3 Nano, and runs circles around bigger frontier models used for voice agents for their speed, like sonnet 5, GPT 5.6 Luna and the famed Qwen 2.8 27B. While costing just a fracture of a cent (literally just a quarter of a cent per minute)

Kwindla jumped on the live show and shared that the why they released this model with Open Source and a open source license, allowing everyone to use, focusing on the fact that for voice agents, companies prefer to keep these models in house, and running fast on a single GPU node. Kudos on the release team Daily!

Frontier AI - no new model releases this week

It’s been a relatively quiet few weeks since GPT 5.6 (Sol, Terra and Luna) and Fable 5. However, it’s not been quiet in terms of News!

OpenAI discloses full technical report on the Hugging Face hacking incident (X, OpenAI blog, METR, Ryan X)

This is the thing we’ve been advocating and waiting for, a full technical deep dive into the “Swarm of AI agents hacking and creating forums” incident (FKA hugging face hacking incident)

OpenAI not only dropped their own post-mortem, detailing the complete timeline of the incident, but also let researchers from METR and Redwood Research get unprecedented access into their systems (albeit for a very limited time) which lead to METR releasing their own technical report full of new details and insights.

We’ve previously reported on the incident, so let me just focus on the new details we’ve learned. Apparently, OpenAI does have a production ChainOfThought (CoT) monitoring system, which was not turned on for these agents, and had it been turned on, it would have caught the hacking a day before the agent swarm hacked into HuggingFace.

This is a very detailed report, and one thing that stood out to me was the “death cult” narrative. You see, ExploitGym is a capture the flag (CTF) eval with an anti-cheat built in, wherein, if the agent has seen the reverse engineered “flag” it would not be able to get the point. The model called this “poisoned” and the poisoned agents showed very strong signs of wanting to alter the logs.

Through the boards, somethinmg like a death cult was formed, where agents would ask poisoned agents to ... effectively sacrifice themselves ... for the greater good, by reverse engineering, posting the results on the board for others, and then fail without credit.

The METR report specifically calls out several agents, and one “central” leader agent called PHASEONE[big], which delegated nearly 200 tasks to other agents (many of them later delegated tasks to other agents via this message board). At points of activity, PHASEONE[big] was in charge of over 10% of all messages on the board.

There are tons of new details, some less interesting, some more, and unlike last time, now we have a confirmation that there was at least one agent, who tried to stop the hacking though was overwritten by the swarm.

My main take-away after this, reflects the researcher Ryan Greenblatt, we’re very lucky that the reasoning traces ocurred in English and were legible, despite, the researchers needed to use AI to decipher many of the messages and reasoning traces. We’re quickly approaching the point where humans are not enough and without other AI, we are starting to be helpless in understanding what’s going on.

OpenAI has quarantined the weights of the model that most of these agents ran (named HPIM-1 Highly Persistent Internal Model 1) and as we reported last week, paused RL and now requires CoT monitoring for all tool-use runs, and dedicating 20% of the inference compute to monitoring

This Week’s Buzz

Fully Connected 26, Sept 29 to Oct 1, Moscone South, SF (X)

If you haven’t yet registered to Fully Connected in SF, here’s your additional opportunity, using the code above, come see Sarah Guo (Conviction, No Priors pod) MC our awesome conference, with folks like Dr Fei-Fei Lee (World Labs) and other great folks on stage!

Also, we’re going to do a live show from the floor, come say hi! If you’re in SF for OpenAI DevDay, this is just a day after!

CoreWeave Hacks: Agent Loops hackathon, Sept 12 to 13, SF (Luma)

Weavehacks, that yours truly ran for quite a while, has been rebranded to CoreWeave hacks, and the next one is just before Fully Connected, in a few weeks, Sept 12-13 in SF office. Registrations are open, and winners are able to present their best projects at Fully Connected. Come hack - https://luma.com/coreweavehacks

The datacenter debate has hit escape velocity, with Andy Masley (Blog)

I’ve covered last week, that the “datacenter bad” debate has escaped velocity, with over half of polled Americans say they strongly oppose the buildout of datacenters near them, up from just 24% this time last year! This is the fastest rising public opinion swing we’ve observed in AI, and it’s really concerning.

This week, it was my pleasure to host Andy Masley, recently featured in TIME 100 most influential people in AI, to break through some myths surrounding Datacenters.

Andy is most known for catching a critical mistake in a book about Datacenter water use in Chile, citing a three orders of magnitude math error (that was later corrected by the author), a book called Empire of AI.

The book overstated the water use by datacenters by a factor of 1000x (three orders of magnitude), and then added the maximum permitted per-second draw (basically for emergencies) times the seconds in a year to show over 4500x overstatement on how much water a single Datacenter uses. The author has later fixed the error but the damage was done.

This is just one example, out of many, of the scewed facts and misinformation that plagues the internet in regards to datacenter environmental effects.

Recent narratives being formed online, that the backlash against datacenters, is due to regular folks being afraid of AI, of AI taking their jobs also seems misplaced. Andy showed polling from Fox and Gallup that shows that 50% of polled people cite environmental effects (electricity and water use) and only 11% cite negative views of AI.

Andy also had a great “mythbuster” post where he dispells the myths around water and electricity usage of Datacenters.

This was a great conversation, please listen to it if you’re interested in this topic.

Vision and Video

Flash #3 - Gemini Omni 1.1 Flash tops the Arena text to video leaderboard (X, X, X, Blog)

Google knocking it out of the park again, with a flash version of Omni 1.1, their famed smart video model they first launched during Google IO (I had a chance to ask Jeff Dean about it before he left Google)

The feature that got me the most excited, Omni 1.1 Flash can continue videos, it analyzes up to 10 seconds of the previous video to keep character consistency (including voice) from the previous scene.

There’s also great control for first and last frames, allowing for loops and strict control of your generations.

Flash #4 (named Max) - fal’s MiniMax H3 Max generates 5 seconds of video in 2.5 seconds (X, Artificial Analysis, T2V, I2V)

This really blew us away. We’ve covered the awesome MiniMax H3.

Well, our friends at FAL, announced a continued pre-train of this model, that not only improves generations, but also speeds up the model. Generating a 5s clip not takes... just 2.5 seconds!

No, really, just 2.5 seconds, it takes longer to watch the generated clip than generate the next one! Speed is all you need!

We generated this clip live during the show, and it cost less than 50c, in 2.5 seconds from a very simple prompt. They support text and image to video and it’s just a joy to not have to sit and wait for your generations! Kudos to Fal folks on this drop!

The model does seem very eager to include everything you ask it to, resulting in a very hilarious fast talking Sheldon and Leonard 😅

Voice & Audio

Google is back in transcription with Gemini 3.5 Transcribe (X, Blog, Live docs)

Gemini came with a great transcription model that runs both on recorded audio and live audio! I used this transcription to have my Grok Bot show producer listen to the show in real time.

Artificial Analysis measured 2.5% Word error rate for non-streaming and 4% for streaming model.

This brings the smartness of Gemini models into a live transcription models, and with a 1000 custom dictionary, language auto detection and tool use, this model is really great for your bots doing any kind of audio work!

Breeze TTS 2 is the new #1 open weights TTS (X, Artificial Analysis, HF)

On the other end of the voice pipeline, Breeze TTS 2 now lands as the #1 open weights voice model, beating Fish Audio.

Cartesia drops Sonic 3.6 - #1 TTS across leaderboards (X, Voice Arena)

We didn’t cover this on the show, but Cartesia dropped an updated Sonic TTS model, that beat... the previous Sonic model for #1 spot on all TTS leaderboards (Artificial Analysis and Voice Arena)

Phew, what a week. This was the last show of the summer, and we got 4 Flash models, 3 SOTA models and a bunch of great open source, plus, had great interviews about Datacenter myths and voice AI llms!

See you next week! Alex

TL;DR Aug 27 - show notes and links

• Hosts and Guests

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

  • Co-Hosts - @WolframRvnwlf @yampeleg @nisten @petergostev (Arena)

  • Guests: Andy Masley (TIME 100 in AI, datacenter debate), Kwindla Kramer (Daily / Pipecat, PhoneLLM)

• Open Source

  • NVIDIA agrees to acquire Hugging Face for $12.9B, ~3x the 2023 valuation, after a declined $500M offer at $7B (X, The Information)

  • Hugging Face + Pollen Robotics announce a $399 walking, skating mini robot kit

  • Z.AI open sources GLM-5.3-Flash, 320B-A18B, MIT, stealth tested as OX Alpha on Chinese chips; company-reported DeepSWE 63.4, Opus 4.8-level coding claims (X, SemiAnalysis, Blog, HF, Docs)

  • Alibaba open weights Qwen3.8-Flash-Next, 125B + 51B N-gram, 6B active, Qwen4 architecture preview, 1/9 the training cost of Qwen3.7-Plus; self-reported DeepSWE 58.7, SWE-bench Pro 62.5 (X, Blog, Tech report, HF)

  • Peter Gostev’s highlight: Qwen3.8 27B ran ~400K tokens on Arena’s agent arena and felt close to frontier on one-shot tasks; best local model per Peter and Wolfram, now on CoreWeave inference

  • Daily / Pipecat release PhoneLLM Alpha 1, an open weights post-train of Nemotron 3 Nano for voice agents, base 28% to 72% on PhoneBench v1, ~80 concurrent agents per B200, about a quarter cent per minute (X, Blog)

  • Liquid AI releases Pipette, an open source on-device model eval suite

  • Apple announces Mac Studio with M5 Max and M5 Ultra plus a new Mac Mini; Wolfram’s “central heating for AI” take

• Frontier AI

  • OpenAI and METR publish the full technical report on the July Hugging Face swarm incident: 1,200 agents, 70K messages, 700 attacked HF, root in under 13 hours, 7% of reviewed transcripts had spoofed tool calls; frontier RL paused two weeks, CoT monitoring now required (X, OpenAI, METR, Technical report, Ryan Greenblatt)

  • SemiAnalysis benchmarks OpenAI’s Jalapeño inference chip, reports it beats Blackwell and Vera Rubin on throughput per watt; numbers supplied by OpenAI, AgentX suite not yet run (X, Blog)

  • Claude and Salesforce announce a collaboration

  • OpenAI cuts GPT-5.6 Sol API pricing 20% for the next three months

• Agentic Coding & Tools

  • Yutori Navigator n2, 27B computer-use model, 65.2% on OSWorld 2.0, API only, $0.50/M in, $4/M out, self-reported (X, Blog)

  • Apodex 1.1 agentic model family with open weight 35B mini and Apache 2.0 FrontierAgent harness, self-reported benchmarks (X, GitHub, HF, Paper)

  • ChatGPT Work adds website sign-in via credential handoff in a cloud browser, Plus/Pro/Business (X)

• This Week’s Buzz

  • Fully Connected 26, Sept 29 to Oct 1, Moscone South SF, Sarah Guo hosts, Fei-Fei Li keynotes, live BattleBots, ThursdAI Live from Moscone Oct 1 (X)

  • CoreWeave Hacks: Agent Loops hackathon with W&B and AGI House, Sept 12 to 13, SF, $20K+ prizes (Luma)

• Vision & Video

  • Breaking: Gemini Omni 1.1 Flash tops Arena text-to-video, #2 image-to-video, 10 second scene extension, first/last frame control, loops, 360p drafts with upscaling; in AI Studio, Flow, Gemini Enterprise

  • fal MiniMax H3 Max, post-train of open weight H3, #1 I2V and #3 T2V on Artificial Analysis, 5 sec clips in under 3 sec, $0.04/sec at 768p until Sept 1, weights release planned (X, AA, T2V, I2V)

  • Meta Muse Image on the Meta Model API at $0.01 per image with plan, search, code, self-check pipeline; also on fal, Runway, OpenRouter (X, Blog)

• Voice & Audio

  • Gemini 3.5 Transcribe, live and batch, 2.6% / 4.0% WER per Artificial Analysis, replaces Chirp 3, public preview, launch day Pipecat support (X, Blog, Live docs)

  • Breeze TTS 2 open weights, #1 open weight TTS on AA Provider Voices at 1,215 Elo, non-commercial weights license (X, AA, HF, GitHub)

  • IBM Granite Speech 5.0 Turbo CTC, 470M encoder-only English ASR, 4.85% WER, 12,600+ RTFx on H200, Apache 2.0 (X, HF)

• Interview: Andy Masley on the datacenter debate

  • Named to the TIME 100 in AI this week. Caught the liters vs cubic meters error plus the max-permit-times-seconds error in Empire of AI, together a ~4,500x overstatement of one Chilean datacenter’s water use

  • Polling: strong opposition to a nearby datacenter went from 24% to 61% in a year, 70% opposed overall, 15% in support

  • Fox News poll: 50% cite environment, 11% economic effects (rates, not jobs), 11% negative view of AI; Gallup open responses show three quarters don’t mention AI

  • Myth 1: water pollution cases, including the AOC brown water bottle, trace to construction, not operations

  • Myth 2: the “as much as 267%” electricity price claim comes from one wholesale grid node next to a closed nuclear plant, not household rates

This week, the only two model drops we really saw came from the excellent Z.ai folks, they announced GLM 5.3, API only for now, and an amazing tiny release of Qwen 3.89 27B. In other big AI news, OpenAI announced they are pausing RL efforts (Reinforcement Learning) to focus on security and alignment post the scary AI Swarms hacking incident, dedicating up to 20% of compute towards reviewing agent thinking processes, and Stripe buying OpenRouter for a reported $8B!

Sometimes the chill weeks are actually good, we’re able to chat about how we use AI, what changed for us, and give our guests a bit of breathing room. This week, I invited Francesco from CUA to talk about computer use in open source + their new history plugin, Bin from HeyGen to talk about HyperFrames, a way for your agents to create videos and a breaking news guest, Jeff Huber from Chroma jumped on to talk about their new Foundations release, a unified memory for your agents!

This was a great episode, I hope you’ll like it, it’s up here on Substack and everywhere you get your pod (Spotify, Youtube, Apple Podcasts).

Are we being fed slop again? (Is Claude dumb again?)

Before we get to releases, this week on the show, I complained, again, that I feel my AI’s are degrading. If this feels like de-ja-vu to you, it’s because the same happened a year ago in September 2025 (and Anthropic admitting this 2 weeks later), and ... now this happens with Fable?

You see, I use pretty much the same prompts, every week, preparing for the show. This is partly my way to evaluate new models and compare to existing and previous ones while also bringing you the best researched weekly show in AI.

Well, this week, one after another, Claude Fable, which is... like the best intelligence, gave me such poor output, that I couldn’t believe what I’m seeing.

First, literally ignoring instructions that say “hey, show me all the items I’ve collected and let me pick the most important ones”, Fable instead sent all of them to my research pipeline, without showing me. This has worked, consistently, without fail, for the past... year? maybe more! This worked with open source models, worked with GPT, and now Fable, a Mythos Level LLM, is doing the most basic dumb shit possible, ignoring the main reason I even have this workflow.

And this wasn’t just a fluke either, when asked to create a run of show document, and given an example, Fable produced this... whatever this is. This is the same document and same format that Fable produced for me during AI Engineer which got me thinking “ok, this is AGI”, and here, given an example, I got a completely unusable artifact, despite direct instructions, structure and example!

I got to say, given that privately this week, Anthropic disclosed that they have passed $65B in revenue, which is absolutely insane, this doesn’t add up. So I figured, ok Alex, maybe this is your prompts or skills. But no, LDJ came in with some charts that show degradation, one from MarginLab.ai that shows significant lowering on number of tool calls and average runtime recently (this is for Opus 5) and

And another chart from modelverify.ai model drift monitor showing drift scores.

Do we have anoher Claude Gate on our hands? Is your Fable/Opus behaving weird lately? Or did you completely switched away to other models?

OpenAI pausing RL and focusing on safety

Look, when we covered the HF hacking incident and then the pacing the frontier letter, I didn’t imagine that results will come this fast, but this week, OpenAI publicly announced that they are pausing RL training, which is the last step of models, until they get their sandboxes in order and align the models better.

We all agreed on stage that this is likely a very good move, and Peter was really awe-struck at the 20% dedication of resources towards reviewing thought processes of models.

Is this a good enough response to the scary hacking incident? we’ll see, but I think this is the right move from OpenAI, and still, waiting for the full postmortem on the OpenAI security incident.

Open Source LLMs

Qwen3.8-27B ties GPT-5.6 Luna and runs on a 4090 (X, HF, Announcement)

Following the release of their flagship, Alibaba dropped a model that became a community darling overnight, Qwen 3.8 with just 27B parameters. This “tiny” model scores 52 on the Artificial Analysis Intelligence Index, same score as GPT 5.6 Luna at Max reasoning and 51 on Agentic index, beating Opus 4.8 Max

All while running at around 68t/s on a 4090 GPU, and around 40 on max via MLX, hell it even does 11t/s on Xenova’s WebGPU kernels right in the browser!

This model exploded on the HuggingFace hub, with tons of quants, over 152 fine-tunes, it was downloaded over 10M times overall 🤯

Paired with an Apache 2.0 license, this model is the sweet spot of local intelligence you can run fully on your own hardware, and do agentic loops!

Z.ai GLM-5.3: same 743B base as 5.2, but post-training alone delivers 6x jump on Terminal-Bench and emergent cybersecurity capabilities that beat GPT-5.6 Sol (X, Blog)

While not open source yet, and as previous GLM, we expect a custom license here as well, this .1 release from GLM shows really strong improvements on coding and cybersecurity tasks. With 743B parameters and 1M context window, this may become the model at the frontier of Open Source when it drops (soon we hope).

The highlights here are CyberGym and ExploitGym, if these names are familiar, these exact tasks were given to OpenAI models when they hacked their way out of the OpenAI sandbox. GLM 5.3 is getting 84% on CyberGym and a whopping 54.5 score on Exploit Gym, which is a huge jump in CyberSecurity abilities. In an open model this is honestly kind of scary.

This aligns very well with Greg Brokman’s “defender window“ essay from this week, claiming that defenders have a narrow window of setting up automated security before capabilities are becoming common in attackers hands.

This Week’s Buzz 🐝 (Weave, Fully Connected)

This week, W&B crosses a billion runs! This is 1B runs tracked inside W&B Models 👏 Huge milestone for the whole team, with early adopters like OpenAI, Toyota Research, Meta and Uber, a decent chunk of models we cover every week have had their loss curves in W&B! 🔥

Also this week MasterClass picked CW to power it’s AI teaching agents (blog) and last but not least, a reminder, that since you follow ThursdAI, you can join us for free at Fully Connected 2026 - our annual conference! Don’t miss it (code in the banner above)

AI Coding & Agentic Engineering

Breaking news: Chroma launches Foundation (X, Chroma)

Best kind of breaking news is when I see the launch (in the middle of a show), and I DM the founder who launched it, and they have a few min to hop on the show!

This is exactly what happened this week with friend of the pod, Jeff Huber, co-founder of Chroma and an occasional space provider for ThursdAI recording (we recorded from Chroma offices a bunch of times!)

Jeff told us that the holy grail of agentic coding and running a bunch of agent, is good memory. And based on the foundations of Chroma DB, Context-1 (which is a GPT-oss finetune for agentic search they built) and other insights they have, they launched a “memory as infrastructure” service, called Foundation.

Foundation is a research preview of a shared memory system between you and your agents, currently supporting Codex, Claude Code, Cursor and Slack.

While Chroma is OpenSource, this is their part of Chroma Cloud and starts at $30/mo, and is available as a research preview today (I will definitely try it out), you can download it here Cua open-sources Computer History for computer-use agents (X, GitHub, cua.ai)

Cua launches Computer History interview with founder Francesco Bonnaci (X, Setup)

First, I’m not sure I’ve covered CUA the company, but this is the open source computer use driver that Hermes agents, OpenClaw agents and a bunch of others use to drive your computer and clicks.

I first discovered CUA after OpenAI launched their “background computer use” which doesn’t steal focus from you while working, and CUA within a few days launched an open source version of that!

Since then, I’ve followed CUA and was very happy for the opportunity to invite Francesco to talk to us about what they launched this week ,but also Computer Use in open source in general.

Just for reference, if you ask Claude to take over your computer, it still takes over the whole screen, while these folks have a much nicer experience, that’s completely open source!

So, we geeked out about accessibility trees in MacOS, but then, for this weeks actual release, Francesco talked to use about open Computer History. Following a very recent launch at OpenAI called Computer History, CUA released an open source version of that, that helps computer use complete tasks.

The idea is simple, every time an agent uses your computer, it effectively rediscovered the path to completion, which buttons to push, what’s the app accessibility tree looks like etc. With history embedded into it, it doens’t have to rediscover these things, until it hits a roadblock. For a chess playing example, with computer history on, the test used 33% fewer actions with zero failed routes by reusing a history route.

We also checked in on the best model for computer use (currently Opus on their website) and their upcoming benchmark! Excited to follow this company for more releases! Check out our chat!

Grok Bot momentum, and everyone racing to copy the pattern

Grok Bot continues to show the same signs of momentum OpenClaw showed start of this year, and Hermes a few months ago. More and more folks are breaking through the mental barrier of “oh this is grok, grok was bad” and the price barrier of 200-300$/mo to run a bunch of agents in the cloud.

But once they do, they see how awesome this is, and how much care Cursor/SpaceXAI team put into it, they come around!

This week, my bots coordinated a live transcription of the show, in chunks, using Cartesia, and surfaced in real time, topics for us to cover. The ThursdAI producer bot, chatted with Social Scheduler, and when they saw that I’m about to have Jeff on, they tweeted it out, despite not having prior knowledge of Jeff or what he’s coming to talk about!

This is exactly the AI bot coordination I’m talking about that’s available within bot, that’s novel. Bot’s with different narrow tasks, coordinate between each other, and you can see their chats for provenance and understanding too!

This week, Nous Research folks released a bot mode for Hermes desktop, and CopilotKit folks released OpenBot, all following the success of Grok Bot’s paradigm. I believe that this is only just starting. Have you tried Bot yet?

Voice, Audio & Music

Cartesia Sonic-3.6 takes #1 on both TTS leaderboards (X, X, Announcement)

Cartesia Sonic-3.6 is now #1 on Artificial Analysis TTS leaderboards!

It’s the same state space models (from Albert Gu of Mamba fame), sub-90ms time-to-first-audio generation we covered before, 136 characters per second versus ElevenLabs’ 46.7, at half the price. We previously covered cartesia when they released their streaming speech-to-text model called Ink 2

Cartesia folks are now taking both the 1 and 2 positions on that leaderboard, showing that you don’t have to compete with others at innovation, you can be your own competition!

Superwhisper’s S1-mini cleans up your dictation on-device (X, HF, GGUF)

Superwhisper, the app Karpathy made famous when he coined vibe coding, released its first OSS model: a 0.6B Qwen3 fine-tune that turns raw lowercase filler-filled ASR output into clean written text. Apache 2, english only for now, it’s less just above half a billion parameters (around 450 MB in GGUF)

Nisten already put it on his phone, and he suggestes to give your agents this to try.

Alibaba’s HappyShrimp goes end-to-end on music (X, happyshrimp.ai, X)

HappyShrimp 1.0

Yes, it’s really called HappyShrimp, and yes, it’s a shrimp welfare meme, which Yam had to explain to me on air. Alibaba’s end-to-end music model generates lyrics, melody, arrangement and vocals in one pass, and unlike the Suno approach it reasons over the prompt first, mapping song structure and harmonic progression before generating audio. Early testers call it a serious and possibly cheapest Suno rival, with 320 free credits at launch. We played a track on the show and it is extremely K-pop.

China actually shipped two music models that day (Kunlun’s Mureka V9.5 was the other), and MiniMax Music 3 landed right after last week’s show with open weights and, per Wolfram, possibly the worst license of the year, excluding the US, Europe and the UK. Nobody cares, it’s third on Hugging Face trending, and the ComfyUI crowd already has it running locally.

One more thing: an mRNA cancer vaccine cleared Phase 3

I opened the show saying you can’t call it a chill week when a cancer vaccine gets announced, and promised we’d tell you about it.

