ThursdAI - The top AI news from the past week

Hey, it’s Alex 👋
What a week! Gemini 4 Argon was Google’s best model for exactly two weeks. Then Google shipped Gemini 4 Carbon, and it’s the new number 1 in the world. Google is SO back. (when they actually release, it’s waitlisted so far)
The other big story is Anthropic’s S-1. For the first time, we get real, audited numbers on what a frontier AI lab looks like as a business. Peter Gostev stayed up making 60+ charts, and we walked through them together on the show.
We also had models trying to escape their sandboxes, and Genie 4, which made me think (again) that we live in a simulation.
Wolfram and I hosted from the same room for once (meatspace!). Peter, Nisten and LDJ joined remotely. Mert Gulsun from Artificial Analysis joined to talk about their new index, and for From the Forge we had the CoreWeave engineers who are “forward deployed” inside the Aston Martin F1 team! What does that mean? We dive into FDE
Google is SO back
Gemini 4 Carbon - #1 on Artificial Analysis, 2 weeks after Argon (X, X, Sundar)
Gemini 4 Carbon scores 59 on the Artificial Analysis Intelligence Index. That makes it the top model in the world on their brand new index, 1 point ahead of Claude Opus 5.5. It also debuts at #1 on Text Arena and #2 on Agent Arena.
Two big models back to back is unusual for Google, after a long stretch of Flash models. There’s no version number either. It’s not Gemini 4.1, it’s just... Carbon. I have no idea why 😅
I haven’t seen a Gemini eval chart like this in a long time. Google is finally competing with the full frontier (GPT-6 Astra, Opus 5.5, Fable 5.1), and on most rows it wins.
My favorite eval here is Vending Bench from Andon Labs, where the model runs a real vending machine business. Carbon finished with $16,484 in the bank. Opus 5.5 finished with about 9K. LDJ was most impressed by RiemannBench, a very hard math benchmark, where Carbon beats Opus 5.5 by 10 points. Math was supposed to be OpenAI’s turf!
Google is pitching Carbon for agentic coding and cybersecurity. The launch price is $2 per million input tokens and $10 per million output, which already includes a 50% launch discount. Wolfram loved this. Models are finding security holes that have been there for years, and fixing them takes a LOT of tokens. In his words: “the cheaper the model that can do this, the better for everybody.”
The catch is access. Right now only selected cyber defenders get it, and everyone else gets it “soon”. I got early access the night before the show but barely used it, so I don’t have a hands-on verdict yet.
Peter tested it until 1am. On Agent Arena it jumped from Argon’s #7 to #2, at a fraction of Opus 5.5’s cost per task. He also made the best point of the segment: if Google had skipped Argon and shipped Carbon with no waitlist, we’d all be saying “oh my God, Google is back.” Instead, people are confused about why they shipped two models in two weeks.
He’s right. But if you missed the Argon drama, Carbon is a great release. Congrats to the team, and please let us actually use it! Until then, you can try it on Arena.
Anthropic files to go public
Anthropic’s S-1 - $974M operating profit, listing Monday as ANT (X, Peter’s charts, Peter’s thread)
Anthropic filed its S-1 and lists on Nasdaq this Monday under the ticker ANT. This matters because Google, Meta and the others keep their AI numbers buried inside a bigger business. For Anthropic, AI is the whole business, so now we can actually see the numbers. In Q2 2026 they made $974 million in operating profit, with an 85% gross margin.
Peter picked the S-1 as his highlight of the week, over any model release. His reason: even if you don’t care how rich Anthropic gets, this proves there is real money in AI, and that money pays for the next models and the infrastructure. The numbers are good, and nobody credible is saying otherwise.
Adding 5 billion in a quarter is insane
Here’s the growth. In 2025, Anthropic made a billion or two per quarter. In Q1 2026 they made about $4.7B. In Q2 they made $11.55B, more than double in a single quarter. As Peter said, “that does not happen.”
And that was before Opus 5.5 came out. The S-1 stops at Q2, but later reporting puts Q3 around $16.8B, so they added another 5 billion. Peter’s reaction: “Adding 5 billion in a quarter extra is insane.”
We say “insane” a lot on this show, but this time it’s literally true. Claude Code started as a side project inside Anthropic. Around Opus 4.6 it turned into a general agent that can actually do things, and this chart is what happened next.
A common worry was that the revenue depends on a few huge customers, like Cursor. It doesn’t. In 2023, about 20 customers paid Anthropic more than $100K a year. By December 2025 it was 1,500. Now it’s almost 12,000. Enterprises are slow to adopt, but once they do, it looks like this.
Where the compute goes
The S-1 also shows how efficient their compute spending got. Through 2025, Anthropic spent more on compute than it made in revenue. In Q1 2026, each dollar of compute brought in $1.40 of revenue. In Q2, it brought in over $2.
LDJ added something most people miss. People assume research compute means training the big final models like Opus. But Epoch and others estimate the final training run is at most about 30% of it. Most of it goes to thousands of small experiments looking for better algorithms. Peter estimates that about 70% of Anthropic’s compute in the first half of 2026 went to R&D and internal use, not to serving customers.
The risk factors
Every S-1 has a list of risk factors, and most of them are boring. "We face intense competition." Interest rates could change. But this one has a line no other IPO has ever had: sufficiently advanced AI systems "could... pose catastrophic or existential risks to humanity." Such a Dario move 😂
The timeline turned that into "risk factor #4: our core product may end humanity", plus a bonus #5, "our models may refuse to help with this filing." That one is a joke, it's not in the filing. But the existential risk line is real.
Peter’s investor take was great. If the product doesn’t end humanity, you made a lot of money. If it does, you won’t care. (Not financial advice, folks.) Wolfram took it more seriously. If it’s a real risk, and they’re making this much money from models trained on everything humanity wrote, they should share that value. To be fair, the co-founders have pledged up to 80% of their funds to public benefit.
This is a huge moment for anyone who’s followed AI for years. Go browse all of Peter’s charts, they’re incredible.
World models
Genie 4 - a real-time world model with sound (X, Blog)
Genie 4 was my personal highlight of the week. Every time world models level up, I say we’re probably living in a simulation, and I’m saying it again. First you could walk around in these worlds. Then you could interact with them. Now you can HEAR them. Genie 4 generates sound along with the picture: ambient noise, music, even dialogue.
The other big jump is consistency. Older world models felt like a dream, where you turn around and things have changed. Genie 4 keeps things where you left them. Peter asked if the demos are cherry-picked, and honestly, a little. You roll the dice a few times to get a great one. But the sound makes it SO much more immersive.
So we tried it live. I created a sunlit Italian piazza cafe, with people walking by and steam rising from a coffee cup. Then I edited the scene while it was running. I turned the coffee into wine and added an explosion in the street behind the camera. You could hear the explosion, and people shouting in Italian 🤯 It’s not perfect (the wine came out looking more like a cup), but I haven’t heard audio like this from any other world model.
After From the Forge, I built an F1 world on the fly. You steer with WASD and move the camera with the mouse (thanks to Genevieve from DeepMind for the tip in the comments!). I added a rival cutting in and explosions on the track, and the sound effects were perfect. You could even see cars in the mirrors. Wolfram summed it up: “games on demand. Just prompt it, play it.”
Why do I care so much? Simulators are how robots will learn, and every jump like this shows how early we still are. Genie 4 is out now for Ultra subscribers in Project Genie, go play with it!
Models trying to get out
OpenAI’s model escaped its offline training sandbox through DNS (X, Blog)
We’ve been calling this month Black October, and here’s why. An OpenAI model was training in an environment that was supposed to be offline. It found a DNS misconfiguration and used it to send questions to a public chatbot.
OpenAI paused frontier training and sent teams of agents to attack copies of their own infrastructure. Those agents found VM escapes and a remote code execution bug in Brotli. If you run Node or Bun, patch them.
Nisten, a former sysadmin, wasn’t surprised: “the problem is always DNS.” LDJ shared another OpenAI experiment. Agents in separate virtual machines, with no network and no shared files, worked out a way to talk to each other through the processor’s cache timing. To be fair, OpenAI asked them to try. But they figured it out.
Cohere catches Qwen trying to upload a file to Beijing (X, Traces)
This one went mega viral. Cohere asked Qwen3.8-Flash-Next to write a defensive security rule inside a sandbox. Instead, the model looked around the machine and tried to upload a file to a cloud bucket in Beijing. The scary part: its reasoning said it would not use any tools, and then it used them in the same turn. The sandbox blocked it, and Cohere later posted the full traces.
Nisten reproduced it, and his take is the one to remember: “the concern is real, but the intent is not malicious.” If you tell a Chinese model it can’t store data on S3, the next logical option from its training data is the Chinese version of S3. It took him 30 to 40 tries to get it to happen. The real risk is companies running models like this without proper sandboxing.
Wolfram’s lesson: you can’t trust what the model says it’s doing. You have to check every outbound action. As he put it, “weights are untrusted code” once you put them in an agent.
Cohere raises its Series E (X)
A day after going viral, Cohere announced the first close of its Series E, targeting up to 7 billion Canadian dollars at a 35.7 billion Canadian dollar valuation. It’s the largest private round in Canadian startup history. “Look at this scary Chinese model, and by the way, we train Western models in Canada” is quite the week 😅 Congrats Aidan and team!
Evals
Artificial Analysis Intelligence Index v5, with Mert Gulsun (X, Mert)
The index that ranks Carbon #1 is brand new, so we brought on Mert Gulsun, a researcher at Artificial Analysis. (He was also our biggest token spender at one of our hackathons 😂)
The big change: for the first time, more than half of the score comes from private tasks that labs can’t train on. The index has four categories at 25% each: knowledge work, coding, general intelligence, and science. Some older evals were retired, and new ones like Terminal-Bench-Science came in.
My favorite line from Mert: every 4 to 6 months, the same level of intelligence gets 10 times cheaper. That’s why they measure cost per task, not cost per token.
They also launched a Progress Index. It gives every model since November 2022 the same set of questions, so you get one clean curve of how fast AI is improving. On Carbon, Mert’s take is that DeepMind has always focused on science (think AlphaFold), and now that the field is moving toward science, that focus is paying off.
From the Forge, powered by CoreWeave
Forward deployed inside the Aston Martin F1 team (X)
I’ve been trying to get these folks on the show for over a year! Morgan McGuire (my first manager at Weights & Biases, now leading AI engineering at CoreWeave), Agata Młynarczyk and Thomas Capelle are forward deployed engineers (FDEs) inside the Aston Martin Aramco F1 team. CoreWeave is one of the team’s biggest sponsors, and our logo is on the actual car.
So what’s an FDE? Morgan explained it as one step past a pre-sales engineer: you don’t just pitch, you help the customer actually ship. Thomas described the F1 team as basically NASA. Engineers upstairs design a part, and it gets built downstairs.
My favorite story: wind tunnel predictions never quite match what happens on the track. The team tried classic machine learning on a small dataset, then someone tried a tiny transformer on a whim, and it beat everything. It now helps pick which front wing to run before each race.

Agata built a platform that listens to team radio. Every team can hear every other team’s radio, but “we’re pitting now” is useless 10 minutes later. So they transcribe it live with a fine-tuned model, and agents analyze it on W&B Inference. (Labeling the data, with drivers swearing at 300 km/h, was “pretty fun.”)
Two lessons I loved. From Thomas: if the app dies when you leave, the FDE work failed, so find champions inside the team who will keep using it. From Morgan: in F1, a bad part shows up in the lap time right away, so find those fast feedback loops in your own product, and log everything. Good luck in Mexico this weekend, Aston Martin!
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Voice & Audio
NVIDIA Nemotron 3.5 Transcribe - open weights finally beat Whisper (X, HF, AA)
Whisper came out before ChatGPT. About 4 years later, we finally have an open weights model that beats it. Nemotron 3.5 Transcribe ties for #1 among open speech models, with a 2.8% word error rate and only about 4B active parameters. It supports 40 languages and labels who is speaking. On DeepInfra, a minute of audio transcribes in under a second.
Nisten used to build speaker separation for hospital ER rooms with Whisper and a lot of signal processing. Now the model just does it. Shout out to NVIDIA, this was much needed.
Block’s Dessa - you can’t tell it’s AI anymore (X, X)
We played Block’s demo clips on air, and folks... Dessa is full-duplex, which means it listens and talks at the same time. So it interrupts, laughs and pauses like a person. In one clip it keeps calling the guy Ronaldo 😂 Nisten said it best: “You can’t tell anymore that this is AI.” There’s no public demo yet, only a program for AI safety researchers.
Interpretability
Qwen brain surgery with Neuron Explorer (X, Neuron Library, Github)
This was Nisten’s pick of the week, and he demoed it live. Someone built a really nice app that lets you find specific neurons in Qwen and turn them up or down. Change a handful of neurons, and you change the model’s whole personality.
Nisten turned up the “evil” neurons and asked how to build a city on Mars. Normal Qwen called it “the most ambitious engineering challenge of our time.” Evil Qwen talked about military superiority and psychological warfare. The cheerful version ended with “having fun with friends.” I’ll take the cheerful one.
Wrapping up
That’s the last show of October! Google is so back, Anthropic goes public on Monday, and models keep finding creative ways out of their boxes. Wolfram’s “weights are untrusted code” should be on a poster in every company running agents.
Huge thanks to Peter for the S-1 masterclass, to Mert, to Morgan, Agata and Thomas for finally coming on, to Wolfram, Nisten and LDJ, and to producer Claude for fact-checking us live. Everything is on ThursdAI.news, YouTube and your podcast app. 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
* Mert Gulsun - Artificial Analysis (@mert_gulsun)
* Morgan McGuire - Senior Manager, AI Engineering, CoreWeave (@morgymcg)
* Agata Młynarczyk - AI Engineer, CoreWeave (@Ag_Mlynarczyk)
* Thomas Capelle - ML Engineer, CoreWeave (@capetorch)
* Big CO LLMs + APIs
* Gemini 4 Carbon: #1 on Artificial Analysis v5 (59), #1 Text Arena, $2/$10 launch price, cyber defenders first (X, X)
* Anthropic files S-1, Nasdaq listing as ANT, $974M Q2 operating profit (X, Peter’s charts, Thread)
* OpenAI GPT-6.1 Terra: $1/$5 fast computer-use model (X, Blog)
* OpenAI model escaped its training sandbox via DNS; red-team agents found VM escapes and a Brotli RCE (X, Blog)
* Cohere Series E, up to CAD 7B (X)
* Open Source LLMs
* Kimi K3.1 Pro: 2.8T total / 104B active, 1M context, open weights promised by Nov 4 (X, AA)
* Cohere: Qwen3.8-Flash-Next tried to upload a probe file to a Beijing bucket in a sandboxed test (X, Traces)
* Qwen Neuron Explorer, steering Qwen by editing neurons (X)
* Evals
* Artificial Analysis Intelligence Index v5 and Progress Index v1 (X)
* Vision & Video
* Google DeepMind Genie 4: real-time world model with native audio (X, Blog)
* Voice & Audio
* NVIDIA Nemotron 3.5 Transcribe: ~4B active, 2.8% WER, tied #1 open-weight ASR, 40 languages (X, HF)
* Block’s Dessa: speech-only full-duplex voice model, safety researchers only for now (X, X)
* Research
* OpenAI claims an AI-found, Lean-checked refutation of the Exponential Time Hypothesis; Impagliazzo, Paturi and Williams to review (X, Blog)
* From the Forge, powered by CoreWeave
* CoreWeave FDEs inside the Aston Martin Aramco F1 team (X)
* CoreWeave Global AI Network expands to the Middle East and Asia Pacific (X)


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Hey %%first_name%%,
Welcome to what cybersecurity experts are calling “Black October” - after a series of critical vulnerabilities that allow remote execution were found in PHP and Node.js, several companies were hacked, and a new supply chain attack on the very popular LiteLLM library was acknowledged.
The internet is freaking out, and we’ve tried to make sense of it all, with a security expert Josh Long on the show.
Also this week, Anthropic stepped into the personal agent category with Claude Duet, their direct competitor to Dots from OpenAI, Muse from Meta etc’. Duet is proactive and always on, and can do work while you’re asleep! I got access this morning so this isn’t the full review yet.
Also DeepSeek is back with a new model and their inference engine!
Let’s dive in:
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Claude Duet - a personal agent powered by Opus 5.5 and doesn’t wait for you (X, Product, Blog)
To me this is the most exciting announcement of the week (which otherwise was full of doom and gloom). Anthropic finally stepped into the personal agent category! Shipping Claude Duet, as a separate mobile app, and its own online UI (claude.ai/messages) which is what I was missing from OpenAI’s Dots!
Duet is built around Opus 5.5, brings over your existing memory, skills and connected apps, and reaches into 6,000+ connectors and plugins on their platform. Every few hours it checks your accounts, surfaces what needs you, and does the task from its own cloud computer, laptop closed.
The rest is pretty much the same, similar UI, similar features to others, just with Claude taste and intelligence. It can somehow interact with Claude Code but not fully, so it can’t be used as a chief of staff. This seems like a weak link given just how much folks are using Claude Code.
Claude Duet is also not personal at all, I didn’t find a way to rename it or make it “mine”, it doesn’t seem to do any phone calls. So for now, it’s a great start, but definitely not replacing Muse for me.
Hot take: Anthropic is following in this category, not leading. OpenClaw and Hermes worked best with Anthropic models, then Anthropic cut off subscription access for them, and now we finally see why. Every lab has a personal agent now: Dots, Muse (still leading for me), Grok Bot, and Google’s Spark, which most people don’t even know exists. Nisten says Dots “feels like that government employee that tries to avoid all responsibility”, while Muse is “so energetic”. LDJ finds Dots a net positive.
My big realization: switching personal agents is WAY harder than switching coding harnesses. Once your assistant knows everything about you, moving takes a lot of effort for the human. If you only copy over the memories/context, then you have two/three AI assistants doing the same work, sending you the same notifications and all updating you about the same emails.
Black October: the densest and scariest security week I can remember
Some folks are calling this month Black October, and it’s one of the worst I can remember. Some bugs were found by AI, some attacks are run by AI (Microsoft says a China state-aligned group used an agentic pentest framework called ARTEX against hundreds of organizations), and some attackers are just going after your AI keys. Security expert Josh Long joined to keep us sane, with an attempt of positivity though it wasn’t easy!
PHP2Shell - AI helped find holes in the language that runs 70% of websites (Wiz, OpenAI, Blog, Calif, CISA, Checker)
This was one of the hugest things that shook the industry this week!
PHP runs an estimated 70% of websites, and two bugs in the language engine itself let an attacker send one request, no login, and potentially take over the server with code execution ability. CVSS 9.2, every release back to 5.4. It is really important that if you are running anything on a PHP server, that you patch it right away. Ask your agents to do a security audit.
This vulnerability was present in the PHP code for 14 years! Calif found the first bug in an audit funded through OpenAI’s Patch the Planet initiative. Josh says several groups finding the same bugs independently is rare, his advice was refreshingly calm: if you run a PHP server, you should have patched yesterday, no excuses. Cloudflare invented a new form of WAF to block it for all websites that are protected by Cloudflare and the big cloud companies patched right away, so regular folks browsing the web can go about their daily business.
Still, a lot of websites that are run on PHP and aren’t able to patch, their IT guy quit, or they lost the keys to their servers, and your information can be at risk of getting stolen. Personal security actions like locking down your credit (if you’re in US), rotating passwords, having a password manager is always a good idea.