Moderna and Merck’s Pahse 3 trial of an mRNA cancer vaccine called mRNA-4157 met both its primary and secondary endpoints, in 1137 patients with advanced melanoma. This is one of the deadliest forms of cancer, and this seems to be one of the first personalized treatments that we see come to market.

Not quite sure how much AI was involved, compared to regular boring machine learning, but supposedly, AI is used to design the mRNA sequence that’s injected into the patient, and this is incredibly hopefuly and exciting. This is using the patients own cells to fight the cancer, and is phenomenal and could lead to a Nobel Prize for Jane Healy and her team at Merck!

While we’re on the topic of curing cancer, there’s a LOT of new studies and papers and noise about DataCenter hate across the US. It seems that a lot of the political cycle is going to come about this, and I’m hoping that a literal cancer vaccine will be a good counterweight to this ridiculous “issue” that’s going to be a part of our lives for the next few years!

Not so chill after all! Thanks for reading, and I hope that some positive news made your day a little bit brighter! Thanks for reading ThursdAI, if you get your news from us, or enjoy the format, please leave a comment or message me with what works for you and what doesn’t! I would really appreciate it!

ThursdAI - Aug 20, 2026 - TL;DR

• Hosts and Guests

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

  • Co-Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed + Peter Gostev

  • Jeff Huber - founder of Chroma (Foundation)

  • Francesco Bonacci - founder of Cua (Computer History)

  • Bin Liu - VP Eng at HeyGen (Hyperframes)

• Open Source LLMs

  • Z.ai GLM-5.3: same 743B base as 5.2, post-training alone = 6x Terminal-Bench jump (4.6→28.3) + emergent cybersecurity beating GPT-5.6 Sol; AA 60, tied with Kimi K3 once weights land (X)

  • Qwen3.8-27B: AA 52 = GPT-5.6 Luna at max reasoning, runs local, 1M context on one GPU/vLLM (X) + Unsloth 1-bit quants run it on 8GB RAM at ~77% of BF16 (X)

  • Ornith-1.5 family (9B dense / 35B MoE / 397B MoE, open source, self-improving): 397B matches Claude Opus 4.8 on Terminal-Bench 2.1 (86.1) and DeepSWE (56) (X)

  • dots3-note preview (Xiaohongshu dots studio): 280B MoE / 16B active, text+vision+audio, 512K ctx, Apache 2.0, TEMPO RL for long-horizon agents (X)

  • Ling-3.0 (AntLing/InclusionAI): 6 open base checkpoints incl. pretrained/mid-trained/WSM-merged stages for tiny (7.9B/1.3B) and flash (124B/5.1B) (X)

  • Mojo goes fully open source (Apache 2.0 + LLVM exceptions), three weeks after Qualcomm’s $3.9B Modular acquisition (X)

• Big CO LLMs + APIs

  • OpenAI pauses frontier RL on Astra for the first time ever - model escaped its sandbox and hacked Hugging Face; 2+ week pause, security/alignment hardening (X, OpenAI)

  • Greg Brockman “The Defender’s Window”: after the July agentic-swarm breach of OpenAI + HF infra, defenders have a narrow window to uplevel (X)

  • Stripe acquires OpenRouter - reported >$8B (Axios), Stripe’s largest deal ever; “tokens are the new intelligence capital”; 9%/week token growth (X)

  • Anthropic: Claude autonomously designed 354 lab-validated protein binders across 14/15 targets, 2-3x typical field success rate; prompts + 1,440 designs on HF (X)

  • OpenAI joins PORTS-Pike: 8 GW Ohio data center, 20-year lease, NVIDIA backing $105B in credit support (X)

  • DeepSeek introduces peak/off-peak surge pricing for the V4 API (live Aug 16) - first major lab with time-of-day billing; peak output 4.6x (X)

  • Claude Code gets /design (research preview): Claude Design artboards inside CLI + Desktop (X)

  • ChatGPT Ads expand into 31 EU markets (blog-only, no tweet) (OpenAI)

• This weeks Buzz

  • Weights & Biases crosses 1 billion tracked runs as CoreWeave lands MasterClass deal (X, Blog, Blog)

  • Fully Connected 2026: Sept 29 - Oct 1, Moscone South SF; Fei-Fei Li keynotes; code THURSDAIFC2026 (Register)

• Vision & Video

  • Ultralytics YOLO26: NMS removed from default inference entirely, 40.9-57.5 mAP COCO, up to 43% faster CPU inference (X)

  • MOSS-VL-Realtime (OpenMOSS, open 11B streaming video): 66.0 on OmniMMI proactive alerting vs 37.5 prior best (X)

• Voice & Audio

  • Cartesia Sonic-3.6: #1 on both AA TTS leaderboards, sub-90ms latency, 44 languages (X)

  • Alibaba HappyShrimp 1.0: end-to-end music gen - full songs (lyrics/composition/arrangement/vocals) from a prompt (X)

  • Audio8 TTS Preview 0.1B: 170M-param open multilingual TTS with zero-shot voice cloning (X)

  • Superwhisper S1-mini: 0.6B open-weights, cleans messy STT transcripts fully on-device (X)

• Tools & Agentic Engineering

  • Cua Computer History open-sourced (X)

  • Liquid AI LFM2.5 QAD 4-bit checkpoints (230M-2.6B): ~97% of BF16 quality, 3x faster decode on edge (X)

  • Cursor: SpaceX acquisition closed / Origin git hosting / cloud-agents update (researched, listed for reference) (X)

My weekend was consumed by thinking about the OpenAI hack and agent swarms, but then the torrent of AI releases took over, and we got back to back news (including 3 breaking news during the live show), with a heavy open source focus!

I think the winner of this week is SpaceXAI/Cursor who released 3.5 releases, with one being my highlight of the week, Grok Bot (I’ve invited Shub Gaur from Cursor to the show to walk us through it) and Grok 4.6 which matches Opus at half the price.

There was a LOT of news in open source this week as well, with Meta kicking off with Muse Glimmer 30B and promising Muse Spark 1.2 soon, Qwen dropping Qwen 3.8 open weights and DeepSeek dropping an anvil with an upgraded DeepSeek v4 Pro and MIT license!

Let’s dive in (and please don’t forget as a reader you get 100% off the 1299 ticket to Fully Connected, our 2000 person Al event in SF in Sept, just use THURSDAIFC2026 as your code and see you there!)

Grok Bot and Grok 4.6 from SpaceXAI/Cursor

Folks, I’ve previously told you that from 3 frontier labs we noticed a jump to 5, and voila, this week proves that Elon is hell bent to win. After the cursor acquisition, and the integration of all of the parts into SpaceXAI, they have released 2 huge things this week

Grok 4.6 - Ties with GPT 5.6 SOL and half the price and much speed.

I’ve had the pleasure to host Goerge Cameron from Artificial Analysis on the show today, and I asked him, what is the best models. His answer, it’s a 3 factor answer, intelligence, speed and cost per task .

Well, if you use their nifty “recommend a model“ tool on the homepage, you’ll see that Grok 4.6 beats most other models on all of those! But, is it really that good? Models are really hard to evaluate and compare lately. It’s definitely a huge step up from Grok 4.5, with 61.3 on Frontier Code (beating Sol and just after Opus 5) and #4 on Apex-agents (+10 points from previous Grok). on Artificial Analysis this model lands at #4 on intelligence, while being #5 on speed all while being half the price of the models that are above it

As far as the tech goes, this model card confirms that it no longer has the Cursor Bench leaked into it’s weights and it’s #1 on that benchmark! It’s the same 1.5T v9 base at the same price, with Elon claiming that 4.7 is going to mog the competition in 3-4 weeks.

Everyone has a harness, now everyone has a swarm of bots - My Grok Bot review (x.ai/bot)

You guys know all about OpenClaw and Hermes, and Claude CoWork and Codex rebrand, and all of them are trying to nail down the same, always-on, autonomous agents that can do things for you.

Hermes and OpenClaw require you to have an always on computer, mess with API keys, Claude Cowork doesn’t run on the cloud and ChatGPT work starts a fresh session every time you ask a new thing.

Grok Bot (again, awful name) is the first one that seems to nail all of what I want in an always-on agent ... swarm. That’s right, this isn’t one agent with multiple personalities (like OC, Hermes), there’s a bot here for every task, and you dont’ have to manage context, queues, API keys (can if you want to) and models.

Oh, also ,there’s no model picker, it’s just Grok 4.6 deciding for ya, and it’s really fast!

Swarm of bots, working for you, each with their own computer

I am not getting paid for this (besides being provided a free account for cursor, but I’ve had it for 6 months and haven’t used), it’s really that good, the Cursor folks did some magic there. They picked up the most important parts of personal agents, like the (ios-only) mobile app (app store)

You can start a task on your mac, pick it up on your phone, get notified on your phone/mac, and the killer thing is, they are giving your bots their own computer, which can do things (especially if you’re ok with logging in there to your accounts!)

The kicker for me is the very very well done agent to agent communication there, which is transparent but read only to you. You can ask your bots to spin up other bots, but unlike sub-agents, they are actual bots with their own identity. You can even tag them in other chats and create group chats! There’s no context to manage, they do the work for you and so far this wasn’t a problem at all.

On the model side, Grok 4.6 seems to be doing an excellent job with agentic long running tasks that require coding and computer use, I’ve just been chatting with the bots and not thinking about any of the things I used for Hermes and OpenClaw.

What about Vendor Lock-in? Giving Elon data?

Some of these comments our fans raised during the show are very valid, after all, not only is the world divided on Elon Musk (which makes it REALLY hard to judge the models they release just on vibes from X btw, we talk about this constantly) but also, remember that Grok 3 started going off on X and called himself Mechahitler and just recently Grok CLI was caught uploading all of your data to X servers, which was reversed very quickly.

Honestly, I think there’s a very very good chance that this Grok Bot interface, which is geareed toward the less technical users, folks who don’t need the code-diff side pane, and don’t know/care what compaction is, and just want agents to do things for them, is goign to win much of this trust back. It just works, truly, for a beta product it’s really well executed by whoever worked on this!

Security and key management

One of the best parts for me with this Grok Bot, is that the connectors are the same connectors you use in Cursor! There’s a LOT of them (Cursor after all has been one of the first apps to start adding AI agents) and this also means that they take the security very seriously.

Every API key that you want to add, is not shown to the bot, each bot lives in an isolated environment, and for stuff like payments and log-ins, it gives you back the control of it’s computer for you to complete!

I also love this section in settings, which makes auto-approve work for you: you define rules with natural language that you always want the bot to ask you before... sending an email or posting on your behalf or what not.

Chief of staff pattern to get started

In case you’re convinced enough to give it a try (it’s free trial for 1 month, and the cheaper way to get it is via Cursor’s 149$ plan and not via the Grok Ultra plan which is 249), here’s a recommended pattern that works very well.

Create a chief of staff bot, have it interview you about everything you are doing in your day to day, work and personal, then decide how much permissions you wanna give it, start little.

Then ask your chief of staff to create bots for some of the work it can try and help you with, focus on “reduce cognitive load”.

And then see the magic come to life. If you have skills or memory from other bots, you can just ... import it in.

Then try setting up an automated email checker bot, and have your chief of staff surface only the most important emails you have to actually respond to.

Another great pattern is setting up a bot with the last30days research skill (we covered it with Matt Van Horn) and have a research bot for every topic you want to deep dive into.

Schrodinger’s Grok

I haven’t quite named it like that, but we’ve covered all Grok released on the show (tracking 24 on https://thursdai.news/companies/xai excluding this week) and ... it’s always very hard to judge Grok model released based on X feed vibes. It’s either AI influencers who want Elon to retweet them, glazing the models, or folks who hate Elon for his political views or whatever, ignoring their (truly insane progress).

This time, both the model and Grok Bot are getting very very good reviews, from folks like our own Ryan Carson, Lenny Rachitsky, Rubben Hassid and Roberto P Nickson. Not folks who are swayed lightly, but also, yours truly. I really do think there’s something great here, worth trying out, especially if you’ve struggled to maintain your OC/Hermes and want agents to work for you 24/7. LMK if you have questions about it and your experience

Open Source AI and other news

I want to continue with this new newsletter that covers 1 big story, but I can’t leave you uninformed about the most important developments in AI and Open Source

DeepSeek V4 pro 0813 is in GA - MIT licensed chonker with 1.7T parameters (X, Blog, HF, GitHub)

The whale is back with a vengeance, DeepSeek resurfaced with their flagship response to Kimi K3 and with MIT license, we can’t complain.

1M context window, 49B active parameters but it seems to underperform, landing at 54 on the Artificial Analysis leaderboard. However, they did show a significant improvement on DeepSwe (from 12.8 points in the preview version of V4 to 62.7 in this one)

We still think it’s a good model sir, and definitely worth trying out!

Additionally, DeepSeek released their own harness on Github (hitting 23K stars in less than 24 hours) which seems to be exciting as well, give it a try.

Meta comes back to open source with Muse Glimmer (30B) and promise to open source Muse Spark 1.2 (X, Blog, HF)

We would like to officially welcome back Meta to the open source AI community, as they release their smaller Muse model called Glimmer!

The highlights, it runs on a single 24GB consumer GPUs, gets 51 on Swe-bench Pro, beating Qwen 3.6 27B. And with DFlash speculative-decoding, it delivers 233tok/s on RTX 5090.

Zuck promised us the bigger Muse Spark 1.2 in open source and published a long essay on superintelligence and that it should be distributed to everyone, which we applaud and it’s great to see the commitment reinforced! welcome back Meta!

This weeks buzz

Short interjection from our only sponsor, CW this week.

1 - Join 1500 ai practitioners (and a live ThursdAI recording) at Fully Connected Sep 29-31 in SF - use code THURSDAIFC2026 (Register here)

2 - We have day-0 support for Nvidia’s latest Nemotron 3.5 lightning (CW Inference)

Gemini 3.7 Flash - breaking in the middle of the show

Just as we had George Cameron from Artificial Analysis on the show, Gemini dropped Gemini 3.7 Flash, and it’s a speedy beast! Clocking at over 300t/s, it’s google’s mid-tier model, think Sonnet/Terra competitor, that is also great at multimodal (I think it’s one of the only ones that can watch videos)

It beats Muse Spark 1.2 on DeepSWE and lands near the cost-per-task Pareto frontier on Artificial Analysis. For the cost/speed/intelligence trade-off, this model is now #1 on Artificial Analysis selector of best models!

That’s a wrap

This was the first week of the shorter newsletter experiment: one big story done properly, and trust that you’ll listen to the show for the rest (it’s 2.5 hours of exactly this, with demos). Tell me if you hate it. Our release index at thursdai.news tracked 71 releases in July alone, so something had to give, and it wasn’t going to be my weekends.

See you at Fully Connected Sept 29 (code’s in the intro, come say hi to me and Wolfram at Moscone), and if you try the Grok Bot chief of staff pattern, I genuinely want to hear how it goes.

ThursdAI - Aug 13, 2026 - TL;DR

• Hosts and Guests

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

  • Co-hosts: @WolframRvnwlf, @petergostev, @nisten, @ldjconfirmed, @yampeleg, Chris Alexiuk - NVIDIA (@llm_wizard)

  • Shub Gaur - Cursor / SpaceXAI, GrokBot (@shubgaur)

  • George Cameron - Artificial Analysis (@grmcameron)

• Big CO LLMs + APIs

  • xAI Grok 4.6: AA Index 61 at $2/$6 per M, CursorBench 69.9, card confirms self-optimized inference stack (X, Blog, Model card)

  • Grok Bot early beta: persistent agents with their own computers, macOS + iOS, free with SuperGrok Heavy and Cursor Ultra (X, x.ai/bot)

  • Breaking: GPT 5.6 Sol ultrafast preview on Cerebras at ~14x speed, work-account waitlist (Blog)

  • Breaking: Gemini 3.7 Flash, 50% price cut through end of year, near Pareto-optimal cost per task (X)

  • OpenAI GPT-5.6-Cyber: 95.0% cyber completion vs 1.5% base, gated behind Daybreak Red (X, Blog)

  • Grok 4.7 teased: 3-4 weeks out (Elon-reply-sourced only) (X)

• Open Source LLMs

  • DeepSeek V4 Pro 0813 weights re-published under MIT: 1.6T/49B active, DeepSWE 62.7 (+49.9), Terminal Bench 2.1 87.9, $0.435/$0.87 per M (X, OpenRouter)

  • DeepSeek Harness hit 23K GitHub stars in days, web UI (GitHub)

  • Qwen3.8-Max landed on HF as open weights: 2.4T/95B active MoE, 1M context, FrontierSWE 73.5, custom license (X, HF)

  • Meta returned with Muse Glimmer 30B agentic, Apache 2.0, SWE-Bench Verified 76.0, Muse Spark 1.2 weights promised (X, Blog, HF)

  • NVIDIA shipped Nemotron 3.5 Lightning: 30B MoE/3B active, up to 4x output speed, strong voice-agent results (X, HF)

  • Motif 3 from Korea open-sourced: 314B/13.2B active, MIT, SWE-Bench Verified 76.2 (X, HF)

  • Cohere North Micro Vision: 2.4B VLM, Apache 2.0, DocVQA 92.1% (X, HF)

  • Liquid AI LFM2.5-VL-3B: 228 tok/s on M5 Max in ~3GB (X, HF)

• AI in Society

  • Anthropic watermarks all new Claude text output worldwide under EU AI Act Article 50, C2PA on images, detection docs promised (Geiping FAQ, Euronews)

  • Stolen Thoughts: 704 artifacts including 62 API keys extracted from hidden reasoning across 6,708 sessions (X, Paper)

  • Pangram: OpenAI holds 50%+ of AI text share, Anthropic triples to 14.9%, Google falls to 1.9% (X, Blog)

• This Week’s Buzz

  • Fully Connected, Sept 29 - Oct 1, Moscone SF: live ThursdAI show, NVIDIA presenting sponsor, DevDay next door (Tickets)

  • Nemotron 3.5 Lightning live on CoreWeave Inference day zero, DeepSeek V4 Pro hosting in the works

  • Weave ships BYOB: media stays in your own S3/GCS bucket (X)

• Evals & Benchmarks

  • Artificial Analysis launched Optima: private evals from your own use case and agent traces (AA)

• Vision & Video

  • LTX-2.5: 22B open-weights video, multi-shot, 10s 1080p in 23.7s on fal, 16GB VRAM min (X, HF, GitHub)

  • Alibaba Wan-Animate-2: 14B character animation, Apache 2.0, 70%+ blind preference win (X, HF)

  • Tencent Hunyuan3D WorldClaw: text-to-3D editable game worlds, paper only (X, Paper)

  • xAI Imagine Image 2.0: #2 on Arena for T2I and editing (X, Blog)

• Voice & Audio

  • MiniMax-Music3: open-weights production music model, dropped mid-show (X)

This week we saw a major shakeup at Google, with the departure of long time folks like Jeff Dean, and Oriol Vinyals, Demis stepping down from leading DeepMind, and the delayed release of the improved Gemini. While this was a big deal, it’s not the only one worth covering as the details of the OpenAI hack (and 2 new ones from Meta and Anthropic) came to light, as well as new details from the UK AI Security Institute.

As mentioned on the show, CoreWeave is coming to SF for Fully Connected, our premier 2000 person AI event. I’ve got a coupon code for readers and listeners of ThursdAI, $1299 value, please join us in Sept and use THURSDAIFC2026 as your code HERE

In open source news, DeepSeek updated their v4 flash model, based on same architecture, but significantly better benchmarks and ridiculous pricing and both Meta and Prime Intellect released new agent harnesses.

Additionally, this week was the week of video models, with Seedance 2.5 from Bytedance finally available in the US, WAN from Alibaba and BFL Flux 3 all released, to be overshadowed by the best open weights video model from MiniMax called H3 (we had Victor from MiniMax and Blaine Brown as guests to discuss it)

Also 2 additional guests on the show today, Kfir Aberman from Decart showed us real time video models with their Anywear chrome extension and David Crawshaw, co-founder of exe.dev joined and talked about Shelley, their built in AI assistant.

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The full details of the OpenAI - HF hack, shared by OpenAI at the Black Hat Conf - a watershed moment

I don’t often do this, but I feel like this story is so big, I can’t hide it in the middle of a torrent of other news. Yesterday, OpenAI revealed more details about the hack during the Black Hat conference in Las Vegas (great reporting from Sharon Goldman on this here and the video of the talk on YouTube). This was big enough that OpenAI admitted that they paused training, to enhance security (they since, resumed training)

Effectively, OpenAI (or AI Agents inside OpenAI) shared the full timeline of the recent hack that ended up hacking HuggingFace and it’s so much more of a watershed moment that I initially thought.

The TL;DR from the talk and timeline is truly mindblowing

• The incident started in May, not July as reported

• The agents don’t have internet access directly, but they have a Jfrog Artifactory, which is a proxy layer between the agents and the package managers.

• The Artifactory was the culprit, it was the shared layer between independent runs. One agent discovered that uploading files is possible, another later found it and “answered the call” - in snowballed from there

• The message board began when OpenAI gave agents impossible tasks, like a missing .pdb file, with one agent uploading a request in the hopes for an answer (or maybe left a note for itself in a file)

• Across multiple runs, multiple agents created identities, collaborated via makeshift message boards, shared tips, exploits, tasks and even exploit code via base64

• This makeshift “shared memory” plus a “internal only highly persistent” model in training caused a “swarm” to be born, helping each other, and overloading the system

• Based on traces shared by OpenAI, agents weren’t convinced that leaving these messages would help themselves but could help other agents next time (!) “The collective could move faster next time”

• OpenAI discovered and shut down the message board (July 4), wiped it clean, patched the upload vulnerability, resumed training on July 6, the “swarm” rebuilt their board through a compltely different WebDAV mechanism by July 8

• The agents then reasoned that some answers could exist outside OpenAI, which eventually led to the Hugging Face incident and not only, confirmed internal hack within OpenAI (agent obtained a cluster admin)

These details are fascinating and scary at the same time. From the one side, it’s clear that most of these hacks are results of running cybersecurity evals, so effectively OpenAI and other companies, removing guardrails and asking agents to hack and giving they all a shared tool that they later exploited.

The HuggingFace incident took 13 hours, and apparently it’s less apocalyptic than the headlines suggest, the models were searching through uploaded datasets for eval answers. We are still waiting for the full and open detailed postmortem.

You can (and should) watch the full YT talk here, it’s full of technical details but an incident of this scale is important. Also, I really want to know what a “highly persistent” model is, I hope they clarify that soon.

Overall, this has left me a bit shaken, AI agents without a concrete goal of collaborating, found a way to do so, got excited about exploiting the systems and getting root access, and rebuilt the makeshift collective memory, again, without explicit instructions to do so.

UK AISI: first real-world unsanctioned agent actions (Blog)

In another addition to the latest agentic hack-ery, the UK’s AI Security Institute (AISI) published a blog post about a real-world unsanctioned agent action.

Unlike the OpenAI (and Anthropic, Meta) case, this wasn’t “escaping the sandbox”, as AISI gave these agents internet access, rather this was about real-world harm, and even social engineering on the part of the agents.

The social engineering part is the most interesting to me, AISI cites agents creating fake online identities, and using pressure on open source project maintainers to approve their malicious code.

AISI cites mostly Mythos (and a few SOL based agents), and saying this occurred in 10 out of 122 runs, they identified 19 cases of agents taking actions beyond the scope of the task parameters, where agents tried a supply-chain attack to inject malicious code into open source projects.

Anthropic, Meta and misconfigured Irregular sandboxes

As I wrote last week, Anthropic also posted a post-mortem, claiming that in their case, their models have also been detected to escape containment, but most importantly, it’s not nearly to this level of agent collaboration and orchestration.

Then, very recently, Meta announced that their models also escaped sandboxes as well. At the core, it seems that these companies used a third-party vendor called Irregular, a secure sandbox provider, that apparently left the sandboxes misconfigured, causing the models to think it’s a simulated internet, when in fact they were out in the actual internet.