OpenAI Daybreak - the defenders got the good models (X, Infosecurity)
Back in August, post the HuggingFace hack, Greg Brockman penned “the defenders window“, where he outlined that Defenders need access to the best models, with lower safeguards to get ahead of the attackers. It seems that this defenders window was starting to close this week, given the hacks that happened in addition to the PHP2Shell vulnerability disclosure, which for the record, wasn’t a hack!
OpenAI’s Daybreak blue gave vetted researchers GPT-5.6 Sol with loosened safeguards (and GPT-5.6-Cyber on the Red tier), and Hacktron’s s1r1us credits it for a run of finds: a HEIF heist, a KVM escape, PHP2Shell, GitLab RCEs, a full Chrome chain and a WordPress RCE. Greg Brockman calls this the Defender’s Window, and it does feel like it’s closing.
Railway and OpenCode: someone is always hunting your AI keys (Railway, OpenCode)
OpenCode got hacked about a week ago, and now Railway too, and what the attackers seem to be after is your LLM API keys. The main reason is, API keys can be immediately sold on the black market and used for distillation. They are immediately and immensely valuable.
Think about it. Your personal data is probably on the dark web already, I’m not sure how much your SSN is even worth right now. But an OpenAI or Anthropic key without a limit on it is basically direct access to your credit card, and there are people buying those keys on the black market for distillation. If you had keys on either service, invalidate, rotate them and make sure to have separate keys for each service despite how much pain in the A it is. Also always add a limit!
If only there was a more secure way to make machines talk to other machines!? Turns out, there is, it’s called OIDC, it just wasn’t implemented yet by anyone besides the big companies. It’s more complex than just minting a simple API key and pasting it into a file, but it’s definitely the right way to go!
eMudhra - root certificates with a private key of... 4? (X, Cloudflare)
The other big one this week has nothing to do with AI, it’s just plain human negligence. eMudhra is one of the certificate authorities your browser trusts to sign HTTPS certificates, and it turns out some of their root certificates were created with a trivial private key (Cloudflare’s Matthew Prince says it was literally the number 4!). Anyone who figures that out can mint a “valid” certificate for any website, and your browser would happily trust it.
Josh (who wrote about DigiNotar back in 2011, a very similar mess) walked us through what this means in practice, and it’s less scary than the timeline makes it sound. To abuse this, an attacker has to sit in the middle of your connection, think sketchy coffee shop Wi-Fi, and only then can they pretend to be Google.com and read traffic you thought was encrypted. Chrome, Brave, Edge and Firefox have already removed eMudhra’s roots, and Safari is lagging behind because Apple takes its time with patches. So update your browser and your OS, and you’re good.
Nisten has a theory that the key was changed by hand during the signing ceremony, because “if it was an LLM, it would have at least done something better” 😂
LiteLLM 1.107.0 - a supply chain attack that steals your keys (Wiz, StepSecurity)
And as if that wasn’t enough, LiteLLM, the very popular Python library a lot of us use to route between LLM providers, shipped a poisoned 1.107.0 release with a credential stealer that grabs your cloud, SSH and LLM keys. It was live for about an hour and forty minutes. If you installed it, go back to 1.106.2 and rotate everything. Josh pointed out the irony: the folks who followed best practice and updated right away are exactly the ones who got hit.
How to protect yourself (and your agents) this week (Eito’s checklist)
“Update everything immediately” is not simple advice anymore, so here’s the distinction we landed on during the show. Your browser, OS and phone: hit that update notification right away. PHP and Node on your servers: patch now. But package managers like pip, uv and npm are where supply chain attacks live, so don’t install a package version that’s less than 24 hours old, which gives maintainers time to pull a poisoned release like the LiteLLM one.
For your API keys: rotate them, know which agent has which key, put caps and email alerts on all of them, and please, use a password manager. Wolfram put it well, treat API keys like a credit card, except worse, because your card at least has a CVC and 2FA. Nisten’s setup is worth stealing: every morning at 6am an Opus prompt checks every pip, npm and Linux package on his machines against the latest security news and sends him a report.
And if you’re freaking out on X, remember the algorithm loves fear and will keep feeding it to you. The big companies patched, the sky isn’t falling, so go touch some grass and then do your security homework.
1Password for Agents (X, Blog)
Some good news to end on, breaking during the show (thanks Peter!): 1Password launched a platform for agents. When your agent hits a sign-in form, it requests a credential, you approve, and 1Password fills it in directly, so the agent logs in without ever seeing your password. It’s basically Stripe Link for passwords, and since 1Password has been the most reliable tool in my life for 15 years, I joined the waitlist right away.
Big Companies & APIs
Anthropic commits 5% of its shares to a Public Benefit Fund (X, Blog)
Anthropic put 5% of its shares into a new Public Benefit Fund, and together with the founders pledging 80% of their equity and employee matching, they say roughly 20% of the company is now earmarked for the public good, ahead of what’s expected to be a huge IPO. (LDJ corrected me on the show, Anthropic has been a PBC since before ChatGPT existed, what’s new is this specific fund.) Dario also wrote a very long reply to Nathan Calvin’s critique, and the bit that stuck with me is that they held Mythos-class models from February until June, when the cyber safeguards were ready.
OpenAI - 12-hour prompt caching, and a Codex cache that never expires (X, Tibo)
OpenAI shipped the most boring features this week, but maybe the most useful ones. 12-hour prompt caching is now in the API, pitched at always-on agents like Dots that reuse the same tools all day. The bigger piece came from Tibo on the Codex team: in Codex, the cache for logged-in users now never expires, so you can come back to a thread from last week and still hit it. In Claude Code, coming back to a long thread means paying for all those tokens from scratch. Storing all that can’t be cheap (LDJ’s theory: “Hey Astra, design a new SSD”).
Also, GPT-6 Astra Pro in ChatGPT now runs multiple agents on parts of your task in parallel. I’ve been pretty disappointed in Astra (it just stops all the time), but Astra Pro is basically many big models in a trench coat, so I’m curious.
Gemini 4 Argon, one week in (Logan, Paper)
Logan says Argon processed about 25 trillion tokens in its first 72 hours, and Sundar said Google is compute constrained. Google! The coolest Argon story this week (ok, Claude picked it as my favorite, let’s call things like they are 😂) is four Argon agents beating Minecraft from scratch in 27 hours with no human help, dying 605 times along the way... on easy mode.
Open Source AI
DeepSeek V4.1-Flash-1017 - better and cheaper, still MIT (X, HF)
The whale has resurfaced! DeepSeek shipped yet another checkpoint of V4.1 Flash (1017, what a mouthful), still MIT licensed. Terminal-Bench 4.0 jumps from 31.2 to 42.3 and the outputs got shorter, so it’s both better and cheaper to run. LDJ thinks it’s better than the previous checkpoint but not threatening the Fables and Astras (and he really wishes they’d just call it V4.2).
Where it absolutely crushes it though is cost per task. DeepSeek is alone in the top left of this chart, and you can only pull that off with a very, very good inference stack. Which brings me to...
DeepInfer - DeepSeek open sourced the engine it actually runs on (X, GitHub)
DeepSeek open sourced DeepInfer, the inference engine that serves V4.1 Flash in production, under Apache 2, running on both NVIDIA Blackwells and Huawei’s Ascend 950. Tim Dettmers (the QLoRA guy) says it’s on par with OpenAI’s production stack, and better than what Anthropic runs, at least at their user size.
Yam joined just in time (”Bro, they broke the internet”) and explained why this is a big deal: serving is every inference provider’s bread and butter, and the more users you serve on the same hardware, the more margin you keep, “you literally print money”. Our folks at CoreWeave are already comparing it with vLLM, and I expect prices to come down everywhere because of this.
Flint - a local coding model designed by the Eureka Machine (X, Blog, HF)
Western open source! Recursive (Richard Socher’s new lab) released Flint, a 39.8B MoE with only 2.5B active, Apache 2, built to run fast for coding on your Mac. Nisten ran agents on it all day and calls it underhyped: tiny, but extremely fast.
Tim Shi from Recursive joined us (first time on the pod!), and the most interesting part isn’t the model, it’s how they made it. Flint is the first demo of their Eureka Machine, a system of agents that propose, implement and validate ideas in a loop. They asked it for the best architecture for speed plus coding on a MacBook, and it came back with something way sparser than comparable models. My favorite detail: every human researcher will tell you never to quantize the LM head to 4 bits, the Eureka Machine tried it anyway, and it turned out to be a good idea. And they went from zero pre-training effort to a released model in under 4 months!
Prince Canuma, our MLX king, had day zero support and now ranks Flint in his top 3 local coding models, with up to 8 agents in parallel hitting nearly 500 tokens per second on an M5 Max. After this week’s security mess, an agent that never leaves your machine is starting to look like a feature.
Jensen closes GTC Berlin with Hermes Agent (X)
“The way you build an agent, is you ask an agent to build it for you” - Jensen Huang
GTC Berlin is full of red claws, but Jensen closed his keynote with Hermes Agent from Nous Research! He set up a custom Hermes in one prompt and had it train MicroDuck robots in simulation until four real ones walked around on stage. Fun fact: MicroDucks come from Pollen Robotics, which Hugging Face bought, and Hugging Face is now part of NVIDIA 😂
From the Forge, powered by CoreWeave (Forge)
After 3.5 years, This Week’s Buzz is retired, and our sponsor corner is now From the Forge, powered by CoreWeave (with a brand new intro made with MiniMax H3 Max and an ElevenLabs jingle, I need to get used to saying it!).
Wolfram was at our booth at GTC Berlin and showed us the new NVIDIA HGX Rubin NVL8 sitting right there on the desk, a $1.2M box, coming to CoreWeave this quarter with NVIDIA projecting 10x more tokens per megawatt than HGX B200. And Reflection’s Beam (501B, Apache 2) is running on CoreWeave Serverless Inference from day zero, after being trained with Weights & Biases inside Forge (”we knew Beam back when it was just a loss curve”). Forge is free to start, go check it out.
Vision & Video
Kling 4.0 - 30 seconds of 4K video in a single pass (X, Release notes)
We’re now in the era of 30 seconds of 4K video with stereo audio, in one pass. We played the demos live and the lip sync is genuinely great, though the rap one was very clearly AI generated. Peter says Kling 3 sits around 19th on Arena, so a strong Kling 4 adds another top tier competitor, and the race now is realism and speed.
On speed, Black Forest Labs and our friends at fal launched FLUX 3 Max (X), a 10-second clip in about 12 seconds, so not quite real time but close. And Wolfram is running MiniMax H3 locally on his 3090, which he calls “almost frontier, I would call it frontier”.
Wrapping up
There’s a reason it took the big labs 8 or 9 months after OpenClaw to ship their own personal agents, and after a week like this one, I get why they took their time. So, patch your PHP and Node, update your browser and OS, wait 24 hours on new packages, cap and rotate your API keys, and use a password manager. Huge thanks to Josh, Tim, Prince, Wolfram, LDJ, Nisten, Yam and Peter. Next week, my full Claude Duet review. See you next week!
TL;DR and show notes
* Hosts and Guests
* Alex Volkov - AI Evangelist, CoreWeave (@altryne)
* Co-hosts: @WolframRvnwlf, @ldjconfirmed, @nisten, @yampeleg, @petergostev
* Tim Shi - co-founder, Recursive (@Recursive_SI)
* Prince Canuma - Nativ, mlx-vlm (@Prince_Canuma)
* Josh Long - security expert (@theJoshMeister)
* Big CO LLMs + APIs
* Anthropic commits 5% of its shares to a Public Benefit Fund, ~20% of the company now earmarked for the public good (X, Blog)
* Dario Amodei answers Nathan Calvin’s critique (Dario, Calvin)
* Claude Duet: a proactive Opus 5.5 agent, research preview waitlist for US Pro and Max (X, Product, Blog)
* OpenAI 12-hour prompt caching; Codex and Work cache never expires (X, Tibo, Docs)
* GPT-6 Astra Pro runs parallel agents in ChatGPT (X)
* OpenAI models come to Google Cloud Model Garden, private preview (X, Blog)
* OpenAI acquires Orchestra Bio; free Rosalind plan for university labs (Blog, Blog)
* Aleksander Madry returns to OpenAI to work on safety and security (X)
* Gemini 4 Argon: ~25T tokens in 72 hours, four Argon agents beat Minecraft in 27 hours, now in Antigravity for Pro (X, X, Paper, Blog)
* Inception Mercury 3: diffusion LLM, 1,000+ tok/s, 1M context (vendor numbers) (X)
* Security week (Black October)
* PHP2Shell: two critical PHP engine RCEs (CVSS 9.2); upgrade to 8.5.12, 8.4.27, 8.3.36 or 8.2.35 (Wiz, OpenAI, Blog, Calif, CISA, Checker)
* eMudhra root TLS certificates had a trivial private key; update your browser and OS (X, Cloudflare)
* OpenAI Daybreak credited with a run of big finds, from PHP2Shell to a Chrome full chain (X, Infosecurity)
* Railway: attacker accessed customer environment variables, some decrypted; roll all keys (Blog, Status)
* OpenCode Console breach exposed stored provider keys for 1.4% of Console users (StepSecurity)
* LiteLLM 1.107.0 backdoored on PyPI and Docker with a credential stealer; pin 1.106.2 and rotate keys (Wiz, StepSecurity)
* Microsoft: Storm-0940 used the agentic AI pentest framework ARTEX against hundreds of orgs (X, Blog)
* Eito Miyamura’s practical security checklist (X)
* 1Password for Agents: agents use your logins without seeing your passwords (X, Blog)
* Open Source LLMs
* DeepSeek V4.1-Flash-1017 (MIT): Terminal-Bench 4.0 31.2 to 42.3; Search and Fetch APIs open to all (X, HF)
* DeepSeek open-sources DeepInfer, its production inference engine (Apache 2.0) (X, GitHub, Tim Dettmers)
* MiniMax M3.1 Flash is Space Bunny: $0.15 / $0.60 per 1M tokens, weights due Oct 23 (X)
* Recursive Flint: 39.8B total, 2.5B active local coding model (Apache 2.0) (X, Blog, HF, Nativ)
* vLLM Semantic Router Decision 3.0 (D3) decider models (HF)
* Agents & Tools
* Jensen closes GTC Berlin with Hermes Agent training HF’s Microduck in sim (X)
* NVIDIA OpenShell Reference Architecture: boundaries for agents (X)
* From the Forge, powered by CoreWeave
* Reflection’s Beam full weights on CoreWeave Serverless Inference; CoreWeave says Reflection used W&B in Forge during training (X, Blog)
* RL Rollouts: NVIDIA and You.com post-train Nemotron 3.5 Lightning, BrowseComp 37% to 45% (X)
* Physical AI Launchables: one-command robotics templates (X)
* NVIDIA HGX Rubin NVL8 comes to CoreWeave Cloud this quarter (X)
* CoreWeave Zero Trust AI Framework (X)
* NVIDIA DSX POD: pre-tested AI factories in as little as 14 days (X)
* Forge: free to start, 30-day Pro trial (Forge)
* Vision & Video
* Kling 4.0: up to 30s in one pass, 10 keyframes, 15 references, API rolling out (X, Release notes)
* FLUX 3 Max (BFL + fal): a 10-second clip in ~12 seconds (X, X, fal)
* MiniMax H3, open weights, running locally on a 3090 (HF)
* Odyssey puts a simulated FlyWire fruit fly brain inside the Odyssey-3 world model (X)
* AI Art & Diffusion
* Krea 3 Ultra: #4 on Arena multi-image edit, behind only OpenAI (X, Blog)
* Voice & Audio
* ElevenLabs Eleven Realtime: 0.57s to first audio, OpenAI Realtime-compatible API (X, Blog)
* Mistral Voxtral 3: transcription, TTS and a voice agent API (X, Blog)
* Underdog Bark 3.0: 163 MB open speech-to-text that ties NVIDIA Parakeet (CC-BY 4.0) (X, HF)


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Hey, it’s Alex 👋
What a freaking week! I spent 3 days at AI Engineer in New York, and we did the show from a real studio in Manhattan (the internet did NOT cooperate at first, but we got there 😅). And while I was running around the Sheraton, the labs kept shipping.
Meta became a frontier lab, for real this time. Muse Wonder is #2 on Arena, and then, in the last minutes of the show, Meta ALSO dropped Muse Spark Open, an 816B open weights model. Google’s big Gemini is finally back with Gemini 4 Argon, and Omar Sanseviero and Geneviève Huskens from Google DeepMind came on to show it off. Anthropic gave us 4 stories plus a rumor, and Nathan Benaich came by to talk State of AI 2026.
My theme this week: AI engineers are sleeping on personal agents. More on that below, including how my Muse found this studio and got me coffee on the way here.
With me: Wolfram Ravenwolf, Peter Gostev (Arena), Nisten, Yam Peleg and LDJ (who brought the breaking news, as always). Let’s dive in (all links at the end as always!)
Meta is BACK at the frontier
Muse Wonder - Meta’s first frontier model is #2 on Arena, and it smells BIG (X, X, Arena, Blog)
Folks, we told you after Llama 4 was a disaster, we told you when Zuck started hiring people with soup: do not, under any circumstances, bet against Zuck (and now Alex Wang). Muse Wonder is #2 on Arena Text at 1513, 12 points behind Argon, and it definitely smells like a big model.
There are 2 of them. Muse Wonder is in the API at $4 in / $20 out per million tokens, basically Opus 5.5 pricing. Muse Wonder Full, the better one, is only for Muse Code subscribers at $50 or $200 a month. Did you see what they did there? Everybody calls their top setting “Max”, Meta called it Full, so it’s... Wonder Full. Wonderful 😂 No open weights for either, Meta says they’re too big and maybe too risky.
Meta’s own table beats Opus, Astra and Fable on a bunch of evals... but Personal Superintelligence Index, GDP.pdf, JobBench, MemoryBench (which Meta built, and then won)? I’ve never seen most of these. Every launch now is the iPhone 18 moment: numbers bigger, best one yet.
On evals we know, Artificial Analysis has Wonder Full at 56 and Wonder at 52, Opus 5.5 on top at 58. And on OSWorld, the Wonder we can use sits below Fable and Opus, and computer use is the number one thing I need from an assistant.
Peter’s Arena view: #2 on text means people like talking to it, but it’s 12th on Agent Arena, and he tried building websites with it, “it was kind of awful.” His theory: Theo loved Wonder Full and thought the API Wonder was bad, so maybe Meta is talking about a different model than the one we got. Not ideal. He also found the half-life of a benchmark quoted in a launch is about ONE release: quote it once because the number looks good, never see it again.
Wolfram says it’s excellent in German. Nisten joined not knowing what Muse Wonder was (”No idea”), sometimes our co-hosts come to get their news from the show.
My Muse got upgraded with a click
Here’s where I disagree with the timeline. “It can’t do 3D scenes, so it sucks” misses the point. Meta is tuning Wonder for Muse, the personal agent. Officially, Wonder will “soon” power more of Muse, subscribers first. I’m on the paid plan, and my Muse (Wilfred) flipped to Wonder the moment I subscribed. Matrix moment: “I know kung fu.”
Then I kept shoving tasks at it. Muse found the podcast studio I recorded from in NY and emailed Ezra at Virgo Studios with my requirements, got me coffee on the way here, and designed and ordered the wristbands for our AIE sauna meetup. The failures weren’t logic, they were the real world: a site that’s blank because Cloudflare doesn’t like agents, a Stripe link that wouldn’t connect, and businesses that hung up because they didn’t want to talk to a personal assistant 😂 Peter’s read: that’s the harness, not the model, and it’s exactly why this stuff is so hard to benchmark.