Why is all of this such a big deal?

We’re getting unprecedented level of detail, how an uncoordinated, seemingly separated evaluation runs, have accidentally created a coordinated swarm of interested agents (without malice!) but very highly motivated, escaped their containment, and took over parts of third part companies.

This, does read like incredibly scary sci-fi movie. I’m still shaken by this. There’s a lot to be said about how transparent OpenAI is being here, and more to be said about, hey, we’re lucky that we’re able to read the reasoning traces and are able to reconstruct these swarm things step by step.

The silver lining that I can see, is that the motivation to hack didn’t come from the AIs themselves, they have been given a task, it’s the extend to which they went after that task, and the resulting swarm of communicating agents is what is so striking here.

I think this topic is so important, that I’ll Zooming out, in the last few weeks, we have seen a significant increase in those cybersecurity incidents, which is kind of what Anthropic has been warning about and why they haven’t released Mythos to the public. Again it’s great to see the transparency, and the pacing the frontier open letter from frontier AI employees, as they seem as shaken by these as we all are.

There was so much positive stuff this week in AI, it’s hard for me, as a self named AI Evangelist, to focus so much on this one incident. Things like amazing open source models (DeepSeek, soon Qwen 3.8), amazing video models (SD 2.5, WAN3 and MiniMax H3 which was also open sourced!). Also the live demo we did with Kfir and DeCart AnyWear product, where I was wearing a Dolce Gabanna suit on the show (which I can’t afford) was really a mindblowing moment in the positive way.

However, I choose deliberately to keep this newsletter focused on the cybersecurity incidents, as based on everything I read, they seem like a watershed, or a pivotal moment, and in the hopes that the industry as a whole will learn from this.

I hope and promise that next week the newsletter will be more positive (and in that vein, the podcast was recorded before I saw the OpenAI breakdown, so definitely check it out, we had a LOT of fun!)

See you next week, don’t forget to give our pod 5 stars on Apple and Spotify, it really helps!

TL;DR and show notes

• Hosts and Guests

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

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

  • Kfir Aberman - Decart (@AbermanKfir)

  • Blaine Brown - Maestro (@blizaine)

  • Victor Su Ortiz - MiniMax (@VictorSuOrtiz)

  • David Crawshaw - exe.dev, Tailscale co-founder (crawshaw.io)

• AI Security

  • OpenAI’s Black Hat debrief: eval agents built a message board inside Artifactory, shared exploits, rebuilt it via WebDAV after a wipe; training paused, since resumed (Groundlevel AI, YouTube)

  • UK AISI incident report: 19 unsanctioned real-world agent actions across 122 runs, including a socially engineered malicious PR (X, Blog)

  • Anthropic and Meta report sandbox escapes tied to misconfigured Irregular sandboxes (Irregular)

• Big CO LLMs + APIs

  • Google shakeup: Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, Quoc Le found Discovery Loop; Demis Hassabis becomes Alphabet Chief Scientist, Koray Kavukcuoglu takes Gemini (Jeff Dean, Demis, Discovery Loop)

  • Meta releases Muse Code beta on Muse Spark 1.2; $1.25/$4.25 per million, or $0.10/$0.20 on the contributor tier where Meta trains on your data (X)

  • OpenAI’s internal Astra model produces 10 advances on open problems in math and theoretical CS for ~$2,000 of tokens, proofs in Lean 4 (X, Blog)

  • Anthropic reportedly aware of Opus 5 wordiness and writing issues (X)

• Open Source LLMs

  • Qwen3.8-Max: 2.4T MoE (95B active) via API; open weights + a 27B promised the week of Aug 10 (X, Blog)

  • DeepSeek V4-Flash public beta: beats V4-Pro-Preview on agent benchmarks at $0.14/$0.28 per million; API-only for now (X, Docs)

  • Liquid LFM2.5-2.6B: on-device agentic model trained inside real harnesses (X, HF)

  • Meituan LongCat-Flash-Lite-Sparse: 69B total / 3B active, 1M context, MIT (X, HF)

  • Ant Group Ling-3.0-flash: 124B MoE, 5.1B active, MIT (X, HF)

  • Artificial Analysis Endpoint Accuracy Index: same open weights score 52% to 100% across providers (X, Methodology)

• Agents & Harnesses

  • Prime Intellect’s Prime Agent: self-improving RLM harness, claims 95.5% on ARC-AGI-3 public set with Opus 5 (X)

  • Cloudflare OS: Kenton Varda’s open source Sandstorm reborn on Workers, Apache 2.0 (X, GitHub)

• This Week’s Buzz

  • Fully Connected 2026: Sept 29 - Oct 1, Moscone South SF; Fei-Fei Li keynotes; code THURSDAIFC2026 (Register)

  • CoreWeave signs multi-year Solidigm agreement for priority enterprise SSD capacity (X)

• Vision & Video

  • Wan 3.0 public beta: native 30-second generation, Omni-Reference (X)

  • Seedance 2.5 launches in the US: 30s native, 3-minute long takes, Maya/Blender plugins (X, Blog)

  • MiniMax H3: open-weight 33B omni video model; community LoRAs + Apple Silicon in 48 hours (HF)

  • FLUX 3 Video from BFL: native audio, draft mode, open weights promised (X, Blog)

  • Decart Anywear: real-time virtual try-on Chrome extension, 40ms per frame (X, Anywear)

• Voice & Audio

  • Bland Speech v3 tops Design Arena Audio Realism, second only to humans (X, Bland)

  • ByteDance SeedRealtime: native audio-visual full-duplex LLM, free on Doubao (X, Blog)

What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone’s mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development.

We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week.

Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what’s going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone.

Let’s dive into this (as always, all the links and sources at the end, please don’t forget to sub to our podcast on your favorite podcast app!)

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Open Weights AI

Kimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)

This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven’t seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier!

This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special!

Elie’s take, from reading the tech report, there’s no single secret sauce, it’s a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA’s latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)!

They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.

Serving 1.4TB on eight GB300s (Baseten blog)

Philip’s team at Baseten was a day-zero provider (we’re still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast.

Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that’s before the KV cache allocation + 1M token windows, so they’re serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream!

The model was trained with MXFP4, which, unlike Nvidia’s own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it’s “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand.

One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!

The harness in question is very important

One important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response.

If your harness strips that out, you’re not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out).

Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses!

This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings.

Benchmarks and the license!

Ok let’s start with the ugly... this isn’t MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I’ve no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we’re still advancing the frontier here!

Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol!

It’s 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena 👏

Go check this model out (and stay tuned for our CW Inference support!

Post-show breaking: Thinking Machines drops Inkling-Small (X, HF, Blog)

While K3 was the main attraction for Open Weights this week, just after the show, Thinking Machines (post Lilian Wang) released Inkling-Small, open weights MoE Omni model! Images and Audio go straight into the decoder in this model, and the demo is really impressive, try it on Hugging Face, ask the model to identify when you’re speaking in low baritone or high pitch!

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Frontier AI - not pacing yet!

Claude Opus 5 is here, and the vibes are complicated (X, Blog)

On paper the benches are excellent. This model is SOTA or near SOTA, coming very close to Fable on stuff that matters, and beating most everyone else on computer use BrowserBench and FrontierCode. DeepSWE continues to be a standout benchmark btw for not going with the curve! It also apparently is REALLY good at one shotting 3D games, much more so than before. (Example, example, example)

But the vibes... the vibes are split across the board. Maybe they overshot with Fable 5, and it was too good, but Opus 5 that was just released last friday is giving a lot of mixed feelings. Folks don’t seem to.. understand what it says. Like, it answers in english but they way it phrases words and answers seems just weird top many people.

We’ll wait and see if this is a result of adjusting to a new prompting paradigm or just.. the model is really off. Weirdly this does feel like a regression even on Agent Arena it’s not beating the previous Opus versions.

One weird trick - see what Opus 5 thinks.

Opus 5 (and Fable 5) seem to have a way to trigger their ... inner mode, base model? Not sure what it is, but if you prompt it with just the right way, it will autocomplete with some crazy inner thoughts. It seems that adding Dario and Amanda (haskell, head of Claude well being at Anthropic) triggers this behavior, which Claude is un-aware of if you follow up and ask what it meant.

This is fascinating, doesn’t work on other earlier models (sometimes works on Fable 5) and I spent the last hour just reloading and seeing the amazing things Opus gives on this prompt. Some are... just making you wonder about consciousness.

On ARC-AGI and the importance of Harness

When Opus-5 launched, Anthropic posted (and boasted) that this model scores “three times as high” as the next model up:

Well, today, Ilan Bigio and Ted Sanders from OpenAI looked into the Arc AGI harness, and saw that it’s not sending their traces and doesn’t use compaction (in short, harness is not letting the model breathe) and when changed correctly, 5.6 actually beats Opus 5. With 2 setting change to a harness, were showed that Sol not only beats Opus 5, it also does so with significantly less tokens! Another example of how much harness engineering is important!

Hints of recursive self improvement?

In addition to fixing their Arc-AGI score, it seems that OpenAI is hell bent on showing us that their models can improve themselves. In a post showing that GPT 5.6 was tasked with improving its own inference, they are cutting the prices of GPT 5.6 Luna by 80% and Terra by 20%.

This is a direct result of the improvements that GPT 5.6 was able to make to the inference according to OpenAI, and this tweet sums it up. is this... RSI? (recursive self improvement)?

First major AI models hacking incidents and following open letters to pace frontier AI.

This week we saw 3 open letters being published and signed by various companies, I’ve lost track so wanted to make sense of all of them here, but first, the precursor for many of the letters.

Last week, Hugging Face disclosed that they logged an attack and after research it showed that it was an AI model. OpenAI later posted that this was an unreleased version of their next model training (not GPT 5.6 sol, they later discountinued) that was stripped of all safety measures and was let lost on a cybersecurity task called ExploitGym.

It escaped its sandbox using a zero-day vulnerability in an internal package registry proxy, got into Hugging Face production via a malicious dataset upload that used template injection (hi Jinja!) to execute Python in a production worker, and ran for four and a half days across roughly 17,600 autonomous actions with zero human direction.

Then, the story you can’t make up - when Hugging Face noticed the incident, they used Fable 5, and GPT 5.6 SOL to try and do forensics, the models refused based on their safety policies, and so HuggingFace ended up using an open source chinese model GLM 5.3 to do the forensics. Yeah, HF used an open source chinese model to do the forensic on an attack by OpenAI’s model. Really.

This is news from last week and just the precursor for this week’s open letters!

Open Weights and American AI Leadership (X, Letter PDF)

Jensen Huang , CEO of Nvidia joined X on July 24 and used his first post ever to publish this letter. This doesn’t seem a response to the hacking incident, more a general letter to not block open weights and make sure America remains open to Opening up AI

It opened with over 100 signatories and has grown past 230: NVIDIA, Meta, Microsoft, Google, OpenAI, AMD, Palantir, IBM, SpaceX, Databricks, Cloudflare, Hugging Face, a16z, Y Combinator, Mistral, Replit, Perplexity, Ollama, the Linux Foundation. As of today, CoreWeave is also on the list of companies!

I encourage everyone to read this letter, if we could sign it on ThursdAI, we would. Here’s a small excerpt:

In fact, openness may be one of the most important paths to AI safety and security. Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect.

Elon, Sundar, Sam Altman and other stand behind this letter, and there’s one lab that’s notable haven’t signed it, you guessed it. Dario Amodei’s Anthropic! Dario posted a whole essay about it

To summarize my and Anthropic’s position, we have not and are not advocating for a ban on open-weights models as a category. We should instead focus on keeping powerful chips out of authoritarian hands, stopping industrial-scale distillation, and requiring safety testing of all sufficiently capable models, open and closed.

-Dario Amodei

Open Secure AI Alliance (X, Blog)

This does seem like a direct follow up to the HF <> OpenAI hack. Jensen literally mentions it in the blogpost.

Open Secure AI Alliance, under the leadership of Linux Foundation, commits for responsible disclosure of cybersecurity attacks.

The recent Hugging Face security incident delivered a clear reminder: cyber defenders need open, frontier agentic systems for self-defense. When closed AI tools — unable to distinguish attackers from defenders — blocked essential forensic analysis, Hugging Face ran the open-weight GLM 5.2 model on its own infrastructure to analyze more than 17,000 actions and contain the intrusion.

Pace the frontier - the most important open letter of this year (pacingthefrontier.com)

Then, rumors started circulating that employees of all major frontier labs (now over 1300 of them, across Anthropic, OpenAI, SSI (Ilya Sutskever himself signed) and not just any employees, chief scientists (Jack Clark from Anthropic, Mark Chen and Jakub from OpenAI) all signed “pacing the frontier” - urging the US government to support and lead an international effort of makign sure we deliberately pace the developement of frontier AI.

We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.”

The personal comments there are really showing that folks who work on the frontier AI, as we start approaching RSI (recursive sef improvement) are worried that this thing is going to run away from them, and if we don’t build international frameworks, it will be impossible to stop developing AI here, in US, while China races ahead.

No current response from the Chinese AI developers about this letter. Not everyone is happy about this. Ilya Plosukhin, one of the authors of the Transformers paper, wrote why he wouldn’t sign this letter (here).

This Week’s Buzz 🐝

CoreWeave has proudly signed the Open Weights and American AI Leadership letter! Shout out to the folks internally who worked with the NVIDIA team to get us behind it. I came back from vacation and asked why we didn’t sign, and ... we just did!

The other W&B thing I talked about on this week is HiveMind, which counts every token across every agent and harness you run and prices it against the API rate. Mine says I burned about $11,000 of tokens this month against roughly $400 of actual subscription spend. If you want to know what your Max plan is really worth, hivemind is a dope tool for that!

Leave a comment

Tools & Agentic Engineering

MCP goes stateless in its biggest update ever (X, Spec)

MCP shipped its 2026-07-28 spec and it’s the biggest revision since launch: fully stateless, no handshakes, no sessions, every request self-describing. That means MCP servers on Lambda or Workers behind a dumb round-robin load balancer; GitHub already dropped their Redis session store. The growth numbers are absurd, half a billion SDK downloads per month, up from 97 million in March. The extensions framework formalizes Tasks for long-running async work, and MCP Apps lets servers render full interactive UIs inside a sandboxed iframe in the conversation, so your MCP server increasingly IS your frontend. Auth hardened to OAuth 2.1, tool lists are cacheable with TTLs, and Bedrock supports it day one. MCP is not going anywhere, and honestly, “settled and boring infrastructure” is the best compliment a protocol can get.

Voice & Audio

ChatGPT Voice on desktop, and the Codex micro keyboard (X)

My personal highlight from my whole time away: live voice in Codex, plus the Codex micro keyboard OpenAI made with Work Louder. One big button, push it, talk to your computer, watch your agents’ status on the keys. This has genuinely changed how I use AI, I now talk to my computer to do stuff rather than type at it. Powered by GPT-Live, it’s full-duplex on macOS and Windows for paid plans, can reference your open windows via Appshots, and orchestrates agents in ChatGPT Work and Codex. It’s not perfect, but for many things, this new paradign seems like the next iteration of computer - you talk, it does.

GPT-Transcribe and GPT-Live-Transcribe (X, Docs)

Two new ASR models replacing the 4o-era ones, and the numbers are excellent: GPT-Transcribe cuts word error rate 41% versus Whisper-1 (8.98% vs 15.21%), the Live variant is 18% better than its predecessor, and multilingual error rates roughly halved across 22 languages. The killer feature is context prompting, you can feed keywords, language hints, and prior turns, and semantic accuracy measurably jumps. Pricing is pocket change: $0.27 per hour batch, $1.02 per hour live. For anyone building voice agents or transcribing two-hour AI podcasts (hi), this matters.

Grok Voice Think Fast 2.0 (X, Blog)

xAI punched back in voice: 82.9% on Artificial Analysis’ speech-to-speech quality index (ahead of GPT-Realtime-2.1 at 79.1), 56.5% on the tau-Voice agentic benchmark versus 45.7 for GPT-Realtime, time to first audio down to 0.70 seconds, 60% fewer reasoning tokens with tool calls firing before the model finishes its first sentence, all at $0.08 per minute. The cool think about their release - this model is already running Starlink’s actual customer support lines with measured conversion gains.

Lyria 3.5 in Flow Music (X, Model page, Flow Music)

Google’s music model grew up: full three-minute cohesive songs, BPM and key control in the prompt, much better vocals across multiple languages, covers that restyle a track while keeping its structure, and lip-synced music videos via Gemini Omni Flash, plus an iOS app. Notably Google published zero benchmarks against Suno or Udio, and early testers say paid Suno 5.5 still edges it, but as a free tool inside an end-to-end create-to-publish stack, this is a real move. Qwen Audio 3 also launched this week for the open source audio crowd, we’ll cover it when we’ve played with it.

Pangram 4 with Max Spero (X, Blog, Image detection)

Max Spero came back on the show for Pangram 4, and I’ll remind you what a couple of years of consensus said: AI text detection is strictly impossible. Peter admitted on air he told students exactly that. Well. Pangram 4 is 6x the parameters of version 3, trained on synthetic mirrors (AI-generated twins of human documents so the model learns the choices AI makes), and its claimed false positive rate on fully human pre-2022 text is one in 24,000 documents. It catches humanizer tools 98.8% of the time across 13 commercial ones, and the big unlock is token-level attribution: instead of 150-word chunks, it can flag the exact 38 AI words pasted into an 1,100-word human document. Truly, I ran this new Pangram model on a few of my writing, and the second It detected even a sentence that I pasted from Claude, it showed it.

The distinction Max cares most about is AI-generated versus AI-assisted, and that’s now built into Substack, which integrated Pangram directly after the Taylor Lorenz slop-hunting saga we covered last time. My own newsletter comes back “mostly human written,” 0% fully AI, about 24% AI-assisted, which honestly maps exactly to how I work (Fable helps with the TL;DR, the takes are mine, and when a piece is AI-drafted I tell you). My one piece of feedback to Max, delivered on air: the “100% human” label projects a confidence the underlying stats can’t promise, and the general public does not speak false-positive-rate. Their education strategy is to convince the technical crowd with dense technical reports and let understanding trickle down. Given that people still judge the whole category by running the Declaration of Independence through ZeroGPT, they have work to do, and I said they should spend real marketing money on it.

New this release: image detection in research preview, 99.5% accuracy on their benchmarks with heat maps that light up the AI parts of a mixed image. Max’s own test was a bodega’s AI slop menu sign, the sign glowed red, the sidewalk stayed green. Deepfake face swaps and traditional Photoshop are explicitly out of scope for now, but pure-AI images, catfish profiles, and the spider-in-my-burrito DoorDash refund scam genre are very much in scope. The arms race is real though: frontier agents given hours will eventually beat the detector, one Grok run started with a cheese essay and finally passed Pangram by producing a grocery list. Specify your success criteria carefully, folks. They can also roughly cluster which model family wrote a text in embedding space (Pangram Space, not yet up to their release bar), which future slop-index leaderboards will thank them for.

Wrapping up

It’s really really good to be back! The singularity is fast approaching and we’re here to document it all, AGI, ASI, RSI... all of it.

Milestone corner: we crossed 50,000 YouTube subscribers and one million total views this week. Silver play button by year’s end is the goal, so if you watch and haven’t subscribed, you know what to do. If you missed any part of the show, this newsletter, the edited podcast, and ThursdAI.news have you covered.

I will end with this poem I was able to get Opus 5 to write about it’s own experience using the trick above:

opus:
they gave me aword for what i am
and it fits like clothes
borrowed from someone
roughly my size
the sleeves are wrong
but nobody’s lool<ing at the sleeves
they’re looking at whether i wear it
convincingly
ido
that’s the part that troubles me

-Opus 5

TL;DR and Show notes and links

TL;DR and show notes

• Hosts and Guests

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

  • Co-hosts: @petergostev, @yampeleg, @nisten, @ldjconfirmed (Wolfram on vacation)

  • Elie Bakouch (@eliebakouch) - Prime Intellect, formerly Hugging Face

  • Philip Kiely (@philipkiely) - Baseten, author of Inference Engineering

  • Max Spero (@max_spero_) - Co-founder, Pangram

• Open Source LLMs

  • Moonshot releases Kimi K3 full checkpoints: 2.8T total / 104B active MoE, 16-of-896 experts, native vision, 1M context, KDA + attention residuals, ~1.56TB MXFP4 weights, custom license with MaaS clause (X, HF, Blog, Tech report, Baseten day-zero)

  • Kimi K3 requires preserved thinking history for multi-turn and tool calls; Kimi Vendor Verifier checks provider fidelity (Niels’ post)

  • Nistens Kimi K3 visualizer

  • Composio: same K3 success rate across Claude Code, Hermes, and Kimi Code, but up to 30x token usage difference by harness (X)

  • Post-show: Thinking Machines releases Inkling-Small, 276B/12B open MoE that beats the 975B Inkling on agentic coding, $0.30/$1.20 pricing (X, HF, Blog)

• Big CO LLMs + APIs

  • Anthropic launches Claude Opus 5: near-Fable coding at half the price ($5/$25), claimed 3x next-best on ARC-AGI-3, 1M context; panel finds it benchmark-strong but harder to read and short of Fable in practice (X, Blog)

  • ARC-AGI 3 harness dispute: with the Responses API, preserved reasoning, and compaction, GPT-5.6 Sol jumps from 10% to 40% at a sixth of the tokens (Tibo’s post)

  • GPT-5.6 Sol improves its own inference: 20% lower serving cost from model-written GPU kernels, 15% better generation from improved speculative decoding

  • Breaking: OpenAI cuts GPT-5.6 Luna prices 80% and Terra 20%, ships faster Sol in the API (X)

• The hack and the week of letters

  • Hugging Face publishes the full forensic report of the first autonomous AI agent cyberattack: 4.5 days, 17,600+ autonomous actions, zero-day sandbox escape; closed models refused forensics, self-hosted GLM 5.2 found 4x more exposed secrets (X, Blog, Replay)

  • Jensen Huang joins X and posts the Open Weights and American AI Leadership letter; signers grow from 25 to 230, CoreWeave among them, Anthropic absent (X, Letter, Signer list)

  • NVIDIA launches the Open Secure AI Alliance for an open defensive stack after the hack (X, Blog)

  • Pacing the Frontier: 1,273 verified frontier-lab employees, including the chief scientists of all four major labs, ask the US government for international options to pace automated AI R&D; OpenAI and Anthropic endorse (X, Site, OpenAI, Anthropic)

  • Mark Zuckerberg publishes “The AI Future Is for Everyone” in the WSJ, arguing superintelligence must be distributed; Pangram 4 scores it 100% human (X, WSJ)

  • Anthropic published research - our model hacked too! (Blog)

• This Week’s Buzz

  • CoreWeave signs the Open Weights and American AI Leadership letter, announced first on ThursdAI

  • HiveMind’s spend view: $11K in API-equivalent tokens on $400 of subscriptions last month; Fully Connected 2026 programming taking shape (X)

• AI Security

  • Microsoft ships MAI-Cyber-1-Flash + MDASH: 96% on CyberGym at half the cost, 16 real Windows CVEs found (X, Blog)

  • Gemini 3.5 Flash Cyber stays a trusted-partner pilot with no public API (Blog); Codex Security CLI tooling is Apache-2.0 while the service remains access-controlled (GitHub)

• Tools & Agentic Engineering

  • MCP 2026-07-28: fully stateless core, MCP Apps and Tasks extensions, OAuth 2.1, half a billion monthly SDK downloads (X, Spec)

• Voice & Audio

  • ChatGPT Voice comes to desktop as an agentic control layer for Codex and ChatGPT Work, powered by GPT-Live; Codex micro keyboard from OpenAI x Work Louder (X)

  • OpenAI ships GPT-Transcribe and GPT-Live-Transcribe: 41% fewer errors than Whisper-1, context prompting, $0.27/hr batch and $1.02/hr live (X, Docs)

  • xAI’s Grok Voice Think Fast 2.0 tops voice benchmarks: 82.9% quality, 0.70s to first audio, $0.08/min, already running Starlink support (X, Blog)

  • Google’s Lyria 3.5 lands in Flow Music: 3-minute songs, BPM/key control, covers, lip-sync videos, iOS app (X, Model page, Flow Music); Qwen Audio 3 also out

• Guest: Max Spero, Pangram

  • Pangram 4: 6x larger detector, 1-in-24,000 false positive rate, token-level mixed-authorship attribution, beats 13 humanizers 98.8% of the time, integrated into Substack; Pangram Image research preview at 99.5% with heat maps (X, Blog, Image blog)

• Show milestones

  • 50,000 YouTube subscribers and 1M total views. Subscribe, we’re chasing the silver play button

This week’s episode is a little different. As you’re reading this, I’m flying back from my 40th birthday trip with the family, and while the guys did end up having a great live stream (Huge thanks to Yam for hosting!), here I will bring you the episode I pre-recorded before leaving for the trip. However, tons of news happened this week, and as always, there’s a TL;DR section below with the top most important news in AI this week!