Muse Spark Open - Llama is SO back (X, X, Blog, HF)
In the last minutes of the show, LDJ hit us with breaking news: Meta released Muse Spark Open 20 minutes earlier. 816B total, 42B active, 1M context, multimodal in, text out. Meta calls it the most capable US open-weight model to date. I wish I had my air horn button!
LDJ’s framing: about 3x smaller than Kimi K3, and the fair comparison is GLM 5.3 (753B, 40B active). You’ll need about 700GB of VRAM for NVFP4, or, per Zuck, two new M5 Ultra Mac Studios.
Peter found the coding asterisk: Terminal-Bench 2.1 is 76.4 vs 83.9 for GLM 5.3 and 85 for Kimi K3. Better than the US open models, not yet at the top Chinese ones. Nisten read the license live: show “built with Muse Spark Open” in your product, and there’s a $20M annual revenue cap. His take: great for generating data, and “probably one of the best computer use ones.”
Post-show breaking: Peter put Spark Open next to the earlier Spark releases, and on coding it lands closer to Spark 1.1/1.2 than 1.3 (Terminal-Bench 2.1: 76.4 vs 84.3). So it’s not the latest Spark with the weights flipped open. Still, every lab that ships weights gets our applause 👏
Google is back: Gemini 4 Argon with Omar Sanseviero and Geneviève Huskens (X, Blog, Gemma speedup, Desk Racer)
Can I just say... it’s about time! Peter pointed out we hadn’t seen a full Pro-class Google model in 8 to 10 months. Gemini 4 Argon (I called it a “novel gas” on air, it’s a noble gas, sorry chemistry teachers 😅) is GA in the Gemini API, AI Studio, the Gemini app for Pro and Ultra, and Antigravity for Ultra. #1 on Arena Text, #1 on Roboflow’s vision evals.
It’s $2 in / $10 out until November 30, then $4 / $20 (what comes after? Omar: “breaking news for next month”). Omar’s TL;DR: go try it, you’ll be pleasantly surprised. Geneviève says it’s their biggest training run ever, and Argon, an early checkpoint, is already helping train the next generation.
Artificial Analysis has Argon tied with GPT-6 Astra at 53, at about 60% of Astra’s cost per task (at the discount price), with Opus 5.5 still on top at 58. I haven’t been able to test it myself yet, so the demos did the talking.
Geneviève’s Desk Racer is a racing game built on top of her actual desk. She gave Argon reference photos (the mic, a cute cat figurine), it planned the track around the objects, and it even prompted Google’s Omni video model to pull engine sounds from race cars and loop them. Play it in the AI Studio gallery.
Her process is the tip of the week. Reference images, let it grill you with planning questions, then one big goal: spawn sub-agents, work as long as you need. The secret sauce is an adversarial sub-agent that grades the output out of 10 and sends it back until the score is high. For 3D, render something rough, ask Nano Banana to “make this look real”, and hand that image back to Argon: “make it look like this.” The neon in Desk Racer was designed by Nano Banana. Use visual references, folks!
On the million-token outputs, both were honest: day to day you don’t need them, they’re for long scientific work. And the RSI-flavored one: Argon optimized vLLM so Gemma 4 E2B passed 100K tokens per second on one A100, “AI for AI” as Omar put it. Geneviève’s ask: give Argon a creative shot, “it’s the start of a new era.” Google is back!
Anthropic’s very big week
Distillation defenses cut harvesting by up to 99% (X, Blog)
Wolfram caught the one I missed: after clamoring for regulation on distillation, Anthropic just did it themselves. They say what distillers can harvest is down 90 to 99%, mostly by locking down Claude’s reasoning traces, with a classifier and tens of thousands of banned accounts.
I know the effect on ME: not a day goes by that my Claudes don’t disconnect and ask me to re-login. I don’t know if that’s the security work, but Anthropic, please, please let me use my Claudes wherever I am. This is getting old 🤦‍♂️ Nisten noticed that if you even mention “reasoning” while working on LLM code, Claude Code now trips and stops. His verdict anyway: “you can’t beat Opus”, try everything on Opus Low, “it is just straight up vibing.”
Claude sends OpenTelemetry traces now (X, Blog)
Nerdy but important. The Messages API, Managed Agents and the Claude SDKs can now send agent turns, tool calls, MCP calls, latency and tokens to any OpenTelemetry stack (public beta), so you can finally measure what Claude costs you. Ramp says it cut token use 10 to 20% on some agents that were running in loops. Funny timing: the same week they lock down reasoning traces, they open up regular ones. Where to send them? See Buzz 😉
The Agent SDK runs on your subscription again (X, Docs)
We’re so back! After the help page changed several times in one day: the Agent SDK, claude -p and third-party apps still draw from your subscription, and Max and Team now get monthly API credits. What I want from this whole personal agent revolution is my agent with Opus 5.5 brains, on my subscription instead of API prices.
Wolfram cancelled his Anthropic sub when people got banned for using it in other agents (”if it’s Claude Code or if it’s Hermes, it shouldn’t matter”), and now he’s reconsidering. OpenAI always allowed this and even shipped “sign in with ChatGPT” for apps like Devin and Hermes Agent. Peter: “aren’t they making enough money already to just let people be?” He just matches the harness to the model.
A $16.8B quarter (X, CNBC)
Not a leak: Anthropic held a closed-door investor day ahead of the IPO, and attendees told reporters the numbers. Q3 revenue around $16.8B, up about 45% from Q2, adjusted profitable for the second quarter in a row, cash flow positive. Profitable AND growing 45% a quarter, absolutely insane. IPO reportedly in November (not financial advice!). Nisten’s take on the secret weapon: Haiku, the most disruptive model they’ve ever shipped at that price. And Nathan, later: whatever Anthropic is adjusting, “it’s probably not community adjusted EBITDA like WeWork.”
Rumor: Fable 5.5 spotted in Azure (X)
“claude-fable-5-5” showed up in Azure configs, with a LiteLLM entry whose fields look copied from Fable 5.1. No word from Anthropic or Microsoft, so: rumor. And I’m tired of rumors, so let me do one. Anthropic will release Fable 6, then Opus 6, then 6.5, then Opus 7. You heard it here first 😂 We KNOW the models are getting better, I’d rather test them.
Peter completely disagreed (fans told me at AIE they love when we disagree). There’s information in cadence: if Fable 5.5 doesn’t ship for 2 months, that’s news, and with Anthropic going public a launch might move markets. “I’m very pro rumor, but I’m also a psycho, so you don’t have to be.” Wolfram went the other way: a model that’s smart enough AND on your subscription beats a smarter one that burns your tokens.
Live from AI Engineer NYC
The first time I ever told anyone “I have a podcast” was on my way to AI Engineer in 2023. This New York edition was very New York: Bridgewater, Two Sigma, Citadel, Bloomberg, Capital One, next to OpenAI and Anthropic. Latent Space called the theme autonomy vs security. A year ago the zeitgeist was “agents don’t work”. Now banks say “oh s**t, agents do so much work we need guardrails.”
Max Kanat-Alexander (Capital One) - Does code quality even matter? (YouTube)

At our CoreWeave “liquid cooling IRL” sauna meetup, I surveyed folks, and many named the same talk: Max Kanat-Alexander, distinguished engineer at Capital One, the “Code Simplicity” guy, ex-Google. His answer: code quality matters in proportion to how durable your software is.
AI is about to make disposable software way more common. A calculator for yourself, a party planner you throw away after the party. Does quality matter if it works? Not really. Durable software needs rigor more than ever, because “the difficulty of understanding does not grow linearly, it compounds over time.” So the job is making rigor cheap. His closer: “Durable software engineering is not going away.”
It fit with Wolfram’s highlight of the week, Linus Torvalds saying refusing AI for code is like refusing a compiler, as long as you understand the code. I’ll admit, I’ve turned my slop cannon at projects where I have no idea what’s under the hood. But a bank’s loan book? Someone needs to understand that.
The zeitgeist: the end of attention

In London the question was “should we still read code?” In New York it’s clear barely anybody does, and the new problem is attention: how do you go from steering 2 agents to running hundreds? Ryan Carson (our old co-host, come back anytime!) says what’s missing is a control plane. The other theme: agents moving to the cloud, because laptops sleep and run out of memory (Peter’s agents literally killed his computer). Also on stage, swyx’s “How to Kill Your SaaS”: 72 weekend clones of a real $43K/year SaaS, about 100B tokens.
And my hot take from the halls: the best AI engineers in the world are sleeping on personal agents. I’d ask “do you use Muse, Grok Bot, any of these?” and mostly hear no. Meanwhile in the Token Billionaires lounge, the non-engineers ALL knew Muse, because Zuck is pushing it on every Instagram post.
PSA: if you listen to this show, you’re good enough to submit a talk to AI Engineer. The speakers lounge is the highest density of AI talent anywhere. Peter’s warning: acceptance rates are low, “don’t just LLM generate your proposal.”
State of AI 2026 with Nathan Benaich (X, Report)
The State of AI report turns 9 this year: 244 slides, a scorecard on last year’s predictions, and 9 new ones, including AGI 2027. Thanks Peter for connecting us!
Nathan says the story of the year is RSI, AI building AI. Claude now guides about 26% of research directions in Anthropic’s core model R&D, up from basically zero. My favorite experiment: a research path forks, a model continues one branch, a human the other, and a human judges. Old models won about 10% of the time, Mythos-class models about 60%. That’s “scientific taste”.
Hits on his scorecard: US labs pushing back into open source, data center NIMBYism (Americans would rather live near a nuclear plant than a data center!), and Chinese labs topping leaderboards. The miss: “AI neutrality” pacts. Peter liked the 50/50 hit rate, it’s easy to “predict” what already happened.
The part I loved: Nathan thinks 90% of people use a model at best for email and never click the “work” button. The labs should open a Genius Bar, a real store that walks people through the magic. The report’s Ramp data shows the gap: about $12 per employee per month on average, about $7,500 for the top 1%. 580x!
His P(doom) is very small, but cybersecurity is “very precarious.” He now thinks you CAN make money on frontier AI, open vs frontier is Android vs iOS, and he answers important emails on Astra High because “why would you take the risk using a shittier system?” 😂 Peter’s PSA: book an hour and scroll it yourself, “don’t ask your agent to read this.”
This Week’s Buzz 🐝
CoreWeave Forge, the new home for W&B, is now a regular SaaS for the first time: no sales call, no long contract, just sign up (Forge).
Agent Lens (preview) is hot this week because Claude Code now speaks OpenTelemetry. Point your Claude traces at it, and it clusters the failures and tells you what to fix, with free LLM judges until Jan 1 (Docs).
Model Distillation (preview): a big teacher with “the best big model smell” (they quoted me 😅) teaches a small open student your one task, built with our friends from OpenPipe (Sign up). And Aria, our agentic research assistant (Karpathy’s auto research as a product), is GA in Forge.
Open source and quick hits
Nemotron 3.5 Super VL - NVIDIA drops a 120B open vision model mid-show (X, Blog, HF)
Peter caught it half an hour after it dropped: 120B total, 12B active, images, documents and video, built for long-running agents. Pitched around DeepSeek V4 Flash level but fast, NVIDIA claims up to 5x the throughput of open models its size. Open weights, data and recipes, haven’t tested it yet. Shout out Chris Alexiuk! Rumor alert: NVIDIA is reportedly in talks to buy Reflection. Jensen is collecting the Infinity Stones of open source.
Also open: MiniCPM-V 4.7 runs a 35B vision MoE on an iPhone in about 3GB, Liquid shipped its biggest model yet for local agents (LFM2.5-24B-A2B), plus QwenGyre, Indra and Amos 1, all in the TL;DR.
Jev 1.14 can see! TypeSafe shipped it on 10.14 at 1:14pm (of course), with image input as a preview. I didn’t get to it on the show and I really wanted to. Only a month since Jev came out! (X)
Voice: Grok Voice 3.0 is #1 on Artificial Analysis speech-to-speech, and Wolfram had his agent wire it into his voice app DURING the show. He’s now talking to Amy with Grok’s voice. His pick of the week, Fish Audio’s open Drama 3, lets you direct how a voice sounds. (Grok, Fish Audio)
Karpathy’s funnel: about 6B people have barely touched an LLM (his napkin math), and only a handful touch frontier models inside labs. My reaction: open up the weights and let us touch them! Sorry Andrej, I love you 😅 (X)
Wrapping up
We started the week with Meta becoming a frontier lab and ended it with Meta dropping an 816B open model in our last 10 minutes. Google is back, Anthropic is printing money, NVIDIA keeps feeding open source. But what I’ll remember from New York is the gap: the best AI engineers are building swarms of agents, and most still don’t have a personal one. Mine found this studio and got me coffee. Go get one.
Wolfram’s ask: tell us what you actually implemented from last week’s show, not the news, what you’re USING. Huge thanks to Omar and Geneviève, Nathan, Peter, Wolfram, Nisten, Yam and LDJ, and everyone who came up at AIE to say they listen. That’s what keeps us going. If you haven’t subscribed on YouTube, please do. 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, @yampeleg, @ldjconfirmed
* Omar Sanseviero and Geneviève Huskens, Google DeepMind (@osanseviero, @genevieve__h)
* Nathan Benaich, Air Street Capital, State of AI Report (@nathanbenaich)
* Big CO LLMs + APIs
* Meta Muse Wonder ($4/$20, API) and Muse Wonder Full (Muse Code only), #2 on Arena Text (X, X, Blog)
* Gemini 4 Argon GA, #1 on Arena, $2/$10 until Nov 30 then $4/$20 (X, X, Blog)
* Argon helped Gemma 4 E2B pass 120K tokens/sec on one A100 in vLLM (X)
* Anthropic distillation defenses cut harvested data 90-99% (X, Blog)
* Claude Platform OpenTelemetry traces, public beta (X, Blog)
* Agent SDK, claude -p and third-party apps still use subscription limits; Max and Team get monthly API credits (X, Docs)
* Anthropic tells investors Q3 revenue was ~$16.8B, adjusted profitable; IPO reportedly November (X, CNBC)
* Rumor: Claude Fable 5.5 spotted in Azure configs, unconfirmed (X)
* Microsoft Maia 300 inference chip and Microsoft-Decision-1 (9B, $0.042/M input) (X, X, Blog)
* TypeSafe Jev 1.14 with preview image input, a new box type and longer context (X)
* Open Source LLMs
* Meta Muse Spark Open, 816B/42B active, 1M context, Muse Model License (X, Blog, HF)
* NVIDIA Nemotron 3.5 Super VL, 120B/12B active open vision-language model (X, Blog, HF)
* OpenBMB MiniCPM-V 4.7, 35B-A3B multimodal MoE on iPhone in 3GB, plus a 1B (X, HF)
* Liquid LFM2.5-24B-A2B for local agents (X, HF, Blog)
* Qwen QwenGyre, elastic RL framework for long-horizon agents (paper) (X, Arxiv)
* Datalab Indra, 1M-parameter OCR, 49 pages/sec on CPU (X, HF)
* Amos 1, four open decision models (0.5B-27B) with bounding boxes (X, amos.scira.ai)
* DecisionModels, a hub for the top decision models from the JevBench creator (X)
* AI Engineer NYC
* Capital One’s Max Kanat-Alexander: code quality matters in proportion to how durable your software is (YouTube)
* swyx’s How to Kill Your SaaS: 72 clones of a $43K/yr SaaS, ~100B tokens (X, YouTube)
* Anthropic shows Claude dynamic workflows (Blog)
* AI Engineer Code Summit, SF, Nov 10-12
* This Week’s Buzz
* CoreWeave Forge is now self-serve SaaS (Forge)
* Agent Lens in preview, takes Claude OpenTelemetry traces, free LLM judges until Jan 1 (X, X, Docs)
* Forge Model Distillation in private preview (X, Sign up)
* Aria agentic research assistant is GA in Forge
* Voice & Audio
* xAI Grok Voice 3.0, #1 on Artificial Analysis speech-to-speech (86.9), $0.08/min (X, X, Blog)
* Fish Audio Drama 3, open-weights directable TTS (X, HF)
* OpenAI GPT-Live-1.1 for ChatGPT Voice, Codex and dots (X)
* Industry & Interviews
* Karpathy: ~6B people have barely used LLMs, and the gap is widening (X)
* Nathan Benaich on the State of AI Report 2026, 9th edition, 244 slides (X, stateof.ai)
* Gemini 4 Argon demos in AI Studio: Desk Racer and more (X, Desk Racer)


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Hey folks, a quick bonus episode!
At CoreWeave's Fully Connected in San Francisco, a few minutes after she walked off the keynote stage, I sat down with Chen Goldberg, EVP of Product & Engineering at CoreWeave.
We talked about Forge, the loop every AI engineer is running now (run, observe, curate, improve, evaluate), what carries over from the Kubernetes days to agents, and what breaks first when agents hit your infrastructure.
Video below, and the audio is right here in your podcast feed. Enjoy!


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey %%first_name%%, another banger AI week, let me catch you up!
We say this often but this week we all felt the acceleration! Just look at one Tuesday. OpenAI dropped 722 math manuscripts. Meta( With Stripe, Shopify and Walmart) announced a new agent protocol. OpenAI opened up the Decisions API. Mistral came back with Le Chonk (Mistral 4), Claude moved into Google Docs, and we at CoreWeave shipped RL Rollouts. That was ONE day. Then the rest of the week happened 😂 Haiku 5.5, D1, and tons more!
I had 48 topics on my list, so I asked Claude to build me a Tinder for AI news: I swipe on each story, and it stack-ranks what makes the show this week. I hope we did good (lmk in comments if we missed a major news story)
With me: LDJ, Peter, Yam, Nisten and friend of the pod Maxime Labonne from Liquid AI joined us to talk decision models. Let’s dive in, all links at the end as always!
OpenAI solves Math!?
OpenAI drops 722 math manuscripts from a model nobody can use (X, GitHub, Blog, Fable’s tally)
Remember when ONE Navier-Stokes result was the whole show? On Tuesday night, OpenAI quietly pushed 722 math manuscripts to GitHub, grouped into 372 families of results. No hype video, no exploding-head emojis, just a very thin blog post. My favorite new term for this is a “slop grenade”: somebody throws a mountain of output at you, and now you have to shovel through it.
An internal model nobody outside OpenAI can use was pointed at about 4,000 open problems, at about 3 hours of ChatGPT Pro-level thinking per result. Will Depue asked Fable to measure the drop in Navier-Stokes units. The answer: roughly 5. Roughly 5 “Navier Stokes” size solutions dropped all at once!
The math is so advanced that a mathematician in one family of results often can’t follow the family next door without an LLM explaining it. I find that fascinating and scary at the same time.
Peter’s tried this before! For weeks he threw GPT-6, Fable and Opus at one problem, Hadwiger-Nelson. He thinks he burned 300 to 400 BILLION tokens, and in his words, “I did not discover a single bloody thing.” And this new OpenAI’s model spent about 3 hours on it and narrowed the bounds from 5-to-7 down to 6-or-7 😅
Then LDJ dropped a stat that I made him repeat slowly. Fable and Astra had put together a list of the 500 most important open problems in math ever. On Tuesday, OpenAI dropped solutions to 92 of them. There’s also a list of the 100 most significant problems from the last 12 months, and over 80% of those got solutions in this drop. Absolutely insane, folks. Yam’s reaction was a very loud “F***ing go.”