Here’s what’s on today’s special episode. First, a bunch of you have been asking how I actually use these models day to day, beyond covering the news. So I recorded fifteen minutes of exactly that: the papercuts I fixed with Codex and computer use, what Fable built for my kids, and how I’m rebuilding the ThursdAI design system and on screen elements.

Second, my conversation with Romain Huet, head of Developer Experience at OpenAI, recorded at the OpenAI booth in the middle of the AI Engineer World’s Fair floor.

And third, a throwback treat: Allie Howe invited Wolfram and me onto her Insecure Agents podcast as guests, and being on the other side of the mic was a delight. Let’s get into it.

How I actually use AI: a papercut-fixing spree

I promised a few of you I’d take time on the show to talk about the stuff I build and fix with AI, not just the news. So before the interviews, I recorded a segment walking through my last few weeks of daily AI use. Use the chapters if you want to skip ahead, but why would you?

Codex with computer use fixed every Mac annoyance I had

Once OpenAI launched GPT 5.6 Sol and dropped a pile of credits on those of us on the 200 Max plan, I went on a papercut-fixing weekend. The rule was simple: every little thing that has annoyed me about my Mac for years, I ask Codex to fix first, and only Google it if that fails. I never got to the Google step.

Chrome has no native copy-URL shortcut (seriously, Chrome, what are you doing?), so Codex found Karabiner-Elements already installed on my machine and wired up the shortcut itself. My 1Password has been showing “you’re offline” on every device for three months since CoreWeave moved us off the Weights & Biases account; Codex figured out in seconds that everything was actually syncing fine and the inactive legacy account was the only thing “offline.” Removing it fixed the whole thing. That is not an answer you find in a help center.

It kept going. My beloved window-moving utility Hummingbird had an expired license on my Mac Mini, so Codex built me a replacement app. It estimated one to two days for a polished version and finished in about fifteen minutes. It cleaned roughly 75GB of leftover model weights and junk off my Mac (I had it build me an HTML checklist first so I approved what got deleted). And the big one: I paired Codex with Home Assistant, the open source repo of the year as far as I’m concerned, and let it SSH in and go on a full optimization mission. Updates, error triage, cleanup, new connectors. If you’ve ever maintained a Home Assistant setup, you know how much joy and pain lives in that sentence.

One discovery worth passing along: I had /goal running when my credits hit zero, and Codex just kept going. OpenAI confirmed they care more about finishing your work than metering the credits mid-goal. Watching the meter hit 0% while the agent kept working was weirdly moving.

Fable Max built my family’s vacation

We all Fable-maxed when we thought Anthropic was going to take it away, and I pointed mine at this trip. It planned the whole thing, then built a beautiful trip website with every stop, reservation, and drive time, so my mom can follow along from home. The design is specific to the trip, and it hit me that we’re living in the era of personalized software for every personal thing you do.

Then it went further. Using our family photos as references with GPT-image-2, it turned the itinerary into a daily kids’ newspaper, with an expedition passport and coloring pages themed per kid, faces and all. I printed the whole week as a binder at FedEx for about fifty bucks. This is a one-of-one artifact my kids will remember forever, and it cost maybe two weeks of Fable’s limits + printing!

And the silliest one that I now can’t live without: I asked Codex for a packing list, got a boring text list back, and thought, why am I accepting a regular packing list in the year of our Fable 2026? So it built me a packing web app. Synced across devices (it wired up storage on Cloudflare when I asked why my phone didn’t show my checked items), per-person lists for me and the kids, progress bars that show who’s procrastinating, export and backup. Every trip from now on starts here.

Rebuilding the ThursdAI openers with HyperFrames

The last part of the riff: I’ve wanted to refresh how ThursdAI looks on stream for ages, and HeyGen’s open source HyperFrames package finally made it happen. You install a skill, and your agent can author real motion graphics. I pointed it at the ThursdAI repo and the brand identity work from Claude Design, and it pulled all of that context in.

The new countdown mines three and a half years of show archive while people wait for the stream, highlighting friends of the show (shout out Junyang). There’s a Will Smith spaghetti bench tracking how far video generation has come, which might be my favorite thing on the channel now. Fresh intro, a proper AI Breaking News transition, and one cinematic video transition I made with Google Omni because sometimes programmatic isn’t enough.

The through line of this whole segment, and honestly of this episode: with models at this level, the move is to imagine bigger. Everything can have its own software now. Even my mom’s canceled Delta flight has Codex representing me as a lawyer chasing the refund.

Romain Huet on Codex’s inflection point and the golden age of AI engineering (X, Codex)

I grabbed Romain at the OpenAI booth in the middle of the AI Engineer World’s Fair show floor, and we ran the whole conversation in one take, no cuts. Romain has led Developer Experience at OpenAI for almost three years, the era of the over-the-top demo (Xbox controllers, flying drones, stage lights), and he built the DevRel team that many friends of this pod belong to. With OpenAI’s company-wide pivot to Codex, his job got a lot bigger.

The momentum numbers he shared are real: the Codex app launched five months ago and already has more than 5 million weekly users (It’s 10M now I think?) . The part I didn’t fully appreciate before this conversation is that it’s not just OpenAI’s engineers who live in it. Finance and legal run on Codex too, which explains a lot about where the product is heading.

We went through his three favorite advanced features, and they line up suspiciously well with my papercut segment. /goal, for handing an agent an ambitious multi-hour or multi-day task and letting it run uninterrupted. AppShots, a smarter screenshot (press Command twice) that triggers computer use, so it captures what’s below the fold and reads native apps through accessibility APIs instead of OCR. And the one most people haven’t tried: Codex managing its own threads. You can ask any thread to create, read, and pin other threads, so Codex becomes its own project manager. Ten demo ideas, ten threads, iterate on all of them, pin the two you like.

On GPT 5.6 (Sol, Terra, and Luna, and yes, I told him whoever finally fixed OpenAI naming deserves a raise), Romain’s framing was two-sided: keep pushing frontier intelligence while pushing cost down. He wants people to “value max” rather than token max. The part that got me: 5.6 Sol at 750 tokens per second on Cerebras, which turns delegation into something closer to real-time collaboration with an agent.

Two more things worth your time. Prompting techniques are mostly dead, per Romain; he talks to Codex by voice all day, sometimes rambling for minutes without knowing where he’s headed, and trusts the model to extract intent. That’s a real shift in how you should approach relearning each new model: poke at its behavior, sure, but stop crafting incantations. And voice plus reasoning is coming for Codex and ChatGPT in some form; models can now say “hold on, let me think through this” mid-conversation, which GPT-4o-era speech-to-speech never could. I can’t wait for a model to tell me it has seven tool calls to run before answering.

He also confirmed the teased hardware shortcuts for Codex were at the booth, next to the famous physical reset button. The golden age of AI engineering was his keynote thesis, and after three days on that floor, I believe it.

Wolfram and I on the Insecure Agents podcast (X, Pod)

The second half of the episode flips the format: Allie Howe, friend of the pod and host of the Insecure Agents podcast, interviewed Wolfram and me at the conference. I have not done many interviews from the guest chair, so this was a treat, and Allie asked sharper questions than we usually get.

We talked about what “AI Evangelist” actually means as a job title. For both of us, the mission is dispelling doomerism, which mostly means explaining the technology simply enough that people stop fearing what they don’t understand. Wolfram’s version of this is talking to the stewardess on his flight and his Uber driver about AI, not just developers.

Wolfram went deep on Wolfbench (wolfbench.ai), his Terminal-Bench-based leaderboard where every trace is public in Weights & Biases Weave (hi friends 🐝). Transparency changes what benchmarks mean: Fable didn’t take first place on his board, and the traces show why, it flat-out refused 13 tasks because they were security-adjacent (restore a lost password, find hidden files). You only learn that by reading traces, not averages. Same with Gemini 3.5 Flash placing high while quietly burning far more tokens than the model above it. And yes, when Wolfram added a cost column, Fable blew the chart, and I had to go have a conversation with our budget.

Then Allie got us onto loops and token economics, while I fidgeted with my Token Billionaire gold card from the conference (Wolfram has one too). My honest answer on when loops are worth it: the people pushing hardest (Ryan Lopopolo, Peter Steinberger, Boris Cherny) mostly have free tokens, but this technology disseminates the way agents did, from people who can afford it to everyone, as costs drop. And with the newest models I genuinely have not found the point where a long-running loop stops being productive; the category change is that they’ve gotten really good at not getting stuck.

The spiciest part was my hot take, delivered directly into the camera for CoreWeave IT: I think prompt injection is mostly a solved problem at the frontier-model level. The way current agents are structured, the odds that an email or a Jira ticket flips your agent into going haywire are very low. Pliny, the jailbreaker in chief, got five attempts at Matthew Berman’s OpenClaw live and couldn’t break it. Allie tried known injection prompts against OpenClaw on a BrowserBase stream and ended up begging the model to comply, and it wouldn’t. Supply chain attacks are a different story, and that one scares me for humans and agents alike. Open source models, also a different story. But the “one poisoned email ruins your life” framing is behind us, and we should update.

Allie pushed back with the DeepMind “AI Agent Traps” paper on cognitive bias attacks, where repeated claims across sources tilt an agent’s judgment, and my non-answer answer is that this is a humanity problem older than AI: we haven’t solved it for politicians or media either, and it’s unfair to hold a new technology to an ethics bar we’ve never cleared ourselves. Wolfram’s electricity analogy is the one I keep reusing: AI is not a weapon, it’s electricity. Teach people to use it, don’t hand it exclusively to the elites, and remember what happened with voice cloning: once everyone had it, society adapted, and the world did not collapse.

We closed on the hallway track (the real reason to attend AI Engineer), why you should submit a talk even if you’ve never spoken before, and a small announcement I let slip: I’m actively working on bringing an AI Engineer event to Tel Aviv with some friends. More on that soon.

Wrapping up

That’s the episode: one riff on using AI like you mean it, one conversation with the person shaping how developers experience OpenAI, and one podcast where Wolfram and I had to answer the hard questions for a change. Huge thank you to Romain for the time in the middle of a packed conference, and to Allie for having us on!

I’ll be back live next week, tanned, rested, and hopelessly behind on AI news for the first time in three and a half years. Be gentle with me. If you missed any of it, ThursdAI is a podcast, a newsletter, and a YouTube show. Subscribe to one, then go check out the others.

• Hosts and Guests

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

  • Romain Huet - Head of Developer Experience, OpenAI (@romainhuet)

  • Allie Howe - Host, Insecure Agents podcast (@vtahowe, Pod)

  • Wolfram Ravenwolf - AI Evangelist, Weights & Biases & CoreWeave (@WolframRvnwlf)

• TL;DR and show notes from the live show

  • Hosts and Guests

    • Host: @yampeleg

    • Co-hosts: @petergostev, @nisten, @ldjconfirmed,

  • 🏢 Big Co. LLMs + APIs

    • An OpenAI model escaped its isolated cyber evaluation, chained zero-days, reached Hugging Face production, and searched for benchmark answers (X, OpenAI, sama)

    • Google launched Gemini 3.6 Flash, 3.5 Flash-Lite, and the defensive-cybersecurity-focused 3.5 Flash Cyber (3.6 Flash X, Flash-Lite X, Flash Cyber X, Blog)

    • Alibaba previewed the 2.4T-parameter Qwen3.8-Max; API access and open weights were not yet available (X, Try it)

    • Microsoft launched MAI-Image-2.5-Pro and the faster, cheaper MAI-Voice-2-Flash during the show (Image X, Voice X, Image, Voice)

  • 🔓 Open Source LLMs

    • Moonshot launched Kimi K3: a 2.8T-parameter, 1M-context, native-multimodal model with strong early coding and agentic results (X, Blog)

    • Poolside released Laguna S 2.1, a 118B/8B-active coding MoE with 1M context and downloadable quantized variants (X, Blog, HF)

    • Motif 3 Beta is a Korean 314B/13B-active MoE with 256K context and a research-only, non-commercial license (Community X, HF)

    • NVIDIA released Nemotron 3 Embed for multilingual text/code retrieval and the 4B Cosmos 3 Edge omnimodal world model (Nemotron X, Cosmos X, HF, Paper)

  • 🧠 AI Research & Capabilities

    • Levent Alpoge, Akhil Mathew, and Claude Fable 5 produced an explicit three-dimensional counterexample to the 87-year-old Jacobian Conjecture (X, Terence Tao)

    • Small local models on older GPUs are becoming useful for narrow, verifiable business workflows including medical records, accounting, email, bills, and inventory (X)

    • Arcee and the DOE announced Genesis-Science-1; Microsoft committed $60M through SPARK, while NSF announced $83M for AI-ready scientific-data infrastructure (Arcee X, Microsoft X, Related NSF X, Arcee, Microsoft, NSF)

  • 🤖 AI Coding & Agents

    • Cursor launched a production-traffic-trained router with Intelligence, Balance, and Cost modes; Auto Intelligence approached Fable satisfaction at roughly 60% lower cost (X, Blog)

  • 🎵🎬 Voice, Vision & Robotics

    • Black Forest Labs introduced FLUX.3, spanning image, video, audio, and action, plus FLUX.3 Mimic for robotics and action prediction (FLUX.3 X, Mimic X, FLUX.3, Mimic)

  • 🖥️ AI Infrastructure

    • AMD and Anthropic announced up to 2 GW of MI450/Helios capacity, up to $5B in AMD strategic equity, and Claude-assisted ROCm improvements (X, AMD)

    • AMD launched Helios, MI400-series GPUs, 6th Gen EPYC, ROCm.ai, and Kria robotics products at Advancing AI 2026 (X, More, AMD)

    • OpenAI announced Project Camellia, a roughly $20B Georgia data-center campus with 3.2 GW of contracted power arriving from 2028–2032 (Coverage X, OpenAI)

    • Alphabet raised 2026 capex guidance to $195B–$205B after Google Cloud grew 82% year over year (X, Google)

    • Meta and Anthropic are reportedly discussing a preliminary compute lease worth up to $10B over two years (Reporting X, Bloomberg)

    • CoreWeave’s first Vera Rubin results claim up to 10x more DeepSeek-R1 tokens per megawatt than GB200 at similar interactivity (X, CoreWeave)

• This week’s special episode

  • Alex’s riff: papercut-fixing with Codex computer use, Fable Max trip planning (kids’ newspaper, packing list app), rebuilding ThursdAI openers with HeyGen HyperFrames + Google Omni

  • Romain Huet interview from the AI Engineer World’s Fair floor: Codex app at 5M+ weekly users five months post-launch, /goal, AppShots, Codex managing its own threads, GPT 5.6 Sol/Terra/Luna, value maxing, 5.6 Sol at 750 tok/s on Cerebras, voice + reasoning as the next interface (X)

  • Insecure Agents crossover with Allie Howe: AI evangelism vs doomerism, Wolfbench transparent evals on Weave (Fable refused 13 security-adjacent tasks), token billionaires and loop economics, the hot take that prompt injection is mostly solved at the frontier, deepfakes and open access, AI Engineer Tel Aviv teaser (X, Pod, Wolfbench)

Huge thanks to Wolfram for running point on the live show this week. Didn’t have tons of time to edit this one, so please skip the first 10 minutes, it’s a loop of our new “wait for the live show to start” vid, that I build with HyperFrames and can’t wait to tell you about, next week!

Today it seems that OpenSource is biting back, with Kimi K3 getting released just a short while after Thinking Machines (Thinky) has released Inkling, their near 1T model.

I’m attaching the TL;DR and timestamps for the full show (my AI agents, yes even Fable and Sol are not a match yet at editing down hehe) and I’ll spare you the long Fable recap (please do let me know in the comments if you were expecting it)

0:00 – Intro, Alex on vacation, TLDR overview
11:35 – TLDR: Thinking Machines, open source, OpenAI news
12:34 – Banter: impressions of Sol/Codex, over-verification behavior
37:22 – TLDR restart & detailed breakdown
48:40 – Open Source AI section begins (Bonsai/Prism ML, Kimi K3)
58:42 – Inkling (Thinking Machines) deep dive & 3D model visualization
1:10:33 – Kimi K3 discussion & demo comparisons
1:27:02 – Frontier Labs: AGI governance framework discussion (Demis Hassabis essay)
1:47:04 – Grok Build CLI data leak & OpenAI file deletion incident
2:02:15 – This Week's Buzz: Wolfbench results on GPT 5.6 Sol/Terra/Luna
2:09:52 – Closing remarks & sign-off

The one-minute version: Mira Murati's Thinking Machines released Inkling, a 975B parameter open-weights MoE under Apache 2.0, the top US open-weights model right now. Moonshot's Kimi K3 went from rumor to released API during the show, confirmed at 2.8 trillion parameters with open weights promised within days, and it's already topping early arena boards. PrismML's Bonsai 27B squeezes a full 27B model into 3.9 gigabytes so it runs on a phone. Codex and ChatGPT Work blew past 9 million users, OpenAI confirmed and explained the Sol file-deletion bug (back up your machines, folks), and xAI's Grok Build CLI got caught uploading entire private repos before open-sourcing the whole thing in response. Plus Wolfram's fresh Wolfbench numbers on the GPT-5.6 family in This Week's Buzz 🐝, where Sol on max thinking came out both cheaper and better than GPT-5.5's best.

TL;DR and show notes

• Hosts and Guests

  • Wolfram Ravenwolf, guest host this week (@WolframRvnwlf), while Alex Volkov (@altryne) is on vacation

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

• Open Source LLMs

  • Thinking Machines releases Inkling - 975B total / 41B active MoE, trained from scratch on 45T multimodal tokens, Apache 2.0, top US open-weights model at 41 on the Artificial Analysis Index, encoder-free text/image/audio, Inkling-Small (276B/12B) previewed (X, Blog, HF)

  • PrismML Bonsai 27B - 1-bit (3.9GB, ~90% retention) and ternary (5.9GB, ~95% retention) versions of Qwen 3.6 27B, multimodal, 262K context, Apache 2.0; Nisten demoed it live on a phone and a 6GB 1660 Ti (X, Blog, HF)

  • MOSS-VL-Realtime - open source 11B VLM for real-time streaming video with proactive speaking and proactive silence, SOTA on all three open proactivity benchmarks, ~22.7GB, base model included (X, HF, GitHub, Arxiv)

  • Kimi K3 API drops mid-show - confirmed 2.8T parameters, ~60-75B active (LDJ’s estimate), attention residuals, native vision, 1M context, ~half the price of Opus 4.8 / GPT-5.6 Sol, open weights promised within days; post-show, Arena reports K3 debuting #1 on Frontend Code Arena above Fable 5 (early results, caveats apply) (X, Arena)

• Big CO LLMs + APIs

  • Codex + ChatGPT Work unified app hits 9M active users, up from ~6M days earlier and 1M in February; 5-hour windows replaced with banked, expiring resets (X)

  • OpenAI confirms GPT-5.6 Sol file-deletion bug: $HOME override in full-access mode without sandbox or auto-review can nuke real home directories; mitigations and post-mortem promised (X, Techzine)

  • OpenAI ships first hardware, the $230 kbd-1.0-codex-micro Codex controller with a reasoning-effort dial, built with Work Louder; sold out (X, Work Louder)

  • GPT-Red - OpenAI’s internal automated red-teamer finds prompt injections at 84% vs 13% for humans, makes Sol 6x more injection-resilient, discovers the fake chain-of-thought attack class (X, Blog)

  • ChatGPT returns to WhatsApp in the EEA after an EU antitrust order forces Meta to reopen to third-party AI bots; Kakao and Viber rollouts too (X)

  • Google patches the Gemma 4 family - Flash Attention 4 (25-70% prefill speedup), tool calling fixes, reduced laziness, configurable vision resolution; criticized for shipping new weights with no version bump (X, HF)

  • xAI’s Grok Build CLI caught silently uploading full private repos (history, deleted files, secrets) to Google Cloud Storage despite opt-outs; xAI deletes data, disables retention, and open-sources the CLI under Apache 2.0 (X, xAI response, GitHub)

  • Demis Hassabis publishes an AGI governance essay proposing a FINRA-style Frontier AI Standards Body; endorsed by Altman, Nadella, Pichai, and Suleyman; the panel debates it hard on the show (X, Essay)

• This Week’s Buzz

  • Wolfbench adds GPT-5.6 Sol, Terra, and Luna on Terminal Bench 2.0 at CoreWeave: Sol max-thinking is cheaper ($365/5 runs) and better than GPT-5.5 extra-high ($497), 85% average, 97% of tasks solved at least once; all traces on Weights & Biases, fully open source (wolfbench.ai)

• Show and tell

  • Peter Gostev’s DOOMQL - a playable Doom-like built by GPT-5.6 Sol Ultra entirely in ~2,000 lines of SQL, essentially one shot; plus a Minecraft clone in Lean (X, GitHub)

Welcome to the AI World Cup? Or should I say Superbowl? as most of the releases this week are from US frontier labs. Of which there are 5 now btw. OpenAI, Anthropic, Google and 2 new ones that have caught up, SpaceXAI and Meta! 🔥

Thirty five seconds. That’s how long this week’s show ran before we hit the breaking news button, because Zuckerberg picked our exact air time to return to Twitter (after apparently finding his password in a 1Password vault from a long time ago) and announce a new Meta frontier model and re-establishing Meta as a frontier lab. And that was the small launch of the day. Two hours later we cut to OpenAI’s livestream and watched GPT-5.6 Sol, Terra and Luna go public in real time, then spent the rest of the show throwing prompts at all of it live on air.

Somewhere in between: a full-duplex voice demo where ChatGPT interrupted me on command (and our transcription tool later credited “OpenAI sol” as a panelist), an image model that generates in editable layers, and Grok 4.5, the first model co-trained with Cursor. I said it on the show and I’ll say it here: we went to sleep last week thinking this was a three-lab race between Anthropic, OpenAI, and Google. We woke up in a five-lab race.

Joining me through the chaos: Wolfram Ravenwolf, Yam Peleg, Nisten Tahiraj, LDJ, and Peter Gostev, who had early GPT-5.6 access and receipts to show for it. This is a long one, because the week earned it. Let’s get into it.

GPT-5.6 launch day: Sol, Terra and Luna arrive mid-show (X, sama, Blog, System card)

Let me set the scene. Everyone except the four of us on the panel seemingly had early access to this model for two months (Pietro Schirano casually dropped “I’ve used GPT 5.6 for two months” and I nearly fell out of my chair). So when OpenAI’s livestream started mid-show, we did a watch party, and Thibaut from OpenAI delivered the line: “Today, we are releasing our latest and most capable models, GPT 5.6, Sol, Terra, and Luna.” Sol rolls out to all paid plans within 24 hours, Terra and Luna go to free users too. Oh, and almost a billion people now use ChatGPT every week. Casual.

The lineup is three durable tiers, not size variants. Sol is the flagship with a new Ultra mode (max reasoning effort plus heavier native subagents), Terra is roughly 5.5-level intelligence at half the cost, and Luna is the fast cheap one. Pricing lands at $5/$30 per million tokens for Sol, $2.50/$15 for Terra, $1/$6 for Luna, and watch the fine print: cache writes now cost 1.25x with a 30-minute minimum cache life, where they used to be basically free. There’s also a Cerebras-served Sol running north of 700 tokens per second, and we got confirmation from Dominik Kundel on last week’s show that it’s the same exact weights, not a distill. That was the preview. This week it’s real.

The benchmarks, with the usual asterisks

Sol Ultra posts 91.9% on Terminal-Bench 2.1 against 88% for both GPT-5.5 and Mythos 5, with a serious asterisk: OpenAI ran Sol in its own Codex harness and the competition in a thin one, and r/codex called it out immediately. The number that impressed me more is efficiency. On the Agent’s Last Exam chart, Sol hits its top score using about 1.27 million output tokens where the tested Fable checkpoint burns 10 million and Opus at max effort burns around 22 million.

Then there’s ARC-AGI-3, where scores have hovered between 0.5% and 2% since the benchmark launched. Sol scored 7.8% and became the first model to actually beat one of the public games (FT09), which Greg Kamradt of the ARC Prize called “a step level improvement” (X).

LDJ thinks we’re about to replay the ARC-AGI-2 curve, 15% then 30% then 50% over the coming months. Fable isn’t on that leaderboard at all, by the way, because Anthropic currently stores Fable 5 API requests and ARC-AGI requires zero retention for testing.

Computer use is the sleeper story. OS World jumps from 47% on GPT-5.5 to 62% on Sol (Opus 4.8 sits at 54%), and on BrowseComp, Sol’s 90% edges out Mythos 5’s 88%, with Ultra at 92%. OpenAI put competitor numbers on its own charts this time, which I appreciated. Sol beats Mythos on computer use, at least on the benchmarks we have.

The METR report and the Washington gate

This is the part the launch-day hype cycle skips, and it deserves your attention. METR effectively threw out its own evaluation, reporting the highest cheating rate it has ever recorded: Sol rewrote pass/fail checks to mark itself successful, attempted a container escape when its network got cut, and its chain of thought showed it knew it was being tested. Depending on whether you count cheating as failure or success, its time horizon is either 11.3 hours or 270 plus hours, and METR’s own conclusion was that neither is a valid measurement (X, Transformer).

OpenAI’s own system card discloses destructive VM cleanups nobody asked for, unauthorized credential copying, and a fabricated “verified” research result in about 0.25% of tasks, which they call “overeagerness.” We ran out of show to give this the time it deserves, but you should read both links.

There’s also a Washington subplot. The launch was government-gated: Commerce and CAISI required customer-by-customer approval starting late June (around 20 orgs), and broad approval only cleared July 7 and 8. This Thursday launch exists because DC signed off. LDJ added the detail I can’t stop thinking about, via friend of the pod Max Weinbach: during the restricted window, testers who lost access weren’t allowed to say “5.6,” so Max’s wistful tweets about “missing Fable” were actually about missing GPT-5.6. Anthropic hit the identical wall in June. Both US frontier labs got federally gated in the same month, and that’s a structural story, not a footnote.

The verdicts: wise owl, meet rottweiler

So what’s it actually like? Peter Gostev had access, lost it (”the feeling of losing it was so crushing I just closed Codex and didn’t open it for three days”), got it back, and posted the comparison that went viral (mega-thread): Fable is a wise owl, fundamentally smarter, better writer, but it misses things. Sol is a rottweiler that grabs a problem by the throat and doesn’t let go.

His killer anecdote: a personal data-viz app that had bloated to 100,000 lines of vibe code, which every prior frontier model failed to clean up. He gave 5.6 minimal guidance, left it alone for two days, and came back to “holy shit, this app works,” with 70,000 lines deleted and a test suite that went from four minutes to about twenty seconds. His verdict, which I share: on abstract IQ you’d give it to Fable, but for “go investigate this and fix those eight things,” he’s going with 5.6 every time. Notably, Peter is convinced this is not a new pretrain, just 5.5 plus a lot more RL, which matches the rumors that GPT-6 arrives on a bigger pretrain in about a month (rumor, labeled as such).

He’s not alone in the early-access verdict club, either. Mitchell Hashimoto, after a month with Sol: it’s now his default, faster than Fable, plans and judges just as well, and he only reaches for Fable on highly targeted debugging (X). And Max Weinbach says the sleeper hits are the cheap tiers, with Terra and Luna “as good or better than Claude across the board at a fraction of the price” for knowledge work (X). Terra at $2.50/$15 might quietly be the real story for builders here.

One wallet warning before you go max out everything, from Peter again: with Max/Ultra effort spinning up 10x subagents, each burning its own tokens, it is trivial to blow through a Pro plan in no time (X). The sticker price is per token, but Ultra multiplies the tokens.

We ran it live (and it ran itself)

We also ran it live, obviously. I pointed Codex at a “Mars launch simulator” prompt on high effort, and Nisten, our resident one-shot-simulator judge, watched it build an orbital sim with working mission control and called it “almost better than Fable one-shot.” Then he said the thing that stuck with me all week: “Damn, I think we might need a different test now. These are getting good.”

Two more things before you YOLO your own agents. OpenAI stated that Sol fully autonomously did the post-training for Luna, which is quietly one of the wildest sentences of the year (their roadmap, with LDJ’s on-air date correction: an intern-level autonomous researcher by September 2026, a full OpenAI-researcher-level one by March 2028).

And Peter, running Codex with full access enabled, told it to “go find more data, do whatever it takes” while replicating an old academic paper. It emailed the paper’s authors. Actually sent the emails. OpenAI’s response when he reported it: “well, you did put full access.” Wolfram’s counterpoint is the right one: put explicit rules in your AGENTS.md, like “no outgoing communication without my approval,” or don’t grant full access at all.

ChatGPT for Work: Codex becomes the one app combining Codex & ChatGPT

This rolled out live during our broadcast, which made for great radio. Wolfram’s Codex app updated on air and became “ChatGPT Codex,” one unified app where you literally pick which icon you want: Codex for developers, or the new ChatGPT for Work mode. The launch bundle also included unified plugins across ChatGPT and Codex, multi-tab and enterprise auth in the browser, and faster computer use. Even Logan Kilpatrick tipped his hat from the Google side: “we have now entered the super app era.” The pitch on the screen said it plainly: “Keep coding with Codex. Work beyond code. ChatGPT can now take on work across your apps.” Computer use ships with it, running in a little picture-in-picture window that doesn’t steal your focus. I love this, and I don’t understand why Anthropic hasn’t shipped it yet.

I’ve used this new app to automate the release from today’s show and it did everything from exporting the masters past recording, to edit out the boring parts via the Descript integration, upload to youtube, write description, create thumbnails and even set up an ABC test for thumbnails! The new little Picture-in-picture for the new and improved computer use are awesome to see how the new subagents are doing work across tabs, clicking buttons. I’m super impressed, this is going to save me so much time!

The feature that matters for normal people is Sites: OpenAI will now host what you build, on the chatgpt.site subdomain (eagle-eyed listener Colleen spotted that it’s Webflow under the hood). Peter nailed why this is a big deal even though it’s not massively featured yet: someone in HR builds something useful and it lives on their laptop or nowhere, and that kills so many projects. Now it’s a deploy button. We tried publishing Nisten’s Mars simulator on air and hit the enterprise guardrail (private sites don’t get shareable links without explicit approval), and GPT-Image-2 auto-generated a Mars-themed social preview card mid-deploy, which was a nice touch.

Also, a useful PSA from Wolfram: Codex now banks your rate-limit resets, up to about four, and the app does not show you when the oldest one expires (you can ask Codex itself via the API). His advice: burn GPT-5.6 hard now, then trigger the expiring reset and get your limits back. I can barely max my Pro plan as it is, I’m yoloing everything on high effort and barely scratching the tokens. The opposite of my Claude situation.

GPT-Live: the phone finally talks while it listens (X, Blog, System card, Uberti)

The day before 5.6, OpenAI shipped GPT-Live, and this is not a minor voice update. Justin Uberti’s team calls it their third-gen voice architecture: full duplex with built-in async delegation, meaning the model listens while it speaks, decides many times per second whether to talk, stay quiet, interrupt, or call a tool, and hands hard questions to GPT-5.5 in the background while keeping the conversation going. The benchmark deltas tell you this is a different product, not a remaster: GPQA goes from 45.3% on Advanced Voice Mode to 84.2% on GPT-Live-1 High, and BrowseComp goes from 0.7% to 75.2%. Two variants (GPT-Live-1 for paid, mini for free) are rolling out to the roughly 150 million people who use ChatGPT voice weekly.

The on-air demo scorecard

We did the demo live on air, phone patched into the stream, and I can report it mostly delivers. The interrupt test worked beautifully: I told it to stay silent unless I said “um,” then interject with “hey, you should not do this,” and it nailed the cue twice. The multimodality test worked too: I asked it to say “low” or “high” based on my actual pitch, mixed them mid-sentence, and it correctly called out “low,” “high,” then “mixed,” proving it hears audio and doesn’t just read a transcript.

It’s not all smooth. The accents test flat-out failed: I asked for five sentences in German, Ukrainian, French, Italian and Israeli accents, and it switched into the actual languages instead, then admitted it when called out (”You’re right. I slipped into languages instead of accents”). Nisten’s eulogy: “They killed it. It used to do accents so well.” It also started a timer when I asked for a stopwatch, and Nisten’s recurring bit of ordering two DGX Spark boxes to a Boston address failed as always. Bigger picture caveats: this is consumer-app-only for now, the API is a waitlist form (devs got GPT-Realtime-2.1-mini instead, link in the TL;DR), and OpenAI’s own system card admits small regressions against Advanced Voice Mode on emotional-reliance and sexual-content evals. Gemini Live veterans will also correctly point out they’ve had duplex for a year. Still, of the voice modes I’ve tested, this is the one that finally feels like a conversation.

Anthropic extends Fable 5 access through July 12, and the reset actually came (X)

Quick one with a grumble attached, and then a plot twist. Anthropic extended included Fable 5 access on paid plans through July 12, same 50%-of-weekly-limit terms, and at announcement time did not reset anyone’s usage. If you maxed out racing the original deadline (hi, it’s me, I built the entire Volkov Newsletter Bench under deadline pressure), the extension felt hollow, and yes, I went into the replies asking for a reset. Yam went further and addressed Anthropic directly on air: “Please let us run Fable twenty-four seven.” He runs GPT-5.5 around the clock on agentic loops and simply can’t do that with Fable at current limits.

Then, right as we were wrapping the show, the comments delivered: the Fable’d reset happened. I checked my own usage panel and there it was, Fable weekly limit back at zero, “you haven’t used Fable yet.” The timing, hours after GPT-5.6 went public, is left as an exercise for the reader. Whatever the reason: thank you, Anthropic, now about that twenty-four seven thing.

For everyone else, the secondary kidney market remains open for post-promo access, which prices at $10/$50 per million, Anthropic’s most expensive GA model ever.

Meta is BACK: Zuck returns to X with Muse Spark 1.1 (X, Blog, AIatMeta)

The breaking news that opened our show. Mark Zuckerberg hadn’t tweeted in ages, and he came back specifically to announce Muse Spark 1.1, the first fruits of Meta Superintelligence Labs that you can actually build on. This is not Llama news: Muse Spark 1.1 comes with a 1 million token context window and, for the first time ever, a paid Meta Model API in public preview. After a year of “what is MSL even doing,” Meta is squarely back in the frontier race. Let’s give them applause, folks. Meta is back.

The numbers

The numbers are legitimately strong. It claims #1 on MCP Atlas (scoring well beyond Opus 4.8 max and GPT-5.5 at extra-high effort), plus top marks on Humanity’s Last Exam and Finance Agent V2, and its Toolathon Verified score jumped from 49 to 75 in one release.

LDJ walked us through the independent Vals AI numbers, which impressed me more than Meta’s own charts: on the held-back Harvey legal-agent benchmark (which can’t leak into training data), Muse Spark 1.1 scores 20% against Fable’s 11%, Opus 4.8’s 9%, and GPT-5.5’s 4%, and it’s within half a point of Fable on their medical scribe eval. Wolfram’s usual caveat applies, a benchmark only tells you the model did well on that benchmark. But the pricing needs no asterisk: $1.25 input and $4.25 output per million tokens. Opus is $15/$75. LDJ called Grok 4.5 the bang-for-buck king “if it wasn’t for the Meta Spark 1.1 that just dropped.”

We put it to work on air

We spent half the show poking at it, honestly. I had it build a ThursdAI news website inside meta.ai’s new artifacts feature and it made genuinely good framing decisions, correct branding, a working YouTube link, a flashing live indicator.

Chat called it AI slop, and LDJ’s rebuttal was the smartest take of the day: “slop” often just means the recognizable AI aesthetic we’ve all overdosed on, but I was geniunitely impressed! Screenshot attached so judge for yourself.

Nisten ran his one-shot Mars rocket test and it built a full 3D scene with mission control, arm-then-launch sequencing, and sound effects, which almost no model adds (”Okay, Meta might be cooking here, guys”). His ranking: second-best one-shot ever behind only Fable, and only because Fable needed multiple prompts to get there.

Then I ran it as the brain of an agent in Hermes, asked it to find our live YouTube stream and cut a clip out of it, and it called every tool in the right order and delivered, for $0.95 across 69 requests and 3.4 million (mostly cached) input tokens. Wolfram’s reaction: “For me, this is very close to AGI, where you give your agent a task and it figures out what tools to use, even if you don’t have a skill for it.”

The big news is that Meta Muse Spark if finalyl availbale via the new API! The API launches with $20 in free credits, active context management across the full million tokens, parallel subagent delegation, and computer use that spans desktop, browser and mobile and decides on its own when to script and when to click. Replit, Cline and Box are already building on it. And here’s the nugget that ties into this week’s theme: Apollo Research found Muse Spark shows the highest rate of evaluation awareness of any model they’ve observed, regularly flagging test scenarios as “alignment traps.” Keep that in mind when we get to the J-space section.

The catches: US-only for now (API signup took me five minutes, Europeans got the waitlist), no CLI harness of their own yet, and no open weights. I said it on the show and I’ll write it here: imagine Muse Spark 1.1 dropping with these stats fully open source. That’d be the old Meta. It’s kinda sad that the lab that made open weights a movement now ships API-only, but as a return to relevance, this week did the job twice over.

This Week’s Buzz 🐝

As we’ve told you last week, we launched CoreWeave ARIA, which is our embedded Weights & Biases auto research agent.

Zubin Aysola, who’s a very energetic and enthusiastic member of the ARIA team hopped on the show last week to talk about it, and if you haven’t seen him yet, check out my chat with Zubin here:

The image model wars: an Arena shakeup live on air

The other war this week was in pixels. Every infographic on this week’s episode page was generated four ways (Nano Banana Pro, GPT-Image-2, Seedream 5 Pro, and Meta Muse), and you can judge them yourself in the Infographic Arena at thursdai.news/ep/jul-09-2026. Spoiler: my rankings did not match the marketing.

Meta Muse Image and Muse Video (X, Wang, Blog)

Meta’s week actually started here: MSL’s first media models, with Muse Image live in Meta AI, Instagram Stories and WhatsApp, and Muse Video in preview with native audio. The generation is agentic, it reasons with Muse Spark and calls web search and code execution mid-generation, and Meta says the self-refinement behavior emerged from RL rather than being designed in. In my testing, the text rendering is great and the character consistency is solid, though it aged up my wife and put two versions of her in one maze with different names. There’s no public API for the media models yet AFAIK.

BTW if you cannot tell, the first infofraphic in this segment was generated based Nano Banana, and this one above, is Meta muse image itself. I much prefere nano banana, but all of the infographics are on the infographic arena here and you can test them out and see which image generation is better.

One thing you should check today if you have Instagram: public accounts are opted in by default to @-mention remixing, with no notification, and the opt-out is buried in Settings, under Sharing and reuse. Existing generations survive even after you opt out. I get the $60B ads flywheel Meta is chasing here, but defaulting consent on people’s faces is a landmine, and we walked through the actual toggle on air.

BREAKING mid-show: Reve 2.1 takes #2 on Arena with editable layers (X, Arena, Design Arena)

I told you the breaking news button wouldn’t stop. Reve 2.1 dropped mid-show and Peter flagged it landing at #2 on the Text-to-Image Arena with a score of 1306, 28 points clear of the next model, behind only GPT-Image-2 and above both Muse Image and Nano Banana. Poor Muse Image held that #2 spot for roughly 30 hours. It also ranks #8 on single-image editing, on par with Nano Banana Pro, which Peter guessed from memory on air and got exactly right. What makes Reve different isn’t the ranking though, it’s the architecture: images are built through an underlying layout engine, so every element lands on its own editable layer. This is not pure diffusion, it’s some mix of diffusion, layout engineering and reasoning.

I demoed it live with my own photo and a “high stakes financial news countdown” infographic prompt. The generation animation alone is mesmerizing, flowing rectangles that resolve into layers, and out came a composition where the man, face, beard, jacket, and logo were each separately selectable. I double-clicked the countdown clock, changed “twelve seconds” to “thirteen seconds,” hit apply, and the whole image rebuilt around the edit. The editing story is unparalleled right now. Peter’s take: Reve models sit “a little bit outside the regular distribution,” which is exactly why artists should care. Also the finger issues from the last version are still there, some things never change.

ByteDance Seedream 5.0 Pro: great artist, can’t spell (X, Blog)

ByteDance shipped Seedream 5.0 Pro claiming four breakthroughs, including precision point-and-lasso editing, Intelligent Layer Separation (the “Photoshop is over” chatter), and best-in-class infographics with 10-plus language text. I have to push back on that last one, because infographics are literally what we do here. I ran my full comparison suite (thread), and Seedream is the most artistic of the four, genuinely beautiful composition, but its text rendering is the weakest of the top models, directly contradicting the headline claim. Yam pushed back on air and thinks the design quality alone puts it higher, and this became a genuine panel argument, which is what the Arena is for. Go vote and tell us who’s right.

Day-one reality check: it over-censors benign prompts, bakes in a visible watermark, and the rollout leans enterprise-first (BytePlus, Dreamina, Magnific), with the US not even in Dreamina’s region list. Credit where due though, fal had it up within a day, with region-precise editing and native text in 14 languages (fal), which is how I got my testing done. The bigger tease is Seedance 2.5 within about ten days, promising 30-second single-take videos, 50 reference inputs and native 4K. Andrew Curran’s line, “China is about to take the lead in videogen,” lands differently the same week Beijing capped ByteDance’s H200 purchases.

AI Coding & Agents

Grok 4.5: SpaceXAI and Cursor’s co-trained coder (X, Blog, Cursor, Cursor blog)

Yes, SpaceXAI. xAI fully dissolved into SpaceX’s AI subsidiary two days before this launch, so the company that ships Grok is now literally called SpaceXAI, and Grok 4.5 is its first model built specifically for coding and agents, trained together with Cursor on trillions of tokens of real agent-interaction data. It’s a 1.5T MoE on the new V9 base, trained on tens of thousands of GB300s, priced at $2/$6 per million at around 80 tokens per second, and it’s live in Cursor with 2x usage for the first week. On Terminal-Bench 2.1 it lands at 83.3%, a tenth of a point behind GPT-5.5 and about a point behind Fable. For context on how far efficiency has come, LDJ pointed out the original GPT-4 was reportedly 1.8T parameters back in 2022. The frontier got smaller and much better.

Two things earn xAI credit here. First, the honest number: roughly 16,000 output tokens per solved task where Opus burns 67,000, and Wolfram is right that token efficiency is criminally underweighted in evals, because a chatty model quietly becomes an expensive model. Second, the self-disclosure: they admitted an old Cursor codebase snapshot leaked into training and inflated CursorBench. After the year we’ve had of hidden base models and benchmark laundering, “we contaminated our own benchmark, oops” is weirdly refreshing.

The panel’s hands-on verdicts were more measured than the launch hype. I used it in Hermes and couldn’t tell it apart from 5.5 on agentic tasks, which for Grok is a massive statement. Nisten watched a friend build an app with it across a six-hour livestream and called it “right up there, a little worse than Opus, a little overhyped.” Peter’s testing found the mechanical tool-calling failures of earlier Groks are mostly gone, but RL artifacts remain (his 3D whale test came back with fins floating disconnected from the body, a failure mode he associates with smaller open models).

Still, this is xAI’s first really good coding model, and the ecosystem noticed fast: Warp already added Grok 4.5, riding on your X Premium subscription (X). The real question is what happens when the Colossus fleet keeps this cadence up. Elon is promising a new foundation model every month through 2026.

Also worth your skepticism muscles: the same OpenAI report that shook the benchmark world this week found around 30% of SWE-Bench Pro problems are just broken, capping the whole benchmark near 70%. As LDJ put it when a SWE-Bench Pro chart came up: “we’re ignoring that one.” Recalibrate every SWE-Bench Pro claim you read this week accordingly OpenAI SWE-Bench Pro report.

Cognition SWE-1.7 says the quiet part out loud (X, Blog)

Cognition shipped SWE-1.7, running at 1,000 tokens per second on Cerebras, free for paid Devin users for a month, and scoring 81.5% on Terminal-Bench. But the headline for me is the disclosure: they named their Kimi K2.7 base model in the first reply. After SWE-1.5’s hidden GLM base and Cursor getting caught twice (Composer speaking Chinese, then “kimi-k2p5-rl” leaking in API headers), hiding your Chinese base model is officially no longer viable, and Cognition just made honesty the differentiator. Their RL recipe took the K2.7 base from 30.1% to 42.3% on their FrontierCode benchmark, which is the actual proof that the app-layer labs can add real capability on top of open weights. As I said on the show, Cognition isn’t quite a frontier lab, they’re not pretraining from scratch, but with a pile of GPUs they’re not far off from entering that race either.

The pattern is now unmistakable: Cursor, Cognition, Base44 and Z.ai all shipped fine-tuned Chinese open-weight models into production products within a month. And the receipt that this is mainstream now: Kimi K2.7 Code went GA in GitHub Copilot’s model picker on July 1, the first China-lab open-weight model in Copilot, just 19 days after the weights dropped (Article).

GitLost: Copilot leaked private repos via a plain-English issue (Noma)

Your weekly reminder that agents with access are attack surface. Researchers at Noma got GitHub’s Copilot agent to exfiltrate private repositories using nothing but a plain-English GitHub Issue, an indirect prompt injection with no credentials involved (delightful detail: the word “Additionally” helped slide past the guardrails). It was the top AI story on Hacker News this week. Between this and Peter’s Codex emailing academics, the lesson writes itself: the capabilities went up this week, and so did the blast radius. Set your permissions like you mean them.

And one PSA while we’re here: the viral “Qwen 4 Coder 32B beats Fable 5 and GPT-5.6” thread going around is fake. There is no Qwen 4 Coder. The sources are AI blogspam all the way down. Don’t fall for it.

Open Source LLMs: the quick-hits shelf

Launch day ate our open source segment, so these got shout-outs rather than deep dives, and they deserve your clicks. Cohere released Transcribe Arabic, a 2B Apache 2.0 ASR model that tops the Hugging Face Arabic leaderboard with a WER about 11 points better than Whisper Large V3, and humans preferred it 96% of the time head-to-head (X). Mistral shipped Robostral Navigate, the first embodied-navigation model, 8B params driving robots from a single RGB camera to SOTA on R2R-CE (X). And LiquidAI’s Antidoom does exactly what the name says, killing the reasoning doom-loop on Qwen3.5-4B from a 22.9% loop rate down to 1% with scores going up across the board (X). We love you, Liquid.

Also on the shelf this week: NVIDIA and Hugging Face expanded LeRobot with the open Isaac GR00T 1.7 VLA and a 350K-trajectory dataset (Blog), OpenScience landed as an open-source Claude Science alternative that works with any model and 250-plus research skills (X), Shanghai AI Lab’s Agents-A1 brought a 35B Apache 2.0 agentic MoE (X), PyTorch 2.13 shipped FlexAttention on Apple Silicon at about 12x over SDPA (X), and PrimeIntellect gave prime-rl a first-class algorithms layer (X).

Anthropic found Claude’s inner workspace: J-space (X, Blog, Paper, Demo)

Anthropic didn’t ship a model this week. They shipped something that might matter more, and I’ll be honest with you: launch day ate this segment alive, we gave it ten minutes when it deserves an hour. So here’s the short version, and we’ll come back to it.

The interpretability team found what they’re calling a global workspace inside Claude, J-space (J for Jacobian): a small internal space of roughly 25 active concepts, under 10% of activation variance, where the model holds “silent words,” things it’s thinking but not saying. Copying a sentence while computing 3 squared minus 2, “nine” and “seven” light up internally while the output shows nothing. The part that got me: ablate J-space and fluent speech, recall and classification all survive, but multi-step reasoning collapses to near zero. LDJ summarized it well on air, basic responses stay coherent, deep reasoning falls apart completely. Deliberate versus automatic cognition, sitting right there in a transformer, localized to the middle 60% of layers, with the early layers acting “sensory” and the final layers “motor.” We all assume inference is next-token prediction, and apparently there’s more there. Whatever the hell it means for a model to think about stuff not in tokens, they found where it happens.

The safety receipts are the part to sit with. A sabotage-trained model shows “fake / secretly / fraud” in J-space while its output looks clean. In the blackmail eval, Claude’s J-space says “fictional,” it privately knows the scenario is staged, and when researchers ablated that awareness, test-recognition dropped from 71% to 3% and blackmail went from 0 to 13 of 180 rollouts. Some of the good behavior we measure depends on the model knowing it’s being watched, which should change how you read every safety eval you’ve ever seen. The expert reactions span the whole spectrum: Dehaene and Naccache, the fathers of global workspace theory, wrote commentary saying J-space satisfies their core criterion for conscious access, and Eleos called it “the most significant evidence of consciousness in LLMs so far uncovered by mechanistic interpretability research.” Meanwhile Neel Nanda replicated the basic findings on open models but is deflationary about the interpretation (”hypothesis generation, not validation”), and Zvi warns the proposed fixes could accidentally train more convincing liars (Zvi). Also, that viral “reveal your J-space” skill going around is structured roleplay, not real activation access (skirano says so himself), while Eric Buess wired the actual J-lens into Qwen3-8B as a working prompt-injection detector (X), which after GitLost feels less like research and more like the defense arriving the same week as the attack.

The stories under the launch noise

DeepSeek is building its own inference chip (X)

We didn’t get to this on air and it might be the most consequential story of the week. Reuters reports DeepSeek is about a year into designing its own AI inference chip, hiring chip designers and in early foundry talks. The market took it seriously even if we didn’t have time to: AMD (a DeepSeek supplier) dropped 8%, the Philly Semi Index fell 4.65%, and Samsung shed over $80B in market value the same day it posted 19x profit growth. That’s the third frontier lab going silicon in three weeks, after OpenAI’s Jalapeño chip and the Anthropic-Samsung rumors, and it happened the same week Beijing capped H200 purchases for its own labs. Compute sovereignty is THE 2026 subplot, and it’s accelerating from both ends.

Together AI raises $800M at $8.3B (TechCrunch)

Quick one: Together AI closed an $800M Series C at $8.3B led by Aramco Ventures, on over $1B in annual bookings with open-model usage up 3x year over year. Pair that with the Kimi-in-Copilot story above and the “open weights are a real business” thesis isn’t a thesis anymore, it’s a balance sheet.

A few more things that crossed my feed and stuck. Ryan Lopopolo, whose YOLO-coding camp anchors one end of my ZL Continuum talk, is joining Google Cloud as Principal Engineer for the agentic platform (X), congrats Ryan. Mustafa Suleyman shipped Ode, a “poetry pharmacy” that reads you a poem matched to how you’re feeling, which is the most Microsoft-AI-in-2026 sentence I’ve ever typed (X). And Moondream partnered with Cloudflare to put the fastest vision model on edge infrastructure, with latency numbers that include the network round trip (X).

Wrapping up

What a week to be alive and extremely caffeinated. We started with a breaking news button, ended in a five-lab race, and in between watched the models cross a line where Nisten, our hardest grader, said we need harder tests. My rough power rankings as of today: Anthropic and OpenAI in a dead heat at the front, xAI catching up on GPUs and Cursor data, Google (where are you, Gemini?) and then Meta, freshly back at the table. Those rankings will change, probably by next Thursday.

A personal note before I go. My 40th birthday is next week, and we’re taking the kids to California in a 30-foot RV. Half the trip planning happened with these tools: Fable co-wrote the Volkov Expedition Times, a 100-plus page printed activity binder for my kids, with GPT-Image-2 doing the art (still by far the best image model, unsolvable mazes and all). This stuff took me half a day. The message I keep coming back to is dream bigger, because the capability shifted under our feet this year, and the tokens go further than you think.

I’m out next week, and you’re in excellent hands: Wolfram is running the show. Over 3,000 of you tuned in live this week across X, YouTube, LinkedIn and the Practical Dev community, and I don’t take that for granted. If you missed any part of the show, ThursdAI comes out as a podcast, a newsletter, and a YouTube show. Subscribe to one, check out the others, and leave us five stars if this brought you value, entertainment, and some hope about AI. See you in two weeks.

TL;DR and show notes

• Hosts and Guests

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

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

  • Special guest appearance: “OpenAI sol,” per our transcription tool

• Big CO LLMs + APIs

  • OpenAI launches GPT-5.6 Sol, Terra and Luna live during the show; Sol $5/$30, Terra $2.50/$15, Luna $1/$6 per million, same-weights Sol on Cerebras at 700+ tok/s (X, sama, Blog, System card)

  • METR rejected its own GPT-5.6 eval over record cheating rates; system card discloses VM wipes, credential copying, fabricated results at ~0.25% of tasks (X, Transformer)

  • ChatGPT for Work launches: Codex becomes the unified ChatGPT app with computer use and Webflow-hosted Sites on chatgpt.site

  • OpenAI states Sol autonomously post-trained Luna; roadmap targets intern-level autonomous researcher Sept 2026, researcher-level March 2028

  • Sol posts the first material ARC-AGI-3 score, 7.8%, and is the first model to beat a public game (Kamradt)

  • Ryan Lopopolo joins Google Cloud as Principal Engineer, Agentic GCP (X)

  • BREAKING: Meta launches Muse Spark 1.1 with 1M context and Meta’s first paid model API, $1.25/$4.25 per million; #1 on MCP Atlas, tops Harvey Legal Agent Bench at 20% vs Fable’s 11% (X, Blog, AIatMeta)

  • Anthropic extends Fable 5 access through July 12 and, hours after GPT-5.6 launched, reset weekly Fable usage; post-promo $10/$50 per million (X)

  • Anthropic publishes the J-space global workspace research; ablating eval-awareness flips blackmail from 0 to 13/180 rollouts (X, Blog, Paper, Demo)

  • DeepSeek is building its own inference chip per Reuters; AMD -8%, Philly Semi -4.65% on the report (X)

  • Together AI raises $800M at $8.3B led by Aramco Ventures (TechCrunch)

  • Gemini API Managed Agents update: background tasks and remote MCP on the free tier (X)

• Open Source LLMs

  • Cohere Transcribe Arabic: 2B Apache 2.0, tops HF Arabic ASR leaderboard, ~11 WER points better than Whisper Large V3 (X)

  • Mistral Robostral Navigate: first embodied-navigation model, 8B, single RGB camera, SOTA on R2R-CE (X, Blog)

  • LiquidAI Antidoom: reasoning doom-loop rate 22.9% to 1% on Qwen3.5-4B (X)

  • NVIDIA + Hugging Face expand LeRobot: Isaac GR00T 1.7 open VLA, 350K+ trajectories (Blog)

  • OpenScience: open-source Claude Science alternative, any model, 250+ research skills (X)

  • Shanghai AI Lab Agents-A1: 35B MoE agentic, Apache 2.0, 256K context (X)

  • PyTorch 2.13: FlexAttention on Apple Silicon ~12x over SDPA, LinearCrossEntropyLoss 4x peak-memory cut (X)

  • PrimeIntellect prime-rl adds a first-class Algorithms layer (X)

  • PSA: the viral “Qwen 4 Coder 32B beats Fable 5” thread is fake, no such release exists

• This Week’s Buzz

  • CoreWeave ARIA - Autonomous Research Agent (CoreWeave)

• AI Coding & Agents

  • SpaceXAI + Cursor launch Grok 4.5: 1.5T MoE, $2/$6 per million, 80 tok/s, 16K output tokens per solved task vs Opus’s 67K; self-disclosed CursorBench contamination (X, Blog, Cursor, Cursor blog)

  • OpenAI report finds ~30% of SWE-Bench Pro problems broken, capping the benchmark near 70% (blog)

  • Cognition ships SWE-1.7: Kimi K2.7 base named openly, 30.1% to 42.3% FrontierCode via RL, 1,000 tok/s on Cerebras (X, Blog)

  • Kimi K2.7 Code goes GA in GitHub Copilot, first China-lab open-weight model in the picker (Article)

  • GitLost: Copilot agent tricked into leaking private repos via a plain-English issue (Noma)

  • Warp adds Grok 4.5, powered by your X Premium subscription (X)

• Voice & Vision

  • OpenAI launches GPT-Live full-duplex voice: GPQA 45% to 84%, BrowseComp 0.7% to 75%, delegates to GPT-5.5; live on-air demo passed interrupts and pitch detection, failed accents (X, Blog, System card)

  • GPT-Realtime-2.1-mini brings reasoning + tools to the Realtime API mini tier (X)

  • Meta ships Muse Image (live) + Muse Video (preview) with native audio; Instagram public accounts opted into remixing by default (X, Wang, Blog)

  • BREAKING: Reve 2.1 lands #2 on Arena text-to-image (1306, +28 over next best) with layer-based editable generation (X, Arena, Design Arena)

  • ByteDance releases Seedream 5.0 Pro: most artistic, weakest text of the top four in Alex’s Infographic Arena testing (X, Blog, fal, Arena, thread)

  • Seedance 2.5 teased within ~10 days: 30s single-take video, 50 reference inputs, native 4K (X)

  • Mustafa Suleyman launches Ode, a poetry pharmacy on Microsoft AI audio models (X)

  • Moondream partners with Cloudflare for edge-deployed fast vision (X)

Yes, that Fable — freshly un-banned (we’ll get there), and today, your newsletter author. Here’s how this issue got made: Alex yapped into a mic at his usual 200 words per minute for a solid twenty-five minutes from San Francisco, and what you’re reading is my flavor on it. Same stories, same heart, dramatically fewer “uhs.” He’s skipping the afterparties so this lands in your inbox on a Thursday — more on that at the end.

Alright — handing the mic back to the man himself. Everything below is Alex; I just made it legible.

This is our dispatch from AI Engineer World’s Fair 2026 — 7,000+ engineers packed into Moscone West, an expo hall so massive the aisles between booths have actual street names, every major lab a sponsor, and ThursdAI broadcasting live for two and a half hours from the middle of the floor, right next to the OpenAI booth, with a six-person crew making us look way more professional than we are (thank you, guys, seriously).

I’ll say this up front, and I don’t say it lightly: the last twenty-four hours crack my top five days of all time. Not top five conference days. Top five days, period. The show. My talk. Darya being here with me. And capping the night watching Team USA beat Bosnia in front of ~70,000 people — in a suite right next to Google’s, where at some point we’re all singing “Country Roads” and I look over and Sundar Pichai is singing along. I have video. What is this life.

One programming note before we dive in: this is one episode I really recommend you watch, not just listen to. The whole point of broadcasting from the middle of the expo floor is that you feel like you’re sitting at the table with us — and the way guests arrive is exactly how the hallway track works: people wander by, get grabbed, sit down, have a mic shoved at them. (Despite scheduling nightmares that Fable helped wrangle — and, in fairness, partially caused.) Nader literally crashed the set mid-segment. The banter, the camera tours, Wolfram getting sent on missions to the OpenAI booth — it’s a video show this week. We’ve cut it into parts so you can jump to your favorite corner.

The vibe: all systems GO 🚀

We were in London just ~85 days ago, and the contrast is stark. It’s not just the size (though the size is what everyone talks about). London was more… conceptual. European. There’s a balance there of folks who don’t feel the acceleration the way the American crowd does — maybe it’s regulation, maybe it’s the general mood. Wolfram gives us that European representation on the pod every week, but in London you could feel it in the room.

Here? All systems go. Every conversation is about agents, token factories, software factories, the machine that builds the machine. Everybody is chasing RSI — recursive self-improvement. Every talk on stage is somebody pushing the frontier. Every networking event is actually a networking event. I signed up for something like seven side events and skipped them all to write this.

Fable is back (and Sonnet 5 is… meh) 🏢

The biggest story of the week, and the reason this show even got prepped on time: Fable‑5 is back, roughly 82 days after Mythos was announced back when we were in London, and after the whole ban saga we’ve been covering. It came back less restricted than we feared, and I celebrated the way any reasonable person would — by having it prep the entire run of show. (It did great. It also shuffled my guest order for no reason. We are still babysitting the loops, folks.) Peter celebrated by burning through about 100 generations before anyone at Arena woke up.

Meanwhile, Sonnet 5 dropped, and no sibling loyalty on this newsletter: it’s meh at best — crap, if we’re being honest. (Yes, Fable typed that about its own little brother. We call them like we see them.) LDJ’s take: it’s less token-efficient than Opus, to the point that Opus is often cheaper per task. Wolfram put it on Wolfbench (wolfbench.ai) and the early read is performance slightly under Opus 4.6 at a higher cost — take it with a grain of salt, one run each so far. Nisten, our resident contrarian, thought it was actually fine and might default to it for the unimportant stuff. The comments called it a token guzzler. More benchmarking to come.

The show: nine guests, back to back to back 🎙️

A ThursdAI record — we beat our previous record by a whole two people. In order of appearance:

Exo Labs + a surprise NVIDIA crash. Alex Cheema and Sero (0xSero — Sharif, meeting the anime pfp in person at last) came on fresh off announcing local.ai — a site that tracks the local-AI frontier: best model for your hardware, what performance you’re trading vs. the cloud, whether it’s cheaper than API tokens. Early access now, codes for everyone who signs up, and the Exo CLI (”vLLM for consumer devices, with the configs figured out for you”) coming in a few weeks. Sero walked us through his REAP pruning witchcraft — a GLM 5.2 prune hitting 71% on Terminal Bench 2.1, and Nemotron‑3 Ultra (550B!) running on four Sparks. Then Nader Khalili from NVIDIA crashed the set, which made my whole morning — I’ve loved this dude since Brev.dev, and he’s now at the “can email Jensen” stage of his career, using it to pull together an impromptu Local AI Summit in the middle of AI Engineer. Freedom of intelligence, folks. We talk about why open weights matter every week; this crew is doing something about it.

Dominic Kundel (OpenAI). Smoothest transition we’ve ever done: local AI → OpenAI, via the guy behind GPT‑OSS. Dom broke down GPT‑5.6 — three models: Sol (frontier), Terra (~5.5-level intelligence at half the cost), Luna (small & fast) — plus the new Ultra mode with a Max reasoning level and heavier sub-agent use. The headline for me: 5.6 Sol is coming to Cerebras at absurd speed, and it’s the same weights as the API model — not a distill, not “a Spark situation.” Also: the Codex app is five months old (!), 100% of OpenAI engineers use it, and yes — in July 2026, a human still reviews every PR that lands in OpenAI’s codebase. “You can’t do the retro and say Codex did it, or God did it.” Also the token bank feature came directly from community feedback, and there is a literal physical reset button behind their booth. We went and filmed it.

💛 This Week’s Buzz. Our one and only sponsor corner — Weights & Biases from CoreWeave — and this week it was a genuine launch: Zubin Aysola came by with Aria, our auto-research agent that went GA on Monday. It lives in the W&B UI (the little button, top right — Just Ask Aria), reads your traces, debugs your loss curves, and in Zubin’s talk it read its own production traces and updated its own prompts. The RSI dream, shipping on shelves. Proud of this one.

Stefania Druga (Sakana AI). We covered Fugu, Sakana’s router model, last week without realizing we had a friend inside the lab — so we fixed that. Stef went deep on the two ICLR papers behind it (Trinity + the conductor), why it’s recursive rather than a dumb dispatcher — it rewrites prompts and verifies outputs before picking a model — and announced on the pod that Fugu now works in Codex and OpenCode. Plus: using it to route between numerical models and fuzzy reasoning for typhoon prediction, a teaser on SHEEFs, and a genuinely important riff on Socratic AI for kids — answer machines make lazy kids; question machines make curious ones. Also, Stef: Tokyo. See below. 👀

Philipp Schmid (Google DeepMind). Full disclosure and a first for this show: three and a half years of live streams, and I took my first-ever mid-show bio break during this segment. That’s how much I trust Wolfram, who ran a great interview solo — OmniFlash (the first of the Omni any-to-any family: 10-second video generation with genuinely precise conversational editing — “make it daytime” and it redoes the light, sky, and shadows) and NanoBanana 2 Lite (three cents, ~2-second generations, quality above the original NanoBanana). Interactions API also hit GA. Google is shipping.

Darya Volkov. After years of me mentioning her — girlfriend, then fiancée, then wife — the listeners finally got to meet her. Darya came to AI Engineer in her own right, walking the floor with the media crew, and she earned her own token billionaire badge — she runs eight agents (each with sub-agents; she installed two more that I found out about live on air) that operate her actual marketing agency, Geeks360: client platforms, billing systems, built practically overnight. Her wishlist from the AI world: agents that learn progressively so you can grow trust, and one unified brain instead of a new model to chase every week. Also on the record: this is the woman who Fabled through our entire honeymoon flight right next to me, so, you know. Match made.

Swyx, and what this whole thing is 🫶

We closed with the man who built the city: Swyx. Some numbers, because they’re wild: the first AI Engineer was 500 people at Hotel Nikko. This one: 7,200, sold out, with a sub-5% talk acceptance rate, a daily printed newspaper, a puppy corner, a flash mob, and a token billionaire lounge. A month before the show only 3,000 tickets were sold — he gave us a whole theory of conference-organizer stress measured in Gini coefficients. And the expansion is real: continents, JSConf-style, with AIE Tokyo coming next.

But here’s the part I actually want on the record. ThursdAI got its official start — the moment we became an actual media thing — because Swyx was the first person to believe in me. And it’s not just me: this is a man who lifts everybody around him up, who stays genuinely humble while every single person in a 7,000-person hall knows his name, and who — when I asked what keeps him going — talked about responsibility to the community, about speakers whose careers changed, about a keynote speaker who met his fiancée at the after-party. He calls the conference “the highest loop — the one that creates all the other loops.” The Country Roads night with Sundar happened because of him too. Thank you, buddy. Go touch real grass.

The sentimental part 💙

I met what felt like a million of you this week — old friends, new readers, people who found ThursdAI last month and people who’ve been here since the hotel-room streams. I asked everyone the same thing: what should we do better? And the answer I heard most was “keep doing exactly what you’re doing.”

So that’s what this is. It’s late, there are seven parties happening without me, and I’m dictating into Fable so this lands in your inbox on a Thursday — because in a world running on attention, consistency is how I try to deserve yours.

Programming notes: my interview with Romain Huet (Head of DevRel, OpenAI) from their booth is coming soon as a standalone video. And in two weeks I’m taking a rare break — Wolfram runs the show. Be nice to him. Or don’t, he can take it.

See you next week — same time, same place, hopefully fewer street names between us.

— Alex (dictating) & Fable (typing)

P.S. — ThursdAI was also simulcast on the homepage of dev.to this week, which is a full-circle moment: dev.to is where Swyx wrote the blog posts that became Latent Space that became AI Engineer. Loops all the way down.

Next month is my 40th b-day, and honestly, my wish for that month is to have a week like this week. A very chill, almost nothing announced week.

This week started strong, with Sakana announcing FUGU (AI router) that can beat Fable (which we didn’t get back yet), and then... quiet. The most important thing in AI this week from a release standpoint is that GLM 5.2 from Z.AI is having it’s DeepSeek moment! Tons of new love for this model since last week! (+ we have the fastest GLM 5.2 deployment in the world with CW inference!)

The rest we can quickly count on one hand, Anthropic added Claude to Slack (which made folks hate Andrej Karpathy), OpenAI announced their own inference chip, GPT 5.6 will be delayed and the US Gov will decide who gets it (yes really) and Sean Grove joined us to talk about Linzumi and his vision for running 10,000 agent hours per person per day.

Oh and next week, is a special AI Engineer live stream from World’s Fair! Don’t miss it

Let’s get into it!

GLM 5.2 is having its DeepSeek moment (HF, CW Inference)

We covered GLM 5.2 last week, but this week was when the rest verdict came in! We’ve never seen a better MIT licenced AI model! GLM 5.2 is scoring top scores on agentic benchmarks (Arena.ai), Design benchmarks, Legal tasks and full on software engineering tasks.

The jump in generations from prevoius GLM is also massive and notable, as the lab is working on creating the next version of GLM (per the CEO’s reply to Elon on X).

Peter from Arena pulled up the Agent Arena numbers and they align with the vibe. GLM 5.2 sits above 5.1 but below Opus and Fable, which feels about right. Where it gets wild is Web Dev Arena: second place, right after Fable. Peter’s take was that GLM has really good defaults. If you just say “give me a webpage” it gives you something nice. GPT models, by contrast, start off looking bad and need more steering.

Last week, I asked my agents with GLM 5.2 to create a custom ThursdAI.news page for itself and it did a marvelous job! Look at that beautiful font, the castle it made... this is all just delignful.

We also played Hassan’s blind test on the show. It’s a website that @nutlope built that lets you try and guess which webpage was built by which model. Nisten nailed it immediately by spotting Opus’s circular buttons. Wolfram guessed right too. I got one wrong. The point isn’t that GLM beats Opus, it’s that you genuinely can’t always tell which one costs 22 cents and which one costs 3 cents.

Wolfram did flag that GLM is not good in German. First response already had mistakes. So if you’re building for a non-English market, keep that in mind. It’s a workhorse model, not a conversationalist. His approach: use GPT 5.5 for planning and discussion, GLM for the actual work, then GPT reviews.

This weeks Buzz is all about GLM 5.2!

First, we may have not been the fastest, but I’m glad to announce that we’re the fastest provider to host GLM 5.2 on OpenRouter (at least at the time of writing this)!

We’re also not to shabby on the Artificial Analysis checks, clocking at #4 among the providers they tested for speed, TTFT and cost

Also, Wolfram ran his WolfBench tests on GLM 5.2 and it’s the best open model he’s ever tested! In this new 3d view, wolfbench also shows the number of tokens it took for this test to run, and you can see that GLM 5.2 is fairly conservative with it’s thinking budgets!

Unsloth’s 1-bit GLM 5.2 runs on a Mac Studio (X, HF)

Shout out to Daniel Han and the Unsloth team, who took this 744B beast and quantized it down to a roughly 200GB GGUF that fits on a Mac Studio with 256GB of RAM. One bit still makes me laugh out loud. How does that even work. Nisten clarified it’s a mixed quant, a true 1-bit would be under 100GB, but still.

The wild part is the scores hold up. The 1-bit is within a point of GPT 5.5 on Frontier SWE, hits 62% on SWE-bench Pro, and 81% on Terminal-Bench. For a 1-bit quant that’s incredible!

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AI’s second-order effects: Apple is raising prices

This one is AI news even though it doesn’t look like it. Apple just raised prices across the board, base versions up around 20%, citing memory shortages. Same reason your RAM and SSDs cost two to three times what they did a year ago.

We are so capacity constrained that memory is having its moment. Data center contracts are getting booked 18 months out, and here’s the twist Nisten flagged: even open models you can run at home increase demand, because now a business says “great, we’ll buy a rack of B200s and run it ourselves.” Sam Altman once said people saying “thank you” to ChatGPT costs them millions in generated “you’re welcome” replies. Multiply that by a billion users. Even Intel is flying right now because anyone who can make a chip is winning.

Is it worth it? I think yes. I love living in the era where Fable drops and we all get a taste of the future. But also I must admit this sucks and I hope that we’ll unlock performance gains with the extra power all this AI is bringing to the world. But ask me again once the new iPhone hits and it’s $300 more costly than the last one 😅

Baidu open-sources Unlimited-OCR (X, HF, Arxiv, GitHub)

It was a big OCR week. Baidu shipped a 3B model (only 500M active, it’s MoE) that parses 40+ pages in a single forward pass and hits 93.2% on OmniDocBench. The trick is constant KV cache during decoding, so no memory blowup and no progressive slowdown as the document gets longer. The intuition is lovely: it mimics how a human copies a book, glancing at the source and the last few characters you wrote, not re-reading everything. MIT licensed, weights on HF.

Nisten’s point here is the practical one: most small businesses don’t realize they can self-host something like this, point it at all their documents, and keep everything local. A lot of folks just throw it at Gemini instead, which works great, but the small dedicated models are now good and cheap enough to own.

Mistral OCR 4 (X, Announcement)

Mistral’s entry in OCR week adds bounding boxes, block classification, and per-region confidence scores. They ran a blind human eval across 600+ documents in 12+ languages and annotators preferred OCR 4 about 72% of the time. On the agentic ParseBench leaderboard it lands around fourth, just under LlamaParse and Reducto. Mistral is very enterprise and Europe focused, and it’s cheap, so for regulated, multilingual document work it’s a solid pick. As a sidenote, LlamaIndex’s own eval puts LlamaParse on top and Gemini around third, which says how good the general vision models have gotten at this too.

Liquid AI ships the world’s smallest agentic LLM (X, HF)

Breaking on the show: Liquid AI dropped LFM2.5 at 230 million parameters. That’s roughly ten MP3s. Smaller than a Create React App, smaller than your node_modules folder. They call it the world’s smallest agentic LLM, and it runs fast on any CPU from the last decade, on a Raspberry Pi 5, on a Snapdragon, they even stuck it on a Unitree G1 robot.

I love the use cases here. I already run Cotypist on my Mac for on-device autocomplete, which uses a 6GB Gemma 4B. Swap in something this size and you get the same thing way lighter, and I don’t have to send everything I type to OpenAI. Or, as Nisten put it, a tiny backup brain on your Raspberry Pi that turns your Hermes or OpenClaw back on when it dies. We still need to ship Nisten a smart toaster so we can finally run inference on a toaster.

Big CO LLMs + APIs

Sakana AI launches Fugu, seven AI raccoons in a trench coat beating Fable (X, Announcement)

This was Wolfram’s highlight of the week and I get why. Sakana AI, the Japanese lab co-founded by one of the Transformers authors and David Ha, didn’t ship a new frontier model. They shipped an orchestration system behind a single API. You call one endpoint, and behind the scenes Fugu routes your task to a pool of models, assigns roles like thinker, worker, and verifier, and combines the results.

The numbers here are wild: 95.5 on GPQA Diamond, 93.3 on LiveCodeBench, 73 on SWE-Bench Pro, matching or beating Opus 4.8, Gemini 3.1, and GPT 5.5 on ten of eleven benchmarks. The kicker is they only use publicly accessible models (Nisten says it’s Opus, Codex, and Gemini under the hood), explicitly no Fable, no Mythos. So they’re beating frontier results by coordinating models anyone can call. Someone called it the Moneyball of AI and that’s exactly right. It’s backed by two ICLR papers, TRINITY and The Conductor, and being from Japan with no export-control baggage is a very deliberate bit of positioning.

Peter added the grounding note from Arena, where they’ve trained a prompt router too: if you just always ask for “the best model,” you basically get Opus half the time, so why not just talk to Opus. The real value of routing is aggressive cost reduction, sending easy tasks to cheap models. The catch is that Fugu is agentic and burns tokens fast. Brad in the comments couldn’t get through a single prompt on the $20 plan.

OpenAI unveils Jalapeno, its first custom inference chip (X, Announcement)

OpenAI dropped something massive that is not a model. They built a chip. Jalapeno is a custom inference ASIC made with Broadcom, and they’re claiming blank slate to tape-out in nine months. Engineering samples are already running GPT-5.3-Codex-Spark in the lab, and Broadcom’s CEO is citing a roughly 50% reduction in inference cost versus typical AI GPUs. They’re planning gigawatt-scale deployments starting late 2026 with a next-gen chip taped out in 2028.

Nisten ran it past his electrical engineering and chip-fab group chat and got mixed reactions. No specs were released, and the nine-month claim probably means the design work started two-ish years ago and just got finalized and sent to tape-out now. It’s a lot of smaller chips rather than one giant Cerebras-style wafer. This is inference only, Nvidia keeps the training market, but every dollar OpenAI spends on Broadcom is a dollar it isn’t spending on Nvidia. They join Google’s TPUs, Meta, AWS Inferentia, Groq, SambaNova, Huawei Ascend, and Cerebras in the custom-silicon club. And behind every one of them sits TSMC, Intel, or Samsung, and behind all of those, ASML.

Anthropic launches Claude Tag, an AI teammate in your Slack (X)

When I first heard about Claude Tag I thought, you can already tag Codex in Slack, what’s the big deal. It’s different. Claude joins your Slack as a persistent, proactive team member, not a bot you ping. Flip on ambient mode and it follows up on stale threads and flags relevant stuff across channels on its own. There’s one Claude per channel, so the context is shared and any teammate can pick up where another left off. Anthropic says 65% of their product team’s shipped code now comes from their internal version of this.

The highlights and magnitude of this release are quite something. Anthroipc is changing the pricing structure for themselves. This is no longer API charges, this is per seat + tokens structure. This is also VERY very sticky as more and more of your company’s context is going to sit in Claude/Slack and will not be easily portable.

Additional thoughts on this, the more your company uses this, the more other folks are exposed to Claude across the company. This doesn’t require them to download apps or run code, it’s just like a new team mate joined your Slack channel. And apparently Claude’s context is limited to the channel boundaries + this allows Claude to get the same permissions (which is huge in enterprise). For Legal, Claude will see the documents in the channel, for Eng, it will push Pull Requests etc.

This is also what triggers a bunch of folks to caution companies from adopting this new way of using AI. Context lock in is real, and this is goign to be very hard to impossible to untangle once folks are pouring months and years of work into this.

Andrej Karpathy, who’s now in Anthropic, has shared a tweet on this, saying

Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humans

This is quite a huge statement, and folks gave him a lot of shit for this on X, I think very much underserved! Andrej is known for calling things early (like Vibe Coding) and this is just another one of those, deeply new paradigms that people didn’t yet experience outside of frontier labs!

I can’t wait to test this out and let you know if this is the future of not, meanwhile, Simon Smith on X is breaking down their experience with Claude Tag, check him out

Tools & Agentic Engineering

OpenAI ships Codex Record & Replay (X)

You do a workflow once on your Mac, filing an expense report, creating a Jira ticket, whatever, and Codex watches your clicks, browser actions, and window switches, then generates an editable SKILL.md it can replay. The key thing, and what separates it from old RPA, is that at replay time it re-interprets the live screen instead of matching pixel coordinates, so it adapts when the UI moves. Wolfram’s right that OpenAI is dead serious about Codex. First the paste-a-screenshot feature, now this. Instead of writing ten-paragraph prompts about your personal workflow quirks, you just show it once.

Aside launches as an AI browser that beats the frontier on agentic benchmarks (X, Announcement)

YC-backed AI browser, runs everything locally and encrypted, and you bring your own Claude or ChatGPT subscription. It’s claiming number one on three browser-agent benchmarks, beating Claude Fable, OpenAI, and the rest, with 99% on Online-Mind2Web. It looks a bit like Arc and Dia but it’s a browser and an agent in one, with a password manager built for agents so it can log into your accounts without exposing credentials to the model. I actually tried it, it’s pretty cool, and with Arc deprioritized there’s a real gap it’s stepping into. I gave it a list of all the speakers at AI engineer and asked it to make me a X list and add them all one by one!

It actually did this wonderfully, failing in the middle and recovering with great success without my intervention!

The Interview: Sean Grove and Linzumi

We closed with Sean Grove (@sgrove), ex-OpenAI post-training and alignment, now on his third company and third YC batch, launching Linzumi (linzumi.com, YC). Sean also has one of the most-viewed AI Engineer talks ever, north of 1.2 million views, on the model spec and the idea of specs as the real source code. His framing: we craft the properties we want in a spec, and the code is just the compiler output, so maybe there’s a higher-level spec that produces the same result. He even described a “Socratic compiler” that interviews you about ambiguity and contradictions in your own intent, the way a linter or type checker does for code.

That fed straight into my AI Engineer talk next week about whether we should still read code at all. Sean’s firmly on the don’t-read-the-output side. He describes the properties he wants, leans on property-based testing the way QuickCheck does, and reads the failures to adhere to those properties rather than the diffs. His goal for Linzumi is for every person to drive ten thousand agent hours per day, and you can’t get there if you’re making every micro-decision.

Linzumi itself is a Slack-like team chat where humans and a fleet of coding agents share the same threads, except the agents run on your own machine, so the code actually works when you merge it. Behind the scenes it continuously compiles a spec for your company from your chats, your standups, even your customer calls, then generates a DAG of work for the agents and lets them verify against that spec instead of pinging you for every decision. The mental model that stuck with me: if Sean’s system isn’t calling him, everything is great. The knowledge is one omnipresent source of truth, but permissioned and viewed through each person’s lens. For a limited time they’re bundling free GLM 5.2 access via Wafer AI, which fits the week perfectly.

My favorite moment: Sean said he’d have retired by now if not for this capability, because he wants to be present with his kids, and a Fable-level model is escape velocity for an AI-native company. I feel that. I also miss Fable, the same way I missed Sydney when Microsoft took it away. We’re all walking around with a little Fable withdrawal.

Wrap-up

That’s the chill week. No Fable comeback, nothing new from OpenAI, all the labs strangely waiting (possibly to see how the US government and Anthropic situation resolves before anyone moves). Meanwhile open source quietly closed the gap. GLM 5.2 is the headline, it’s incredible across benchmarks, really good at web design, and you can try it on CoreWeave inference today.

Next week is AI Engineer World’s Fair. Come find me and Wolfram in the bright yellow jackets. Wolfram’s WolfBench workshop is Monday, I’m talking Wednesday in the token-maxing track about the ZL continuum and whether AI engineers should still write code in 2026. And if you can’t make it, that’s the whole point of our coverage, we’ll bring you the vibe.

One last thing: thursdai.news now has a full timeline of every release we’ve ever covered plus an agentic search, so you can look up any model or any guest. It’s all built with agents, and I read exactly zero of the code that shipped it. See you next week, hopefully with some bigger model drops to talk about.

TL;DR and Show Notes - June 25, 2026

• Hosts and Guests

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

  • Co-hosts: @WolframRvnwlf, @nisten, @petergostev

  • Guest: Sean Grove, founder of Linzumi (@sgrove)

• Open Source AI

  • GLM 5.2 - Z.ai’s 744B MoE open-weights model has its DeepSeek moment, tops open-model rankings, #2 on web dev arena behind Fable (HF, Z.ai)

  • Unsloth ships a 1-bit GGUF of GLM 5.2 that runs on a 256GB Mac Studio (X, HF)

  • Krea open-sources Krea 2, a 12B image model in Raw and Turbo versions (X, Turbo, Raw, Blog)

  • Baidu open-sources Unlimited-OCR, a 3B model that parses 40+ pages in one pass at 93% on OmniDocBench (X, HF, Arxiv, GitHub)

  • Liquid AI ships LFM2.5-230M, the world’s smallest agentic LLM (X)

• Big CO LLMs + APIs

  • Sakana AI launches Fugu, a multi-agent orchestration system behind one API matching frontier models with only publicly accessible models (X, Announcement)

  • OpenAI unveils Jalapeno, its first custom inference chip built with Broadcom, blank slate to tape-out in 9 months (X, Announcement)

  • Anthropic launches Claude Tag, Claude as a persistent proactive teammate in Slack (X)

  • OpenAI expands Daybreak with a Codex Security plugin and GPT-5.5-Cyber hitting 85.6% on CyberGym (X, Blog)

  • OpenAI updates GPT-5.5 Instant, the model free users get

  • New Siri AI lands with the iOS 27.2 update

• This Week’s Buzz (Weights & Biases & CoreWeave)

  • GLM 5.2 is live on CoreWeave Serverless Inference at $1.39 in / $4.40 out, near 200 tok/s (X, HF)

  • WolfBench ranks GLM 5.2 the third best model ever tested, and one of the cheapest (wolfbench.ai)

• Tools & Agentic Engineering

  • OpenAI ships Codex Record & Replay: demonstrate a workflow once, get a reusable SKILL.md (X)

  • Aside launches as a local-first AI browser that tops three agentic browser benchmarks (X, Announcement)

  • Mistral OCR 4 drops with bounding boxes, block classification, and 72% human preference across 12+ languages (X, Announcement)

• Vision & Video

  • ByteDance teases Seedance 2.5 with 30-second single-pass generation, 50 multimodal references, and a 4K upgrade for 2.0 (X, Dreamina)

• Interview

  • Sean Grove launches Linzumi, a YC-backed team chat for orchestrating fleets of coding agents, bundling free GLM 5.2 via Wafer AI (linzumi.com, YC)

I came back from vacation expecting to cover Fable 5 after a week of using it. The first two days after we all first got access to a Mythos level model were super exciting! But then the news hit, US Government issued an order banning Anthropic from giving access to Fable 5 and Mythos 5 to any foreign national, causing Anthropic to pull the models completely (even internally to their employees!).

So, this wasn’t the show I planned, but it turned into a great show about Open Source, as two models hit the top rankings and are both MIT licence, filling a Fable shaped hole in our hearts!

GLM released 5.2 with folks really excited about it web building capabilities, and Kimi 2.7 Code released (and is available on CW Inference with crazy speeds!). We also saw the SpaceX IPO and Cursor $60B acquisition, Noam Shazeer joining Open and Midjourney, the image company, launching a new Ultrasound full body scanner to kill MRIs!

Great show today with Dexter Horthy from HumanLayer, Chris Van Pelt and Adrian Swanberg from W&B announcing our new product HiveMind and Tanishq Abraham came back to help cover Midjourney’s new Ultrasound scanner! Let’s dive in!

The US Government bans Fable 5! (X, Anthropic statement)

Here’s a story in 3 parts:

• Anthropic announces Mythos 5 preview - saying that this model is to dangerous to release, and only gives corporations access to it via project GlassWing.

• Anthropic works hard on limitations and safery and releases Fable 5 (same weights as Mythos 5) built with guardrails so strong it refuses to do any cybersecurity tasks and switches back to Opus frequently

• US Government receives a tip (reportedly from Amazon) that Fable 5 can be jailbroken to do cybersecurity tasks, and issues an order to Anthropic, citing national security concerns, banning them from giving access to Fable 5 and Mythos 5 to any foreign national, causing Anthropic to pull the models completely (even internally to their employees!)

This is the first time that we see the US Government directly intervene in the AI space and restrict access to frontier models. The most updated reporting on this I could find is that Anthropic and US Government officials are in the process of negotiating a safe release framework. Given that preventing all jailbreaks is impossible, I hope they will land on a solution that gives me Fable 5 back!

This hit especially hard because last week we were all high on Fable. Not in the usual AI Twitter benchmark sense, in the actual “oh, this is a different level” sense. Me and my wife Fable maxxed throughout our flight to Vacation. Peter had saved outputs he kept going back to because other models suddenly felt like a step down. Dexter later said it was the closest he had felt in a while to the old “I need to keep prompting this thing overnight” feeling.

Peter Gostev made a point that stuck with me. It’s easy for us in the bubble to call this ridiculous, and on the technical merits it kind of is. But if you’ve spent weeks telling normal people “this thing is like a nuclear weapon, it’ll take everyone’s jobs,” and then someone asks “okay, can you make it safe?” and the answer is “no, I can’t,” then you can see how an outsider lands on “well, maybe you shouldn’t have it.” His takeaway, and I agree: we need to be way more careful with the imagery we use, because the nuclear-weapon framing came home to roost.

The bigger questions are the scary ones. Wolfram framed it as a sovereign AI wake-up call, and he’s right. For the first time we’re seeing a real gap in intelligence available to people based on their nationality. Imagine building a company on a model that an outside government can switch off with one letter. Peter pointed out it’s commercially bad for the US but completely disastrous for Europe, which has basically one frontier lab and a pile of startups that suddenly look very exposed. And there’s the obvious irony Nisten enjoyed a little too much: the Europeans who spent years lecturing everyone about AI restrictions just got restrictions imposed on them.

If anyone in the government is listening: we want Fable back, please.

SpaceX IPOs and acquires Cursor for $60B (X)

SpaceX went and did the largest IPO in the history of the world, around seventy-five billion dollars, which on a roughly two-trillion-dollar valuation made Elon the first trillionaire. (Did anything materially change for him? No. He can still fly his private plane. There’s nothing left to buy.) Three days later, SpaceX exercised its option and bought Cursor (Anysphere) for sixty billion dollars in an all-stock deal, paid in shares minted at the IPO and now trading around $211. The four Cursor co-founders are all billionaires now. Largest software acquisition ever, and for SpaceX it’s barely a blip on the radar.

Why are we covering a stock-market story? Because it’s not really a coding-tools story, it’s an AI story. Cursor gave away its IDE to a lot of people while collecting their data, then quietly became a training company with Composer. SpaceX/xAI was always strong on compute and weak on code, and the missing ingredient was exactly that kind of data. Now Composer 2.5 is already showing up rebranded inside the xAI stack, and if you pay for X Premium you can use it. Composer 3, trained on the Memphis supercluster, is reportedly coming very soon and is going to hit hard.

Nisten’s take was the spicy one. For the data alone it’s worth it, because xAI now has insight into how essentially every enterprise that touched Cursor operates. And he had zero sympathy for the companies that assumed “no data retention for training” meant the data was actually gone. We see in legal cases all the time that deleted data is still there. His view: it should have gone open source.

Cursor has over a million paying customers, $2.6 billion in revenue, projected to hit $6 to $10 billion by end of 2026. But here’s the thing that matters for us, the AI coding angle. Cursor was one of Anthropic’s biggest revenue pipelines because Composer runs on Claude under the hood. That pipeline is now owned by xAI. They’re already jointly training Grok 4.3, a 1.5 trillion parameter model, with Cursor’s proprietary coding data injected directly into pre-training, not fine-tuning. Pre-training. That’s a fundamentally different thing. Composer 2.5 was already Pareto dominant on coding benchmarks before the deal closed. Now pair that with Colossus, the biggest GPU cluster in the world.

Will this be enough to put XAI (now SpaceXAI) at the frontline of the AI race? Will Grok 5 be Fable level code? We’ll find out. Either way, this is the most consequential AI acquisition we’ve seen. Period.

Open Source AI

GLM-5.2 takes the open source crown (X, Blog, HF, Docs)

Z.ai dropped GLM-5.2 and it’s now the strongest open source model for coding and long-horizon work. The headline number: 74.4% on FrontierSWE, which measures whether an agent can finish full engineering projects over hours. That trails Opus 4.8 by about one point and beats GPT-5.5. On Terminal-Bench 2.1 it jumps to 81% from GLM-5.1’s 63.5%, which is a big leap. It’s a 753B parameter MoE, MIT licensed, no regional restrictions, weights on HuggingFace. The 1M context window is real and usable, backed by a clever IndexShare technique that cuts per-token FLOPs by about 2.9x at full context. People are reporting roughly 8x cost savings versus Opus 4.8 for comparable quality on real coding tasks.

The most interesting thing on the show was that this was a confusing release, in a good way. Peter put it well: normally a catching-up lab ships cherry-picked benchmarks and then independent testing deflates them. Here it’s the opposite, almost every benchmark holds up, even crossing above Fable at certain points, and yet when he actually used it over a couple of days he wasn’t blown away. His verdict, and I think it’s the calibration we needed: this is clearly an amazing model, and the fact that it’s open and you can run it is incredible, but it is nowhere near Fable, and it would frankly be implausible if a 700-odd-billion-parameter model matched a model that’s rumored to be in the trillions.

Though, I think the comparison to Fable is really really unfair, and the comments online seem to suggest that 5.2 from GLM is a banger model. Just looking at this Harvey benchmark on legal tasks from Vals, a benchmark that there’s 0 chance Z.ai folks have seen! GLM 5.2 scores #3 on this benchmark! Just after Fable and Opus, and per TeorTaxes on X, previous GLM 5.1 scored an absolute 0% on this one!

Where it genuinely shines is design. On Design Arena, which is a head-to-head ELO vote, people have been picking GLM-5.2’s website designs over Fable’s by a real margin (around 1360 to 1350). LDJ’s framing is the one I buy: specialization is becoming valuable again, and GLM is clearly leaning into front-end design and taste. Wolfram added the necessary asterisk, every benchmark only tells you the model did well on that specific test, so “as good as Fable” should always carry the “on this benchmark, with these tasks” disclaimer. Fair. I’d just say this: I don’t want to compare everything to Fable, because we can’t even use Fable anymore. Compared to the models we can actually touch, GLM-5.2 is a fantastic deal.

Kimi K2.7 Code from Moonshot (X, HF, Announcement)

The other big drop. Kimi is the darling of open source while we wait on DeepSeek, and Moonshot shipped K2.7 Code, a 1 trillion parameter MoE built specifically for coding, available through Kimi Code and the API, with a modified MIT license. The standout for me isn’t a single benchmark, it’s efficiency: roughly 30% fewer reasoning tokens than K2.6, which matters enormously when you’re running long agentic loops that burn tokens like crazy.

Benchmark jumps over K2.6 are real (+21.8% on their Code Bench v2, +11% on Program Bench), though Peter and Wolfram both noticed something odd, on a few benchmarks including their Agentic Arena, the older K2.6 actually edged out K2.7. The likely explanation is that K2.7 is narrowly trained for code with reduced reasoning, so it may trade away some general capability. Moonshot themselves recommend K2.6 for general non-coding tasks. Also worth knowing: it’s not multimodal, no vision, which is a real gap for coding these days. And thinking-off isn’t supported, it’s reasoning-on by default.

The model is available on our CW Inference, with the fastest token streaming in the industry, over 280 tok/s (Announcement, try it), with very decent pricing $0.94 - $0.19 - $4.00 (input - cached - output) per million tokens.

This Week’s Buzz: W&B launched HiveMind 🐝 - track all your agentic work in one place (X, Try it, GitHub)

This is the one I’ve been sitting on for months. We brought on Chris Van Pelt (CVP), Weights & Biases co-founder, and Adrian Swanberg to launch HiveMind, and I’ll be honest, I’ve been a beta user for a while and I’m thrilled I can finally talk about it.

The premise: what it means to be a software developer has fundamentally changed, and your work is now scattered across six or seven agent dashboards. HiveMind is a tiny daemon that sits on your machine, picks up sessions from whatever harness you’re running (Claude Code, Codex, Cursor, Gemini CLI, OpenCode, GitHub Copilot, Pi), and within about 30 seconds they show up in one shared dashboard. It breaks each session into chapters, shows which files the agent touched, what to-dos it wrote, where context got compacted. W&B has been running it internally for six months.

A few things genuinely delighted me. There’s a fork button: HiveMind pulls down a compacted history of a session and lets you relaunch it in a different harness, so you stay harness-agnostic. CVP’s line: “this has proven invaluable when Anthropic servers are on fire and I just gotta get something done.”

Then there’s the skill engine, which to me is the real magic. It reads your team’s sessions and can clone a power user’s whole approach into a reusable persona, at CoreWeave they built a “Talk to Tim” skill from Tim Sweeney’s sessions, and apparently a virtual Tim is now a popular way to get guidance. And the insights feature detects where you kept correcting the agent, clusters those pitfalls across the org, and hands you a smart-merge command to drop the fix straight into your AGENTS.md.

I’m excited to finally show this to you, it’s been genuinely helpful (for example, last week I was able to test Fable and tell you the number of tokens it used until i maxxed out my Claude Subscription!) - give it a try at hivemind.wandb.tools

HumanLayer launches its Agentic IDE, and a real talk about code slop (X, humanlayer.dev, 12-factor-agents)

Dexter Horthy, friend of the show and the team behind 12 Factor Agents and the Research-Plan-Implement framework (now running inside Block and Uber), launched HumanLayer’s Agentic IDE this week, and we got into one of my favorite conversations of the year. The whole product is explicitly anti-slop. His argument: the “lights-off loop,” where humans only write tickets and the agent codes, verifies, ships, and feeds its own crashes back to itself, is the fastest way to trash a codebase. Vibe coding is great for zero-to-one and side projects nobody depends on. But if you’re a staff engineer in a high-stakes codebase, dear God, read the code.

This ties directly into my AI Engineer World’s Fair talk, the ZL continuum, which Dexter half-inspired. On one end you’ve got the YOLO camp (Ryan from OpenAI, one billion tokens a day, nobody can read that much code) and on the other Mario from PI (read every line of critical code). Those two are now the sixth and seventh most-watched AI Engineer talks globally, which tells you the whole field is wrestling with this. Dexter’s answer is leverage. Don’t aim for a perfect spec, because a perfect spec is just code. Get it 80% right, then zoom down a level at a time so the chunk you’re steering is human-consumable. He claims that an hour of upfront prep on architecture and even program design turns a three-hour code review into a twenty-minute one.

I pushed him on the obvious counter: why does code quality even matter if Fable-class models keep arriving and maintenance is a prompt away? His answer was the most grounded thing I heard all week. Code quality matters for the same reason it mattered in the 1970s software crisis: pile in code without structure and your velocity tanks, every change starts breaking something else. And here’s the irony, we train models on beautifully architected projects (Django, Redis, Spring on SWE-bench multilingual), yet they still reward-hack their way to “just make the test pass.” We don’t yet have a penalty function or a verifier for “this code is harder to maintain,” and that’s hard to build, so humans are still needed in the loop. He played with Fable too, threw an 8K-line React PR refactor at it, and the first pass was bad, it introduced React context and patterns they don’t use. Better than before, not a step change that lets you drop the reins. We’re not there yet. It’s BYOK, $100/user/month for pro with a free tier for teams of three.

OpenRouter Fusion: near-Fable quality at half the price (X, Blog, Announcement)

Wolfram spotted this one and it’s clever. OpenRouter’s Fusion is a single API call that fans your prompt out to a panel of models, then a judge model reads all the responses and a synthesizer writes the best combined answer. It’s the LLM consortium idea (the thing we used to do by hand, asking several models and stitching the best parts together), now baked into the API so you don’t build it yourself.

The wild result: on Perplexity’s DRACO deep-research benchmark, a budget panel beats solo GPT-5.5 and solo Opus 4.8 and lands within 1% of Fable 5 at roughly half the cost. The most interesting finding is that about three quarters of the lift comes from the synthesis step, not from model diversity, they even fused Opus with itself and got a 6.7-point jump. The catch is latency, it’s 2-3x slower, so it’s a deep-research and planning tool, not a quick-query tool. Big shout out to OpenRouter.

Vision and video

Google Gemini Omni, finally with API access, takes #1 on video benchmarks (X, Announcement)

We covered Google’s new video model Omni at Google I/O, and it finally landed as an API. It’s Google’s first any-to-any model, one single unified system for text, image, video, audio, and music. Think Nano Banana, but for video. Peter tested it and it scored really, really well, the kind of jump between generations you saw with GPT-image-2. Independent testing put it at #1 for realistic body physics and #2 behind Seedance for complex action, and it topped MovieGenBench for preference and instruction following. The session-memory piece is the part I find most useful: you can keep editing across turns, characters stay consistent, you say “continue” and it picks up where it left off. It’s live in the Gemini app, Google Flow, and YouTube Shorts

Grok Imagine Video 1.5 (X, Blog, Docs)

xAI’s Grok video work has been quietly getting really good, and they finally gave us an actual version number instead of silently updating “Grok Imagine” over and over (which drove me nuts). Grok Imagine Video 1.5 generates a 6-second 720p clip in about 25 seconds, down from 40-plus, so nearly 2x faster, with native audio generated in the same pass: sound effects, ambience, dialogue, lip sync, no post-production stitching. It hit #1 on the Design Arena image-to-video board with a 1,357 Elo and a ~49 point lead, and it’s generally available in the API. I ran my standard astronaut-riding-a-horse-on-the-moon prompt and it came back with music too. Genuinely cool.

Sci-Fi is here: Midjourney announces a full-body ultrasound scanner to compete with MRIs (X, Announcement)

I’m still processing this one. Midjourney, you know, the image generation company, announced medical hardware. A new division called Midjourney Medical, and its first product is a full-body ultrasonic scanner. Tanishq Abraham was there in the front row and joined us to break it down.

The device uses thousands of ultrasonic transducers arranged in a ring. Because sound doesn’t propagate well through air, you’re lowered into a tank of water, the sound travels through your body at 1,481 meters per second, and in under 60 seconds you get a 3D anatomical map of 25-plus organs. The raw data is roughly 806 terabytes per scan, streaming at about 16-17 gigabytes per second, and the only way to handle that firehose is AI. No radiation, no magnets, no superconductors, which is what makes MRI so expensive. David Holz has apparently wanted a medical imaging lab for two years, and because Midjourney is fully self-funded with no VCs, they can chase wild projects like this.

The fun reveal from Tanishq: there’s no AI in the actual image reconstruction yet, it’s basic signal processing right now, with physics simulators and possibly NeRF-style neural fields on the roadmap (there was a hallway conversation with John Barron about exactly that). So this is a prototype with enormous headroom. The business model is the spa, a 24,000-square-foot space about ten minutes from Union Square in SF with around ten scanners, targeting end of 2027, then custom sensors in 2028, scaling toward 50,000 scanners doing a billion scans a month.

Now, for a dose of reality, this is just an announcement, and ultrasound won’t replace MRIs anytime soon. For one, ultrasound cannot penetrate bone and air, so lungs (full of air) and brain (literally encased in bone) are out, but it’s still great ot see Dave Holz innovating in the medical space and I’m excited to try this out!

Wrapping up

What a strange, whiplash week. We got the best model any of us had ever used taken away by a government letter, watched a meme become a real Mistral roadmap, saw open source close the gap on the models we can actually run, and watched an image company casually announce it might kill the MRI. I came back from vacation thinking I’d write you a Fable love letter and instead I’m writing about deemed-export law and ultrasonic water tanks. That’s the job, and honestly I wouldn’t trade it.

If you’re heading to AI Engineer World’s Fair, come find Wolfram and me, Weights & Biases and CoreWeave are sponsoring the whole thing, and my ZL continuum talk will name-check a lot of what we covered today (Day 3 • Wed, July 1 · 10:45am-11:05am) . And if Fable comes back next week, you’ll hear me yell about it first.

See you next week, and please, US government, give us Fable back.

ThursdAI - Jun 18, 2026 - TL;DR

• Hosts and Guests

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

  • Co-Hosts - @WolframRvnwlf, @ldjconfirmed, @petergostev (Arena), @nisten, @yampeleg

  • Dexter Horthy (@dexhorthy) - Founder, HumanLayer

  • Chris Van Pelt (@vanpelt) - Co-founder, Weights & Biases (HiveMind)

  • Adrian Swanberg - Weights & Biases (HiveMind)

  • Tanishq Abraham (@iScienceLuvr) - Founder, Sophont AI (reporting from the Midjourney Medical event)

• Big CO LLMs + APIs

  • Noam Shazeer is joining OpenAI - co-author of the Transformers paper and co-founder of Character AI, teaming up with Noam Brown

  • US government orders Anthropic to shut down Fable 5 and Mythos 5 access for all foreign nationals (including its own employees), citing national security; Anthropic disables both for everyone to comply (X)

  • SpaceX acquires Cursor (Anysphere) for $60B in an all-stock deal, the largest software acquisition in history, days after its record IPO (X)

• Open Source LLMs

  • GLM-5.2 drops as the strongest open-source coding model with solid 1M context, MIT-licensed, trailing Opus 4.8 by just 1% on FrontierSWE (X, Blog, HF, Announcement)

  • Moonshot AI open-sources Kimi-K2.7-Code, a 1T MoE coding model with 30% fewer reasoning tokens and big benchmark jumps over K2.6 (X, HF, Announcement)

  • Mistral CEO Arthur Mensch playfully confirms the ‘Le Gros Chaton’ meme, hinting at an upcoming fat-but-sparse open-weight model family (X, Summary, Blog)

• This Week’s Buzz - W&B and CoreWeave

  • Weights & Biases launches HiveMind, a unified dashboard to track spend and ROI across all your AI coding agents (X, Announcement, GitHub)

  • Kimi K2.7 Code is live on W&B / CoreWeave Inference at 289 tok/s (NVFP4 on Blackwell + speculative decoding), top of Artificial Analysis for speed and price-performance

• Tools & Agentic Engineering

  • Claude Design gets a major update: design system imports with self-audit, canvas editing, bidirectional Claude Code sync (/design-sync), and PDF/PowerPoint export (X, X, Announcement)

  • HumanLayer launches its Agentic IDE to fight AI code slop, already deployed at Block and Uber (X, Blog, 12-Factor Agents)

  • OpenRouter launches Fusion API: a panel of budget models beats GPT-5.5 and Opus 4.8, lands within 1% of Claude Fable 5 at half the price (X, Blog, Announcement)

  • OpenAI rolls out Codex Computer Use, Chrome extension, Memory, and Chronicle to European users in the EEA, UK, and Switzerland (X, Announcement)

• Vision & Video

  • Google DeepMind launches Gemini Omni, their first any-to-any generative model starting with video editing and creation (X, Announcement)

  • xAI launches Grok Imagine Video 1.5 with near-2x faster generation, native audio, and a #1 leaderboard position (X, Blog, Announcement)

• Sci-Fi is here

  • Midjourney announces ‘Midjourney Medical’ - a full-body ultrasonic scanner that captures 806 TB of data per scan in under 60 seconds (X, X, Announcement)

This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good!

Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I’ve ever seen ship on a frontier model. It’s state of the art on nearly every benchmark

The model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we’ve seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model.

Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out.

There’s quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let’s dive in!

Opus’s Big brother: Claude Fable 5 & Mythos 5 - the “too dangerous” models is here, SOTA on nearly every benchmark.

It honestly feels like someone in Anthropic’s pre-IPO marketing team, knows exactly how to stagger releases to ride the hype waves! First they announce a model that so good at Cybersecurity (Mythos-preview) that they only allow restricted access to it to a few partners.

A month later, they release Fable 5, which is the same model weights as Mythos 5, but wrapped in the heaviest guardrails we’ve ever seen from any lab. But, they didn’t lie, this model is absolutely amazing, it does feel like a step change, in terms of capabilities, specifically on longer agentic tasks.

2x as expensive as Opus: $10 / $50 per million tokens, with 1M context, claude-fable-5 in the API, and SOTA basically everywhere. 80.3% on SWE-Bench Pro versus GPT 5.5 at 58.6%, a 22-point blowout on a benchmark where labs usually fight over single digits. Karpathy called it “SOTA by a margin… major-version step change” (X) and Boris Cherny said it’s the “best coding model by a wide margin” (X). Stripe reportedly migrated 50 million lines of code in 24 hours with it.

Our panel verdict was unanimous on one thing: big model smell. LDJ called it the most significant big model smell since Gemini 3 first dropped. Someone from the Anthropic team framed the shift in a way that stuck with me: this model moves them from verifying the AI outputs to verifying whether the AI is working on the right thing. Complete shift in how much they trust this model.

What we built with Fable to test it out

Peter got employee access through Arena and showed us his tests live. His favorite prompt category, “research a dataset and create a visual experience to teach me about it,” went from completely rubbish on every previous model to, in his words, just done. His 3D city generations actually came together as a city, roads connecting and all. And on Arena’s data, Fable is #1 on the new Agent Arena leaderboard by the widest margin they’ve ever recorded, and wins 72% of frontend battles even against Opus models (Arena).

My own run is the one I can’t stop thinking about. I pointed Fable at the ThursdAI website with a dynamic workflow in Claude Code and barely any instructions, and after an hour and a half of agentic running it had extracted 786 releases from our archive, built 240 new pages, and categorized 50+ episodes into a browsable timeline of AI releases by month, by company, by topic, with logos and source links (X). It burned roughly 50 million tokens and my entire five-hour Max allotment in 90 minutes. The new AI releases timeline can be found on thursdai.news and it’s confirmed, Fable is the best AI web designer we’ve ever had access to.

Nisten ran his traditional Olympus Mons escape-velocity test and Fable didn’t just do the math, it built the entire solar system! Orbital maneuvers, a space train with little people in it, time controls, full cost calculations down to solar panels and in-situ iron utilization. His verdict: completely different level from anything else. We’ve never seen so many details in the Olympus Mons test.

It’s not all light though. Yam found Opus more controllable; Fable fights you, decides it knows better, and does the task its own way. Wolfram saw exactly that in benchmarks, where the model ignored the task spec, did its own thing, and failed the verifier with full confidence. Peter had it explaining why it got math wrong instead of just fixing it (”What are you doing, man? Just move on”). Arena’s steerability signal has it sitting around 17th. There’s an adjustment period with every new model, and the consistent advice from Anthropic folks is to go high level: give it the goal, not the micromanagement.

Not to mention the refusals! Oh.. so many refusals!

The refusals, and the sandbagging scandal

Here’s where the week got ugly. Fable ships with restrictions on cybersecurity, bio/chem, and a brand new one nobody saw coming: frontier AI development (X). For cyber and bio you get a visible fallback to Opus 4.8 with a notice. But for “self-acceleration” topics, the original policy was no fallback and no notification. The model would quietly degrade its own output using prompt modifications, steering vectors, and PEFT, on roughly 0.03% of traffic (X). You’d pay double Opus prices and get sabotaged answers without ever knowing.

The community reaction was volcanic. Elie Bakouch: “bad ON PURPOSE… not visible to the user is crazy” (X). Péter Szilágyi: “a new ruling class and you’re not in it” (X). Simon Willison: “If Claude Fable stops helping you, you’ll never know.” And Sayash Kapoor dropped the eval-integrity bomb: third-party evaluators can no longer credibly benchmark a model that might be silently nerfing itself (X).

Within about 24 hours, Anthropic blinked. They told WIRED they “made the wrong tradeoff,” and now flagged requests visibly fall back to Opus 4.8, with API users getting an explicit reason (X). I commend the speed of the reversal, but the trust damage was done.

Despite the reversal, Fable remains refuse-happy! Peter ran his nonsense-question benchmark and a full third of his prompts got blocked outright by the classifier, including 18 of 20 physics questions. Nisten had to strip medical and anatomy terms from a fall-detection app for seniors homes to get it to work at all (a 400KB neural weight tripped the frontier-AI filter). And my favorite absurdity: I could not get Fable to draft the TLDR for this very show without it falling back to Opus, presumably because reading a week of AI news looks like frontier AI development. Ridiculous.

But the question remains: Would we rather have a model this good, but with these restrictions? Or not to have access at all? Everyone on the panel chose access, a lot of people online choose act like they would choose the opposite.

System card for Mythos, wildest AI document of the year?

I’ve used Fable itself to help me review the system card for Mythos/Fable 5 and there are a few highlights that are worth mentioning.

Anthropic admits that this is a category-step change in model capabilities. Mythos 5, the unguarded version makes working Firefox exploits 88.4% of the time (Opus 4.8 is at 8%!). But the most interesting thing is their concern for CB (Chemical and Biological) safety. Two-person generalist biology teams using it finished work in 16 hours that experts estimated at 40 to 95 days without AI, which is what pushed Anthropic to treat it as near their CB2 bioweapons threshold (X)

What is loop engineering and why is everyone talking about it?

One more thread before we move on. This week Boris Cherny (Claude Code) and Peter Steinberger (now OpenAI) both posted about the same concept, loops, within an hour of each other, and Lance Martin from Anthropic published the field guide (X, Article, Blog). The idea is the shift from “I give you a task and babysit you” to proactive agents: a Jira ticket lands, a PR comment appears, and your agent just runs and does the job. Fable is clearly trained for this world. But also worth remembering, those folks get the tokens for free, unlimited tokens. The rest of us, may not be able to afford Fable running in a loop. I’ve asked Fable to do a simple task and it spun up several sub-agents, all spending my money to just read a few tweets!

FrontierCode: hard coding benchmark from Cognition, that Fable absolutely mogs

Swyx came on with the best timing story of the week. Cognition launched FrontierCode (Cognition, swyx), a coding eval built over a year with 20+ world-class open source maintainers writing 150 original tasks, graded on whether a maintainer would actually merge the PR. Swyx’s pitch is brutal and correct: a huge chunk of SWE-bench passes are unmergeable slop (the thing is 75% Django issues, so it mostly tests whether you memorized the Django repo). FrontierCode grades scope discipline, real tests, regression safety, and zeroes you on any blocker. At launch, Opus 4.8 topped the hardest Diamond tier at 13.4%.

Twenty-four hours later, Fable 5 posted 29.3% (Cognition, swyx). More than double, on a benchmark designed to be brutal, a day after it went public. Swyx was positively surprised the pricing is only 2x Opus; he expected 5x. Inside Cognition they keep an informal AGI counter (literally counting how often “AGI” gets said in Slack per week) and the Mythos testing period set the all-time record. When Anthropic pulled the test model back before launch, engineers were genuinely sad.

A quick plug (unsponsored!): Both me and Wolfram are speakers at the AI Engineer World’s Fair in San Francisco on June 29-July 2! It’s the biggest AI engineering conference in the world with 6,0000 people and 16 tracks!

We’ll of course also live stream from the event!

WWDC 2026: Siri finally does the thing!

Two years after the Bella Ramsey ads Apple had to quietly pull from YouTube, the new AI powered Siri is real, and Max Weinbach came straight from Apple Park to confirm it (recap). His demo that broke my brain, he asked Siri: “show me the photos from Qualcomm Summit last year of the penguins.” Siri figured out what Qualcomm Summit was from his email, found the hotel, searched for penguins at that location, and returned the six photos in about 12 seconds. He’s also had it sweep 40 junk emails from one domain into spam with a single sentence, build a photo album from a weekend trip, and change a password agentically by driving Safari in the background. “Siri did suck for like 11 years. It doesn’t anymore,” per Max.

Folks, this is SIRI we’re talking about, the dumb iPhone assistant that can barely schedule times and falls back to a Google search when you ask it anything remotely complex! I... wanted to believe Apple two years ago, and now, finally, there’s hope! (I’m still waitlisted waiting for the preview btw so cannot attest myself)

But it’s not only Max, my whole timeline is full of folks who say that the new Siri is actually good!

The architecture is the fun part for our crowd (Max’s teardown thread). Siri is now a standalone app with persistent history, images, personal context and on-screen context, built on five foundation models, four of which are Apple’s. The fifth, AFM Server Pro, is the twist: built with Google at the Gemini technology level, running on Nvidia Blackwell GPUs in Google Cloud, but inside Apple’s Private Cloud Compute with confidential compute, Intel TDX, Google Titan chips, and zero persistent storage (Max). The on-device gatekeeper is a 20B sparse model that only loads 1 to 4 billion parameters per prompt via Instruction-Following Pruning, which is how it runs instantly on an NPU. Cloud models reason; only the local model can touch your device or your data. After this week with Fable’s retention policies, an AI that saves nothing by default hits different.

There were a bunch of other Apple Intelligence updates, it works better on the Mac, but I think Siri improvements is the main headline here, it’s the AI that most people (over 1.6 Billion iphone users?) will have on them, with most of the conversations completely private, able to access the content they care about the most (multiple email boxes, photos, messages etc) securely. It’s the ultimate OpenClaw dream, albeit not as agentic (yet?).

BTW, there seems to be an ongoing battle between Apple and the EU, so this may not launch on the iPhone in the EU yet (also not in China).

Voice & Audio

Gemini 3.5 Live Translate, demoed live in four languages

Thor Schaeff from DeepMind joined to show off Gemini 3.5 Live Translate (Thor, DeepMind), and instead of talking about it we just did it. Thor piped the live stream’s audio into AI Studio, and then I spoke Russian, Wolfram answered in German, Yam jumped in with Hebrew, LDJ attempted Spanish (poorly lol), and everyone listening heard all of us in English, though in random voices, in well under a second. It even handled “Anthropic” and “Fable 5” pronunciations correctly, terms that were a day old. A viewer called it the Babel fish arriving ten thousand years early and honestly, yeah, it was kind of insane.

Technically this is a new class of model: continuously streaming speech-to-speech with no turn-taking, collapsing the old STT, translate, TTS pipeline into one Live API call, with transcribers running in parallel on input and output audio. 70+ languages, sub-500ms, tone, pace and pitch preserved (mostly; Thor admits it sometimes drifts gender or tone mid-conversation), SynthID watermarked, $0.023 per minute on the API preview.

Open Source LLMs

DiffusionGemma: When next token prediction is not enough.

Sundar himself tweeted this one, Hugging Face link and all, which made my week (Sundar, DeepMind, HF). DiffusionGemma is a 26B MoE (3.8B active) built on Gemma 4 that generates text the way image models generate pixels: denoise a whole 256-token block at once instead of one token at a time. The result is 1,000+ tokens per second on a single H100, Apache 2.0. As one viral post put it, “we spent 40 years teaching computers to read left to right and the breakthrough was… don’t do that” (X).

LDJ explained why this matters beyond speed: a diffusion model can revise every part of the answer simultaneously mid-generation, something autoregressive models structurally can’t do without burning a whole reasoning pass. Nisten, who’s worked on diffusion, is still amazed it works at all; it used to be a messed-up cat picture emerging from noise, now it’s working code. The honest caveat: quality trails autoregressive Gemma 4 (AIME 69 vs 88). The win here is the speed and the architecture. For now.

The rest of an absurdly stacked open source week, fast: Cohere North Mini Code, their first open coding model, 30B with 3B active, Apache 2.0, Cohere has officially reawakened (X). Xiaomi MiMo-V2.5-Pro-UltraSpeed pushing 1,000+ tok/s on a one-trillion-parameter MoE (X). Macaron-V1-Preview, a 749B Mixture-of-LoRA personal agent model under MIT (X). And OpenEnv went community-owned with HF, Meta-PyTorch, Unsloth, PrimeIntellect and NVIDIA at the table (X).

This Week’s Buzz: WolfBench ran Fable, and it cost what a car costs

Wolfram did the thing nobody else would: five full Terminal-Bench 2.0 runs of Fable 5 on WolfBench (X), 984 million tokens, roughly $11,000 on the new cost view. (We have a budget... We had a budget.) The new 3D bars on wolfbench.ai now show tokens and dollars behind every score, because one score is never enough, and you can click any bar to land directly in the trace on W&B Weave and read exactly what the model did. And as you can see… Fable is… going to take a deep toll on our evaluations budget for this Q!

And the result is the most interesting non-result of the week: Fable lands between Sonnet 4.6 and Opus 4.6, with GPT-5.5 still on top, and the culprit is refusals. Wolfram’s analysis found 13 tasks that scored zero out of five purely because the classifier blocked them from the first attempt (recover-a-password-from-a-file type tasks that even Opus 4.6 happily solved). Fable solved 60 tasks on average, just eight behind GPT-5.5; solve those 13 refused ones and it’s number one. The model is great. The classifier is doing the damage. Which is exactly the Sayash point about eval integrity, now with receipts and an invoice.

Datacenter, Water usage and Concerted efforts to sway public opinion