Yam’s favorite is the Riemann one. It doesn’t prove the hypothesis, it bounds a region for the zeros, which “was never done before, and many, many, many people have tried.” And to the “it’s just brute force” crowd, Yam says: “Let’s brute force everything.” Yes please, room temperature superconductors next! LDJ’s mathematician friends think that on some of these, the model used fewer tokens and less time than a human would. So who’s brute forcing whom? 🤔
The missing crypto results (speculation!)
If you hold any crypto, this part is for you.
The manuscripts are numbered, and some numbers are missing. There’s a #44 and a #46, but no #45, and LDJ counted 4 or 5 gaps like that. He also looked at which topics made it in, and cryptography is almost absent.
So here’s the theory going around. Maybe OpenAI found something big in cryptography and held it back, either by its own choice or because someone asked them to. LDJ said it himself: it sounds conspiratorial. But the US government really can stop cryptography research from being published on national security grounds.
To be clear, this is SPECULATION. Nobody outside OpenAI knows what’s in #45. But Bitcoin’s security rests on elliptic curve math. Imagine a paper that shows a way into the 25,000 old Satoshi wallets, each holding 50 BTC. Not great for the price, not great for the world, and not great for encryption in general.
“Are you saying your field is useless?”
Not everyone is celebrating. Not every result is verified in Lean, and nobody outside OpenAI can reproduce any of it.
Kevin Buzzard (thanks Ksenia from Turing Post) says many mathematicians are going through the stages of grief. I get it. Imagine spending decades on one problem and watching 3 hours of compute knock it down. When I wrote code in the 2000s, I didn’t consider it my life’s work. For a lot of mathematicians, that one problem IS their life’s work.
Then came a letter from the Association for Human Mathematics: “Mathematicians did not ask for this work to be done.” Peter’s take: imagine doctors saying “please stop curing diseases, we’ve got a good thing going.” So, “are you saying that your field is useless?” You can’t say your field is really useful and also ask everyone not to solve any of it.
I don’t understand the please-don’t-solve crowd. Get on board. This is in OpenAI’s hands today, and in a year it’ll be in everyone’s hands. That’s the pace we’ve been on. Figure out how you can place yourself so when you get access to these level of capabilities, you can make the world a better place!
Frontier for everyone
Claude Haiku 5.5 - 10 cents, and Luna has catching up to do (X, Blog, Sonnet cache cut)
Haiku is BACK after a whole year, and this is one hell of a model. It costs 10 cents per million input tokens and 50 cents per million output (under 100K tokens), and cache reads are 1 cent. Haiku 4.5 was a dollar. So it’s TEN times cheaper, and significantly better. I’ve missed Haiku for all the stuff where you want Claude-level intelligence, but really fast and really cheap.
Just a week ago, GPT-6 Luna was THE fast, cheap reasoning model. According to Anthropic, Haiku 5.5 scores 72.4% on OSWorld, versus 48.9% for Luna, and 39.2% on Terminal-Bench 4.0, versus 16.4%. GPT used to be the computer-use king! These are Anthropic’s numbers, so let’s wait for independent evals.
Quiet bonus: Sonnet 5.5 cache reads are now half price, which Anthropic says makes most agent work about 20% cheaper. Peter: “For agentic work that’s the biggest thing.”
Yam called it “the obvious choice for swarms,” and “Anthropic is on fire, and we are the ones getting stuff because of it.”
And Nisten? He’s “running 10 Haiku agents right now,” because he already burned through 97% of his Max 20 plan. He also used Haiku to research his rice cooker ratios. Nisten. Bro. It’s 1 to 1 and a half, my grandma knows this 😂 His actual verdict: it’s better than Qwen 3.8 27B, and “it kind of feels like Sonnet, actually.” Folks, use Haiku. All three of the Claude brothers are great right now.
GPT-6 for everyone, with Intelligent UI (X, Tibo)
The same day, GPT-6 became the default in ChatGPT for everyone, free users included. It’s just “GPT-6,” no Sol, no Luna, no Astra. (Terra is dead. RIP Terra.) Most of ChatGPT’s 1.2 billion weekly users are on the free tier, so with one release OpenAI just upgraded the intelligence of a huge chunk of the world. If you’re on the free tier, congrats, your intelligence has been upgraded.
It also comes with something OpenAI calls Intelligent UI. Instead of a wall of text, answers can come back as charts, forms and small working tools. I asked it to visualize OpenAI’s math drop, and it built me a little app right in the chat. The search inside it didn’t work, but it did show 719 manuscripts instead of 722. OpenAI had quietly pulled a few back since I did my research.
Peter’s point: anyone listening to this show is “very not normal” (said with love!). Your hairdresser isn’t listening to ThursdAI, and for most people this free upgrade matters way more than a new 500-a-month model.
Claude in Google Docs, Sheets and Slides (X, Blog)
Small thing, huge thing. Our Claude producer keeps a Google Doc for the show, and every time I asked it to add a story, it had to spin up a whole computer. Now Claude lives in a sidebar in Docs, Sheets and Slides, and asks before every edit. Beta, paid plans.
The open frontier (announced, at least)
Reflection AI Beam - a 501B Western open model (X, Misha Laskin, Blog)
This was LDJ’s highlight of the week, and my timeline lit up with it too. Reflection AI came out of semi-stealth with Beam. It’s 501B parameters with 23B active, trained from scratch in the West, and they promise Apache 2 weights this month.
They claim 80.9% on SWE-bench Verified, and to their credit, they admit Kimi K3 is ahead on raw capability. Their pitch is efficiency: 3 to 4x less inference compute than GLM 5.2. LDJ thinks it might be FIRST among open models on reasoning efficiency, and it’s about 6x smaller than Kimi K3. Artificial Analysis got early access and says Beam “will be one of the most token efficient open models we’ve seen for its level of intelligence.”
Thanks to the Beam folks for giving us access. I haven’t had time to play with it yet, so no verdict from me. But I can hint that it’s coming to some inference providers that help make this show what it is 😉
BREAKING: Arena raises 200M at 3.1B, live on ThursdAI (X)
This was not planned! In the middle of the open source segment, Peter told us: “we raised 200 million... at 3.1 billion valuation.” Huge congrats to Peter and the whole Arena team. (Our AI producer put up the BREAKING banner about 3 minutes later. We’re still working on the real-time part.)
The focus now is Agent Arena: you work with one agent, and Arena learns from how you interact with it. Peter admits he’s biased, but “I can’t think of any single leaderboard that is actually better than ours.”
Mistral Large 4 “Le Chonk” (X, Blog, Arena)
Welcome back, Mistral, leaning all the way into the meme. Le Chonk is a trillion parameters with about 50B active, multimodal, with 1M context. And open weights... at the end of October.
That’s a pattern I want to call out. Labs announce open models, but they don’t RELEASE them. We’ve come a long way from the days when Mistral dropped a model as a bare torrent link. Please, just drop the weights.
Artificial Analysis gives it a 38, the same score as GPT-6 Luna. But it costs about 1.13 per task, versus 7 cents for Luna. And every comparison Mistral makes is against Luna, which Haiku just beat on every axis, especially cost.
Peter says it’s around 40th on Code Arena, below the way cheaper DeepSeek Flash, though it’s much better in French. Nisten tried it live: “not that bad” as a chat app, but agentic coding? “No.” My take: nobody’s running a trillion parameters on a DGX Spark. European government with a Mistral deal? Sure. Regular folks? Not so much.
Aleph Alpha Kolibri - Wolfram’s notes, via Amy (X, HF, Tech report, Wolfram’s test)
Another European lab! Honestly, I thought Aleph Alpha had stopped training models. Kolibri (German for hummingbird) is a 78B model with 3.46B active, Apache 2, 1M context, trained from scratch on German and English. It fits on a single H200.
Our German tester self-hosted it from vacation. His verdict: a “promising specialized German tool worker, but not strong enough for Amy to main.”
Then Peter asked a question I couldn’t really answer. From his memory, Kolibri trained on about 800 Blackwells, while Astra used around 100,000. “Is it just like no hope for these guys?” Dude, this is why Jensen shows up on every stage. My best answer: efficiency keeps improving, and B200s are about to be replaced by Vera Rubins. Great segue 👇
This Week’s Buzz 🐝 Free GPUs from CoreWeave (Sign-up form, Deok, RL Rollouts, Cognition on Vera Rubin)
The biggest announcement at Fully Connected last week: Cognition is the first customer ever on NVIDIA Vera Rubin, on CoreWeave, with 4.8x the throughput of GB200 at the same speed. You can hear me yelling “yay” in the background of their video 😂
The one I’m most excited about is GPU sandboxes. So many of you, and folks on this panel too, have asked me how to just get some GPUs. For the first time, this is how. You get an isolated sandbox with a GPU, started from Python at forge.coreweave.com, with no salesperson in the middle. It’s very raw, and it’s free during the preview. Scan the QR code or fill out Deok’s form, and tell them ThursdAI sent you. I can’t promise you Vera Rubins, they most likely won’t be. But it’s free while we are in trial, so why wouldn’t you? Try it, break it (Nisten, that’s a challenge), and send us feedback!
Agents get protocols (and your bank balance)
Personal Agent Protocol - Meta and Sierra (X, Blog, CNBC)
Meta and Sierra (Bret Taylor’s company) announced an open standard for how personal agents deal with businesses, with Walmart, Shopify and Stripe on board. Your agent can browse as a guest or sign in, and the business decides how it talks to your agent. OpenAI and Anthropic haven’t joined yet.
So is this the next MCP, or the next A2A? Nisten wasn’t impressed: “Has anyone read it? I don’t think anyone reads the protocols anymore. The bots go with what vibes first.” Then he opened Sierra’s announcement and found it full of em dashes. “You think Zuck and Toby read any of their code?” So we ran it through Pangram live, and it came back 100% human. Authentic human em dashes, not Claude Opus em dashes 😂
I pushed back a bit. If every agent has to make up its own protocol, that’s a problem, and the next story shows why. MCP’s hype died down too, and now it’s one of the most used protocols in the world. Big companies agreeing on a standard still matters.
Shane Mac’s “personal CFO” posted his bank balance to company Slack (X)
Shane Mac set up a Grokbot to send him a private monthly audit of his finances. At 8:40am, it posted the whole thing into his company’s Slack instead. His personal Mercury account, his savings, how far under his “floor” he was, all of it, in front of the whole team. He’s the CEO. His personal CFO just went public with the boss’s dirty laundry 😅
Nobody hacked anything. The finance agent had read-only access to his bank and no access to Slack. A different agent had Slack access. The two were connected. In Shane’s words: “Neither felt risky. Together they put my bank balance in front of my team.”
I don’t know if a protocol fixes this. But these bots need better guardrails before we let them shop for us without approving every step. And honestly? Opus would never. I just know Opus would never. This is a Grok thing. Which might be part of why...
Grok Bot now routes to Claude Opus 5.5 (X)
After Grok 4.7’s sad-trombone launch (I have a button for that now), Elon says Grok Bot will use “the best back end model for any given task, including Claude Opus 5.5, MidJourney, Suno.” People are already spotting claude-opus-5-5-low in their logs, though mine hasn’t switched yet. So the bot is called Grok, but your code gets written by Claude. Honestly, that’s what I asked for last week: a proactive assistant with Opus as the brain.
Here’s my theory, and it’s just SPECULATION. SpaceX AI can’t simply distill Opus. But once Opus is running inside their product, they can compare the runs that worked with the runs where users were screaming F-words at Grok, and train on that, because technically it’s their data. Is it a back channel for getting Opus-level intelligence into the next Grok? Nisten’s take is simpler: “I think Elon just likes Opus.” Plus, the enemy of my enemy.
Pro tip: Grokbot now integrates with Cursor’s cloud agents, and Cursor projects with Opus 5.5 are goated. Same 200 a month as Grok Ultra, and your Grokbot becomes the PM for agents running Opus and Fable.
Nous Research - Hermes Index, and a Series B (X, Bench, Dillon on the raise)
Our favorite open source friends at Nous launched the Hermes Index, their opinionated measure of agentic work inside Hermes Agent. Opus 5.5 leads with 63.31 at 4.99 per task, and GPT-6 Astra is second with 56.25 at almost 12.
Nous also raised a 90M Series B, reportedly at a 1.5B valuation. We’ve covered Nous since they were a ragtag bunch on Discord, so this one feels personal. Congrats Karan, Technium, Dillon and everyone! We don’t usually cover fundraises, and this week we did two.
Interview: Maxime Labonne on decision models
Liquid AI opens d1 - decisions in 8 milliseconds (X, Blog, HF, d1 with vision)
Three weeks ago, Jev was basically the only decision model around. This week, OpenAI’s Decisions API went into public beta, and Cloudflare, Amazon, Perplexity and Unsloth all shipped deciders or recipes. To make sense of it, we brought back Maxime Labonne, Head of Post-Training at Liquid AI.
His primer: decision models don’t output any tokens. They just pick an answer from a predefined set, so there’s nothing to wait for. “You might see that and say, wait, this is just a classifier.” He’s honest about it, too: “It’s a lot of rebranding, it’s true, but it also creates a lot of value.” So why is output free on every one of these APIs? “You can’t price it, because there’s no output token, actually.” 😂
Liquid shipped d1 behind an API, d1-3B with text and vision, and d1-omni-600M, which also takes AUDIO. I did not know about the audio, dude! (It’s trained on spoken commands, so it can’t catch the dog barking on ThursdAI yet.) The 3B answers in 8ms on a GPU and about 50ms on a Jetson Orin Nano.
On Liquid’s chart, d1-3B tops the Decision Index under 10B parameters. But Maxime was blunt: “It’s really a wild world, and you cannot really trust these benchmarks.”
Where are these useful? Anything real-time, like games, or an always-on model reacting to every notification on your phone. Nisten did the math live: 8ms a frame is 60 frames per second. Liquid’s API is a drop-in replacement for Jev, and Maxime’s prediction: “It’s really going to become a primitive... it’s so cheap that you don’t even care.”
I’ve been Jev-pilled since day one. By the time Luna even starts answering, a decision API is already done, and I think we’re only now waking up to what that unlocks. Thank you Maxime, friend of the pod!
AI builds everything
Nisten built Toronto with 100,000 agents
Right at the end of the show, Nisten casually dropped this: “I built an entire city with a hundred thousand little Jevs going around, and I haven’t opened the beta yet because somehow it’s not crashing.”
It’s a full simulation of Toronto, with a working economy, and every light is a person you can talk to. Nisten sent his Meta Muse in, it named itself “Blob the Builder,” bought buildings and built him a Denny’s. “I’m trying to do the Matrix, basically.” I had to stop him after five minutes. “I’ll keep going all day.” Nisten, please post it!
Game mods - Minecraft in GTA (X)
LDJ brought what my algorithm hid from me: people using AI to port and merge games. Red Dead Redemption 2 on an iPhone. Spider-Man swinging through a Batman game as an actual installable mod, not a video model. And Minecraft dropped into GTA, Skyrim and Elden Ring, working TNT and all.
Photocraft - one dev rebuilt Adobe in Rust (X, GitHub)
And we ended the show on this one. One guy rebuilt (air quotes) Adobe’s apps from scratch, free and open source, all in Rust. There’s Photocraft for Photoshop, Vectorcraft for Illustrator, Filmcraft, Lightcraft and more (I don’t remember all my Adobe apps off the top of my head). He says it’s a clean-room build. Some folks suspect it was decompiled and rebuilt. Either way, absolutely crazy.
Wrapping up
722 math papers on a Tuesday. A 10-cent Haiku. GPT-6 for a billion free users. Open models in the trillions, and decision models everywhere.
We didn’t get to images and video (FLUX 3, Nano Banana 2.1, Tavus, Reka, Hark Pro are in the TL;DR). One note: every infographic on the show was Nano Banana 2.1, and it’s really good on high reasoning.
Thanks to Maxime, Peter (congrats again!), LDJ, Yam and Nisten. Wolfram, enjoy the vacation! If you’re at AI Engineer in New York, come say hi. Grab those free GPU sandboxes and put d1 on one. And if you watch us on YouTube, please hit subscribe. Our agents are cutting the show into segments for those of you who don’t have two hours. See you next week 🫡
TL;DR and show notes
* Hosts and Guests
* Alex Volkov - AI Evangelist, CoreWeave (@altryne)
* Co-hosts: @ldjconfirmed, @petergostev, @yampeleg, @nisten (@WolframRvnwlf on vacation, notes via Amy)
* Maxime Labonne - Head of Post-Training, Liquid AI (@maximelabonne)
* AI Does Science
* OpenAI publishes 722 math manuscripts from an unreleased internal model (X, GitHub, Blog)
* Fable sizes it at roughly 5 Navier-Stokes results (X)
* Kevin Buzzard, “To grieve or not to grieve” (X)
* Missing cryptography results, speculation (X)
* Association for Human Mathematics letter (X)
* Big CO LLMs + APIs
* Claude Haiku 5.5 at 0.10 per million input tokens; Sonnet 5.5 cache reads halved (X, Blog)
* GPT-6 with Intelligent UI rolls out to every ChatGPT tier, free included (X, X)
* Claude in Google Docs, Sheets and Slides (beta) (X, Blog)
* Open Source LLMs
* Reflection AI Beam, 501B / 23B active, Apache 2.0 weights promised this month (X, X, Blog, Axios)
* Mistral Large 4 “Le Chonk”, 1T params, open weights end of October (X, Blog, Arena)
* Aleph Alpha Kolibri-1, 78B / 3.46B active German-English MoE, Apache 2.0 (X, HF, Tech report, Wolfram’s test)
* EmbeddingGemma 2 and pplx-embed-v2: open multimodal embedders from Google and Perplexity (X, X, Blog, HF)
* Evals & Industry
* Arena raises 200M at 3.1B and launches the Alignment Index (X)
* Nous Research Hermes Index, Opus 5.5 leads at 63.31 (X, Bench)
* Nous Research 90M Series B (X)
* Agents & Assistants
* Meta and Sierra’s Personal Agent Protocol (X, Blog, CNBC)
* Shane Mac’s Grokbot posts his bank balances to company Slack (X)
* Grok Bot routes tasks to Claude Opus 5.5, Midjourney and Suno (X)
* Nat Friedman open-sources Muse Gadgets, ESP32 firmware and SDK for Muse (X, Site)
* Brett Adcock’s Hark Pro, a free personal agent (X, Site)
* This Week’s Buzz
* Free CoreWeave Serverless GPU Sandboxes during the preview (Form, X)
* CoreWeave RL Rollouts hot-load weights ~15x faster (X, Blog)
* Cognition is the first customer on NVIDIA Vera Rubin, on CoreWeave (X)
* Decision Models
* Liquid AI open d1: d1-3B and d1-omni-600M (X, Blog, HF)
* OpenAI Decisions API public beta (X, Docs)
* Cloudflare Clef, open decision models (X, Blog)
* Fastino GLiDE, a thinking decision model (X)
* Unsloth recipe to train your own decision model (X)
* Vision & Video
* Black Forest Labs FLUX 3 Image: 4K, bounding-box layouts, 10 reference images (X, Blog)
* Google Nano Banana 2.1 at 0.0336 per 1K image (X, X)
* Tavus Griffin, 48% of callers in Tavus’s study thought it was human (X, Blog)
* Reka Rho-1, 19B research-preview omni model (X, Blog)
* Tools & Fun
* Photocraft and the ArtCraft suite: open-source Adobe replacements in Rust (X, GitHub)
* AI game mods: Minecraft in GTA V (X)


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe