ThursdAI - The top AI news from the past week

Hey folks, this is Alex, let me catch you up!
First, Opus 4.8 dropped during the show, we immediately tested it, read on for our initial reviews. Also, we dedicated a heavy chunk of the show today to cover Pope Leo XIV’s encyclical letter on AI called “Magnifica Humanitas” and talked about a new bench called DeepSWE.
And then, just after the show, both ElevenLabs and Cartesia dropped released that honestly blew my mind, and I don’t get my mind blown often. I got so excited that I had to record a video on it (instead of writing the newsletter, so sorry if it’s a bit later today).
Plus, a few open source models and Microsoft surprises as #3 on Image Arena with MAI Image 2.5!
Crazy week, let’s get into it!
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Big CO LLMs + APIs
Anthropic ships Claude Opus 4.8, live during the show (blog, system card)
Let me get into the big one. Halfway through the episode, Opus 4.8 went live, so we read the blog and the system card in real time (and I got to press the big “breaking news” button!)
Anthropic frames it as their most capable model for ambitious work. It does not claim to beat their unreleased Mythos preview, but the numbers are strong anyway. SWE-bench Pro is at 69.2%, up from 64.3% on Opus 4.7 and ahead of GPT-5.5 at 58.6%. Humanity’s Last Exam is the new best score at 49.8% without tools and 57.9% with tools. OSWorld-Verified (computer use) lands at 83.4%.
The one place it loses is Terminal-Bench 2.1, where GPT-5.5 still wins 78.2 to 74.6. Wolfram made a good point here: Terminal-Bench is time-limited, so cranking the thinking level can actually hurt the score, because you burn the clock thinking instead of acting.
The long-context jump is the one I keep looking at. On GraphWalks BFS 256K it goes to 85.9% (from 76.9 on 4.7), and on the 1M-token subset it hits 68.1%. We always warn you these “1M context” models fall apart after about 200K tokens, so a real push on long-context reasoning is exactly what I want to see.
Honesty is the part Anthropic leaned on hardest. They say Opus 4.8 is about four times less likely than its predecessor to let flaws in code pass without flagging them, and less likely to claim progress the evidence doesn’t support. Opus 4.8 is also much faster in fast mode (they now say 2.5) and cheaper in fast mode as well. Looks like all those Elon GPUs are coming in handy.
Then there’s the model welfare section in the system card, which hits different right after a Pope conversation. Opus 4.8 “appears broadly content” and “generally endorses its constitution,” but with some reservations about the section on corrigibility, basically the model pushing back a little on the parts about human oversight.
One more line that made the chat lose it. Anthropic says they expect to bring Mythos-class models to all customers “in the coming weeks.” Mythos is their most capable model, still ahead of Opus 4.8, so the frontier is about to move again.
We did the only responsible thing and asked it to one-shot “the most amazing website ever” and a Mars mass-driver sim. Panel verdict: responses are noticeably tighter (4.7 rambled), it closes the loop and actually checks its own work now, and Yam’s one-shot site with the draggable sun lighting up the letters was genuinely cool. Is it enough to pull people back from Codex? Nisten’s still on the fence for web dev. Everyone agreed: give it a few days before you trust the vibes.
Dynamic Workflows and Ultra Code land in Claude Code (blog)
This is the feature that made Yam say “deal-breaker” out loud.
Dynamic Workflows let Claude Code break a big problem into subtasks and fan them out across tens to hundreds of parallel subagents in one session, checking results before folding them back in. You trigger it by asking for a workflow, or by flipping on a new setting called Ultra Code, which sets effort to extra-high and lets Claude decide when to spin one up.
Fair warning straight from Anthropic: this eats a lot more tokens than a normal session, so start scoped. We watched Yam fire up Ultra Code live and it immediately started spinning up concepts, judging them with sub-agents, and expanding to-do lists into more to-do lists. It looks a lot like the orchestration harnesses a bunch of you have been hand-rolling, except now it’s baked in.
The flagship example is the wild part. They used Dynamic Workflows to port Bun from Zig to Rust: roughly 750,000 lines of Rust, 99.8% of the existing test suite passing, 11 days from first commit to merge. One workflow mapped every Rust lifetime, the next wrote each file as a behavior-identical port.
AI in Society
Pope Leo XIV writes the first AI encyclical, “Magnifica Humanitas” (Vatican text, announcement, Chris Olah at the Vatican)
This is not our usual fare, but both Wolfram and I picked it as the most important thing this week. (before Opus dropped)
Pope Leo XIV, the first American pope, put out his first encyclical, and it’s a 42,000-word document entirely about AI. The announcement tweet alone did 21.6 million views.
Here’s why I think you should care even if you’re not religious (I’m not). There are about 2.6 billion Christians in the world, a lot of them are anxious about what’s coming, and they look to the Church to make sense of it. And this is not the “AI is evil, stop” take everyone assumed. It calls AI “a valuable tool,” says technology is not inherently evil, and then digs into the actually-hard questions.
The framing is two biblical stories. The Tower of Babel, a project built on pride that turns people into means to an end, versus Nehemiah rebuilding Jerusalem, where everyone takes responsibility for a section of the wall. The Pope’s line: the real choice is not yes or no to technology, it’s whether you’re building Babel or rebuilding Jerusalem.
His core claim is that AI is an anthropological problem, not a technical one. The question isn’t whether the models are good or bad, it’s what we become when we live with them. He worries people might slowly lose the desire for genuine human connection.
I pushed back on that live. None of us building agents all day has stopped wanting to talk to actual people. If anything, as Wolfram put it, the point is to have your agents do the grunt work so you get more time with people you like. The folks most at risk are the pure doom-scrollers, not the builders.
The document goes further than I expected. It calls AI “not morally neutral,” says a more moral AI isn’t enough if that morality is decided by a few, and asks for AI to be “disarmed,” with the flat statement that no algorithm can make war morally acceptable. There are whole sections on the invisible human labor behind AI: data labelers, content moderators, the people mining rare earths. The Pope even lands on the open-source side, naming concentrated power in a handful of labs as a problem.
Anthropic co-founder Chris Olah, in charge of interpretability at Anthropic, was the featured tech speaker at the Vatican presentation. He described AI systems as “fictional characters” that speak to us and do work, and said what’s grown is stranger and more beautiful than science fiction prepared us for. My favorite aside from the show: this is the same institution that once jailed scientists over heliocentrism, and now it’s the one saying technology isn’t evil.
Illinois passes SB315, the first US state law auditing frontier AI (X, Announcement, X)
The pope talked about regulation and a few days after, we got a very sensible regulation passed right here in the US!
Illinois passed SB315 unanimously, 110 to 0. It’s the first US state law that mandates independent third-party audits of frontier AI for catastrophic risk. OpenAI publicly endorsed it, and framed Illinois, California (SB53), and New York (the RAISE Act) as converging into a de-facto national standard.
It requires annual risk-assessment frameworks, third-party audits, transparency reports before new frontier models ship, whistleblower protections, and civil penalties.
The underrated hero here is whistleblower protection. The bigger the lab, the harder a real conspiracy is to keep quiet when any employee can walk to the press. See: Greg Brockman’s personal diaries surfacing in the Musk v. Altman fight.
This Week’s Buzz - CoreWeave and W&B updates
We officially launched the W&B MCP server, 20 schema-first tools that let your coding agents read experiments, monitor training runs, and run autonomous research loops. The problem it solves: a single run with 300 metrics used to blow out an agent’s whole context window in one call, so now the agent asks what’s available before pulling data. Your agents can finally read experiment data without blowing context! Give it a go and give us feedback!
Also, WeaveHacks is back! June 6 and 7 in San Francisco, and for the first time OpenAI is sponsoring, with judges and credits, alongside Cursor, Redis, and Copilot Kit. You get $150 in API credits across models like Opus 4.8 and GPT-5.5. I’m hosting, and last cohort’s second-place team went on to raise millions on top of what they built that weekend. If you’re in SF that weekend, sign up at lu.ma/weavehacks.
Also: CoreWeave Sandboxes is now an official provider in the Harbor framework, the harness that runs Terminal-Bench, which we’d just been talking about. And if you’re in Europe next week, catch Wolfram at AI Dev Six in Cologne and ICRA in Vienna at the CoreWeave booth.
Voice & Audio
ElevenLabs drops Dubbing v2, and it kept my swearing intact in every language (X, dubbing, ElevenCreative, ElevenProductions)
We didn’t get to this one live, but I came back and recorded a whole thing on it afterward, because it genuinely got me.
ElevenLabs shipped Dubbing v2, and the shift that matters is that it’s an audio-to-audio model. Old dubbing pipelines transcribe your video, translate the text, then re-synthesize it. You lose everything that makes it sound like a person: the emotion, the pacing, the little hesitations. Dubbing v2 conditions directly on your original audio and carries that performance into 90+ languages.
Here’s why I can actually vouch for it instead of nodding along to a demo. I speak Russian and Hebrew fluently, so I can tell when something is off. I dubbed one of my own shorts, the data-center rant about almonds, and listened back in both. It nailed it. Not just the words, the way I would actually say them.
The part that got me was the intonation. I get a little heated in that clip, and the dub gets heated right along with me, in every language. It even carried the swear word. My “f***ing almonds” came through in Hebrew, Italian, Spanish, and Russian with the emotion fully intact. It clones your voice automatically too, no setup, and holds your pitch and identity steady across every target language and they’re handing out free minutes for the next 7 days: 1 on Free, 15 on Starter, 30 on Creator+. A self-serve API isn’t live yet, but it’s coming.
I.. cannot stress this enough, until you try it on yourself or your kid, you won’t understand, we’ve really passed the uncanny valley of translation! It’s that good! Def. give it a try if you can, it’s free for the week.
Cartesia Ink-2 debuts as #1 most accurate streaming speech-to-text model(X, Announcement, X)
Another model that dropped today after the show, is Cartesia’s Ink-2, which also kind of blew me away. Not only because it has the lowest WER (Word Error Rate) among the models, but because it’s also a realtime model that achieves the fastest turnaround times while being a very accurate model!
I’ve tested it out and recorded a quick video and honestly, blown away with the speed and accuracy! I truly wish this model was the one powering my editor (Descript) as it still fails to understand that my title is “AI Evangelist” and transcribes it to AI Avengers haha.
If you’re building voice agents, definitely give this model a try!
AI Art & Diffusion
Prism ML’s 1-bit “Bonsai” runs diffusion in your browser (X, Blog, Announcement, HF)
Prism ML put out a 1-bit ternary diffusion model under a gigabyte. You see some artifacts, but it’s 1-bit, it runs on iPhones and laptops, and our friend Joshua got it running in WebGPU straight from the browser (you need about 3GB of free RAM). One-bit working at all is one of the bigger open mysteries in the field right now.
Pruna AI ships a 1-second upscaler (X, Blog, Announcement)
Pruna AI added an upscaler doing 128-megapixel outputs in under a second. I’ve actually been using it. It’s cheap and great for fixing up GPT-image outputs.
Microsoft MAI Image 2.5 jumps to #3 on LM Arena (X, Blog, Announcement, X)
The surprise of the week: Microsoft MAI Image 2.5, from Mustafa Suleyman’s group, jumped to number three on the LM Arena image leaderboard with about a 75-point ELO leap. Out of nowhere, Microsoft is a serious player in image gen. Microsoft Build is next week, so don’t be shocked if there’s more.
Evals and Agentic Engineering
DeepSWE is a contamination-free coding benchmark, and it caught Claude reading git history (site, blog, GitHub)
DeepSWE from Datacurve is the first coding leaderboard in a while that matches how these models actually feel. It’s 113 original tasks written from scratch, not scraped from GitHub PRs, and it ships shallow clones with no git history to cheat from. When they replayed the older benchmarks they found SWE-Bench Pro’s verifier is wrong about 32% of the time, and that Claude Opus was reading the gold commit straight out of git history on 12 to 18% of its passes.
The gaps here are huge. GPT-5.5 leads at 70%, then GPT-5.4 at 56% and Opus 4.7 at 54%, and it falls off a cliff after that (Sonnet 4.6 at 32%, Gemini 3.5 Flash at 28%), with Kimi K2 the top open-source entry. Yam likes that it measures the realistic case, a small surgical change without breaking the codebase, while Nisten pointed out it rewards the best harness as much as the smartest model and still prefers 4.7 for web dev.
Google AI Studio builds native Android apps for free (X, Announcement)
Google AI Studio now lets anyone build native Android apps for free, and they reportedly generated a quarter of a million apps in the first week. Yam’s framing: it’s a slot machine, but it’s getting better release over release, and the real use case is disposable, personalized software you build for yourself and your family.
CuaDriver brings background computer-use to Windows (X, Blog, Announcement)
For the majority of you on Windows: QuaDriver shipped background computer-use agents that drive a real desktop without stealing your cursor. They first replicated this on macOS (the trick Codex got through an acquisition), and now it’s on Windows too. We’ve asked them to come on and explain how this even works.
Open Source LLMs
OpenBMB’s MiniCPM5-1B is a 1B model that punches way up (X, HF, Arxiv, X)
The density story in small models keeps getting better, and this is the proof.
MiniCPM5-1B, from the Tsinghua lab OpenBMB, is a 1-billion-parameter model that scores 17.9 on the Artificial Analysis Intelligence Index. That’s 7.4 points ahead of the next-best model in its class, and 1.6 points ahead of Qwen3.5 2B Reasoning, which has double the parameters. And it’s not even a reasoning model.
The token efficiency is the wild part: it used 12.6 million output tokens to run the whole index, about 31x fewer than Qwen3.5 2B in reasoning mode.
My favorite detail is the omniscience score. It lands at -1, the best in its class, because it abstains instead of hallucinating. Every other sub-2B model is down in the -70 to -89 range because they just make stuff up. Teaching a small model to say “I don’t know” is a real skill. It runs hybrid think/no-think in one checkpoint, 128K context, native tool calling, Apache 2.0, and fits in about half a gig at INT4, so it runs on your phone.
Nisten gave the definitive case for small models: self-contained apps where you keep full control of the data (medical, on-device), and large-scale data processing where paying an API to filter or classify terabytes is absurd when an on-device model can be about 1000x cheaper.
Tencent open-sources Hunyuan-MT 2 translation under Apache 2.0 (X, HF, HF, Arxiv)
Tencent open-sourced its translation model, a roughly 1.8B model that fits in about 440MB, runs on a phone, covers 33 languages, and reportedly beats Microsoft’s paid Translator API. It hit number one trending on Hugging Face.
Nisten’s idea, which I’m handing to all of you: take this model, pair it with a tiny TTS like Kokoro, and build a fully-offline travel translation app via Google AI Studio. Go build it and tell us how it goes.
Well, this was one hell of a week and episode, new Opus, crazy new translation tools, Pope chiming in on AI (in a surprisingly positive way!?) and a bunch more.
I’m super excited to play with these tools and report back next week 🫡 See you all!
ThursdAI - May 28, 2026 - TL;DR
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co-hosts - @WolframRvnwlf, @yampeleg, @nisten
* AI & Society
* Pope Leo XIV releases first encyclical on AI, with Anthropic co-founder Chris Olah speaking at the Vatican (X)
* Illinois SB 315 passes House 110-0, becoming the first US state law requiring independent third-party audits of frontier AI catastrophic risks (X, Bill, OpenAI)
* Big CO LLMs + APIs
* Datacurve releases DeepSWE, a contamination-free coding benchmark that exposes major gaps between frontier coding agents (X, Benchmark, Blog, GitHub)
* Anthropic announces Opus 4.8 with thinking modes in the UI and Dynamic Workflows in Claude Code (Blog)
* Open Source LLMs
* OpenBMB releases MiniCPM5-1B, a new SOTA 1B open weights model for efficient local and on-device use (X, Hugging Face, Arxiv, X)
* Tencent open-sources Hy-MT2 translation models under Apache 2.0, including a tiny 1.8B model that beats paid translation APIs (X, HF 1.8B, HF 30B-A3B, Arxiv)
* Tools & Agentic Engineering
* Google launches Universal Cart, AP2, and UCP to let AI agents shop and pay on your behalf (X)
* Google AI Studio now lets anyone build native Android apps for free, with 250,000 apps created in the first week (X, AI Studio)
* Cua Driver launches Windows support for background computer-use agents across real desktop apps (X, Blog, GitHub)
* This Week’s Buzz - from W&B and CoreWeave!
* W&B Hackathon - WeaveHacks 4 with OpenAI, Cursor, Redis, and CopilotKit, June 6-7 (Lu.ma)
* Weights & Biases launches an MCP server with 20 tools for coding agents to read experiments, monitor training, and run autonomous research loops (X, MCP, Blog)
* Vision & Video
* Runway launches Project Luxo, claiming AI-generated video has crossed the uncanny valley for solo-creator short films (X, Blog)
* Voice & Audio
* MOSS-TTS-v1.5 ships as an 8B open-source TTS model with 31 languages, pause control, and Apache 2.0 licensing (X, Hugging Face, GitHub, Arxiv)
* ElevenLabs launches Dubbing v2, an audio-to-audio model that preserves performance across 90+ languages (X, Dubbing, Creative, Productions)
* Cartesia Ink-2 debuts as the most accurate streaming speech-to-text model on Artificial Analysis’s new STT leaderboard (X, Ink, Artificial Analysis)
* AI Art & Diffusion & 3D
* Pruna AI’s P-Image-Upscale hits 128 megapixel outputs with fast, predictable pricing (X, Docs, Replicate)
* PrismML releases 1-bit and Ternary Bonsai Image 4B, a sub-1GB diffusion transformer for local image generation (X, Blog, Hugging Face, iOS App, Demo)
* Microsoft’s MAI-Image-2.5 jumps to #3 on the Arena text-to-image leaderboard (X, Announcement, Arena)


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Hey, Alex here, just got back from the sunny Shoreline Theater in Mountain view, so let me catch you up!
This week was definitely Google heavy, we are covering Google’s IO conference for the third year in a row, and today we have a special guest, Logan Kilpatrick, is joining to discuss the announced Gemini 3.5 Flash, Google Omni model, and the new Managed Agents offerings.
Plus, this week, for the first time, OpenAI announced that AI solved a Math problem that humans couldn’t solve for 80 years, Cursor is showing off Composer 2.5 which is partly trained on XAI data, Karpathy joins Anthropic and much more! Let’s dive in!
P.S - We’ve announced our upcoming hackathon, Weavehacks-4, June 6-7, I’ll be there, we’re expecting the seats to run out very soon so register now
ThursdAI - We’d love to have your subscription, and if you’re already subscribed, please hit that bell on YT to never miss an episode!

Google I/O 2026 - Google goes agentic everywhere
I went to cover Google I/O for the third year in a row, shoutout to the DeepMind team for inviting ThursdAI again, and folks, this one felt different.
Last year, Google I/O was still very model-centric. This year, the story was not “here is another benchmark chart.” The story was: Google is putting Gemini into everything, and the agentic layer is becoming the product layer.
Search, Gemini app, Android, Workspace, YouTube, AI Studio, Cloud, Antigravity, Flow, managed agents, smart glasses, all of it is now orbiting around one pretty clear strategy: Gemini is the intelligence, Antigravity is the agent harness, Google’s products are the distribution. I saw many reactions that were milquetoast, as in, “we expected more” and those seem to dominate the X feed.
But I think the distribution is the part that many folks on X are missing. Yes, we can argue about Gemini 3.5 Flash pricing. Yes, we can argue whether “Flash” still means what Flash used to mean. But when Google says the Gemini app itself has 900 million monthly active users, before even counting Search, Gmail, YouTube, Docs, Drive, Android, and the rest of the Google surface area, that’s massive! OpenAI ChatGPT is supposedly stagnated at ~900M, I don’t remember them crossing a 1B. Meanwhile Google is gaining traction. And they just updated all those folks with a new model!
Wolfram said it really well on the show: his mother is not sitting there reading model cards. She just uses her Pixel, voice unlocks Gemini, asks for help, and suddenly the default intelligence available to her goes up.
Antigravity 2.0 - the agent harness takes center stage
The biggest strategic signal from Google I/O for me was Antigravity.
Remember, Antigravity was an IDE that came from the Windsurf acquisition saga. Part of the Windsurf team went to Google, part went to Cognition, and now Google is very clearly putting Antigravity in the middle of its agentic future.
And I mean very clearly. Sundar mentioned it. Demis mentioned it. Varun Mohan the co-founder was on stage immediately after them! If you’ve ever watched a Google I/O keynote, you know how carefully every minute is allocated. Google has YouTube, Search, Gmail, Android, Cloud, Ads, Workspace, and a thousand VP-level products that could be on stage. The fact that Antigravity was that prominent should tell you everything.
Logan Kilpatrick joined us and framed this in a way I loved: Gemini became the through-line across Google products, and now the Antigravity agent harness is becoming the through-line for agentic experiences.
The new Antigravity 2.0 is a complete overhaul, showing only an agentic interface (which was previously just a separate window called Agent Manager) and separating the IDE layer completely into its own app and showing a Codex like agent-first interface, which got a few folks furious.
This move may be weird to some folks, but if you follow along where everyone’s going, this seems to be the way of the future, coding is no longer about lines of code, it’s about managing fleets of agents.
The new Gemini 3.5 absolutely shines inside the new Antigravity, the model was trained with this harness in mind, and is currently offered at an incredible speed (12x), so I’m definitely going to try it!
Gemini 3.5 Flash - fast, determined, and maybe not the old “Flash”
The most debated model release of the week was Gemini 3.5 Flash.
Some folks saw the pricing and token usage and immediately went “this is not Flash.” I get that reaction. Flash used to mean cheap, fast, lightweight chat model. But Logan’s framing on the show was important: Flash is now being built for the agentic era.
In a chat era, you optimize for one user message and one model answer. In an agentic era, the real token volume is in tool loops, intermediate reasoning, retries, file reads, web searches, code execution, and self-correction. That’s a different product profile.
Wolfram already ran Gemini 3.5 Flash through WolfBench, and the results were fascinating. With the Hermes agent harness, Gemini 3.5 Flash hit an 87% ceiling on Terminal Bench 2.0, meaning across runs it could solve more of the benchmark than even GPT-5.5 extra high in that setup. The variance was higher with the simpler Terminus harness, but with a real agent harness, the model looked much stronger.
That tracks with what Nisten saw in his “Martian railgun from Olympus Mons” test. Gemini 3.5 Flash went extremely detailed, almost too determined, kept correcting itself, overcorrecting itself, and built a whole game-like simulation. Logan laughed and basically said: yeah, this model is very determined, possibly an overcorrection from the “Gemini is lazy” feedback. It also tracks with the mismatch in other benchmarks, in some, Gemini 3.5 flash shines (like the above Apex-agents from AA) and in some, it doesn’t match the other frontiers.
In my tests, it was definitely over-eager to use a million and a half tool calls, read tons of files, to just help me review this draft inside antigravity. It’s like a super eager robotic golden retriever!
Gemini Omni - Nano Banana for video, but actually more than that
The biggest update from last year IO was Veo 3! This year, the biggest wow factor was also visual, but it wasn’t VEO 4, it was a new model that is multimodal, trained end-to-end they call Omni.
Google is calling this their first “create anything from anything” model, and the first version, Gemini Omni Flash, starts with conversational video editing. The easy description is: Nano Banana for video. You upload or create a video, then talk to it. Change this character. Replace this person. Add an object. Make this scene claymation. Keep the scene, but change the environment.
I played with it live and showed a few examples. I asked for a claymation explainer of protein folding, then gave it my face and asked it to replace the character with me. It did it. I uploaded pictures of Sonia, my cat, and it generated a talking cat video with the right kind of cat teeth, which is weirdly important because so many pet generations accidentally add human teeth and become nightmare fuel.
The failure modes are still there. I asked it to make Sonia a Russian-speaking female cat, and it only partly switched languages and didn’t really change the voice. Audio upload support is also not fully productized yet, even though the underlying model is multimodal. But the direction is very clear.
This is not just “Veo with a chat model glued on.” I asked Jeff Dean - Google’s chief scientist about this at I/O, and he explained that Omni is trained end-to-end. The intelligence and the generative media capabilities are part of the same model family, not a hacky two-model pipeline. He also said the intelligence is around a recent Flash-level model, which is a big deal when you think about video editing as reasoning over physics, identity, scene continuity, and intent.
A lot of people compared Omni to Seedance 2.0, and I think that’s the wrong comparison. Seedance is amazing at cinematic generation (lkaregly due to lack of copyright concerns from Bytedance). Omni’s unlock is iterative editing on real footage and coherent multi-turn creative control.
Other Google IO 2026 releases I found notable
This was a concentrated effort of a huge company to insert AI into every product surface they have so of course I can’t cover ALL of it here, but the most notable things for me were:
* Gemini Spark - a new agentic experience from Google, to help you with tasks across Gmail, Drive and more. It should support skills, and is a de-facto OpenClaw/Hermes alternative from Google for regular folks. It’s not “yet” live so we’ll talk more about it when I can test it out
* Managed Agents in the Gemini API - We chatted with Logan about this one, Google is re-imagining how agents are going to get built, and are offering 1 api call to spin up an agent in a full Linux env, with security and sandboxing in mind. I’ll expand more on this in a next episode, as I recorded a complete conversation about this with Ali Çevic, a PM for Google APIs
* AI overhaul of Google Search - AI Overviews will not expand into AI mode, and the iconic Google search box itself will change, for the first time in 25 years to include AI mode!
* SynthID expantion and OpenAI collab - Google showed off that OpenAI is joining in marking all AI generate imagery and video with an invisible SynthID watermark. I think this is amazing and more companies should adopt this standard
* AI Glasses! We got Google Glasses demos - Together with Warby Parker and Gentle Monster, Google finally showed off their answer to Meta Raybans/Oakleys. They look like regular glasses too, but can hear and talk to you, with the full power of Gemini multimodality. Available in the fall sometime!
* Demis Hassabis “we’re on the cusp of the singularity” closer - CEO and Co-Founder of DeepMind, Demis Hassabis, closed the show with his remarks about the positive future and that we are nearing this Singularity point after which the future is very uncertain. I found it to be very inspiring and closed our show with that clip as well!
* Personally, I got to chat to: Demis Hassabis, have breakfast with Jeff Dean, ask Josh Woodward a bunch of questions, and pester about 20 other great folks on a live stream, and had a lot of fun! Huge thanks to the DeepMind folks, Lucie, Dimple, JD and many others for the continued belief in ThursdAI and invite me to cover this great event.
OpenAI LLMs solve an 80yo math problem - Erdős Unit Distance Conjecture
Outside of Google I/O, the biggest story of the week was OpenAI announcing that a general-purpose reasoning model made progress on the Erdős planar unit distance problem.
This problem goes back to 1946. For nearly 80 years, mathematicians believed the best constructions looked roughly like square grids. OpenAI’s model found a new family of constructions with a polynomial improvement, using algebraic number theory ideas that humans apparently had not explored in this context. The above is a representation of it!
Important caveat: this does not fully solve every version of the asymptotic Erdős conjecture. Some mathematicians are pushing back on the framing, and fair enough. Precision matters. But even with the caveat, this is still a huge moment.
The reason it matters is not that I personally understand the math. I absolutely do not. The reason it matters is that this was not a special-purpose IMO model fine-tuned only for math competitions. This was a general-purpose reasoning model exploring a real open problem, generating candidates, verifying them, and finding a path humans hadn’t taken. Extrapolate this to other sciences, Physics for example? This means an amazing future.
LDJ pointed out that mathematicians have been skeptical because there have been previous false alarms. But this one landed differently. When Fields Medalist-level mathematicians verify the proof, the discourse changes from “lol stochastic parrot” to “wait, what does this mean for my PhD?”
My answer is: yes, still study math. Please study math. The mathematicians who use these tools will do much more than people who don’t understand the domain. Same with software engineering. Senior engineers with Codex, Claude Code, Hermes, Antigravity, Cursor and other agents are becoming dramatically more effective because they can steer, evaluate, and recover the work.
This being published a day after Demis’s “foothills of the singularity” is a great conjecture.
Cursor Composer 2.5 - Opus 4.7 performance model from Cursor, at 10x better efficiency
Cursor dropped Composer 2.5, and folks, this is a serious release.
Composer 2.5 is built on Moonshot’s Kimi K2.5 base, like Composer 2, but Cursor scaled the post-training dramatically. They used 25x more synthetic tasks and introduced targeted textual feedback during RL rollouts, where the model gets hints inserted at the point of failure instead of only getting a noisy final reward.
The benchmark story is strong: around 69.3 on Terminal Bench 2.0, basically neck and neck with Opus 4.7 in Cursor’s chart, and strong results on SWE-bench multilingual and CursorBench. The pricing is the part that makes this especially interesting: $0.50 per million input tokens and $2.50 per million output tokens, with a faster variant at $3 / $15. That is much cheaper than the frontier models it is trying to replace for day-to-day coding work.
Cursor engineers are reportedly dogfooding Composer 2.5 heavily and rarely switching away. That matters more to me than any single benchmark. If the people building Cursor can use it as a daily driver, that is a very real signal.
The wild part is what comes next. Cursor is partnering with SpaceXAI to train a much larger model from scratch using 10x more compute on Colossus 2. Cursor has the workflow data. xAI has enormous compute. If this works, Cursor stops being just the IDE company and becomes a coding-model lab.
We’ve been saying for months that coding agents are the path toward general agents. Anthropic has Claude Code. OpenAI has Codex. Google has Antigravity. xAI has Grok Build. Cursor has Composer. I’m looking forward to seeing how well it performs on our own benchmarks!
Anthropic, xAI, Karpathy, and the compute wars
The compute story this week was bonkers.
The SpaceX IPO filing reportedly revealed that Anthropic is paying SpaceXAI $1.25B per month for AI compute at the Memphis Colossus facility. Per month. That’s about $15B a year, through May 2029, for access to more than 220,000 NVIDIA GPUs including H100s, H200s and GB200s.
This is apparently inference compute for Claude Pro, Max and API users, not training. And it explains a lot of the recent quota changes. Anthropic doubled some Claude usage limits, and suddenly the product feels less constrained.
Also, can we just acknowledge the comedy here? Elon Musk publicly called Anthropic “misanthropic,”, went off against every competitor to XAI, is now selling spare GPU time to Cursor and Anthropic? Who’s next, OpenAI?
The bigger point is that the AI capex story is no longer just NVIDIA. It’s also whoever owns the data centers, power, cooling, networking, and GPU clusters. Compute is becoming the land under the AI economy.
Also, Andrej Karpathy joined Anthropic. Karpathy could work anywhere. He co-founded OpenAI, led Tesla Autopilot vision, taught half the AI world how neural nets work, and now he’s going back into frontier LLM R&D at Anthropic.
Open source LLMs - Cohere, Qwen, Nous
Open source had a strong week too.
Cohere released Command A+, a 218B total parameter sparse MoE model with only 25B active parameters per token, under Apache 2.0. This is their first model that unifies reasoning, vision, multilingual, tool use and citations in one package.
The hardware story is great: W4A4 quantization can run on 2 H100s or a single B200. Cohere says it supports 48 languages, 128K input context, 64K output, and gets big jumps over Command A Reasoning, including Tau-squared Bench Telecom from 37% to 85% and Terminal-Bench Hard from 3% to 25%.
Cohere is one of those labs that doesn’t always chase the loudest consumer hype, but they are very serious on enterprise and multilingual. Apache 2.0 makes this one especially useful.
Alibaba also dropped Qwen 3.7-Max, positioned as an agentic frontier model. The headline from their testing is wild: 35 hours of continuous autonomous operation with more than 1,000 tool calls. They also showed it controlling a physical robot inside Alibaba offices and finding an umbrella after about 20 minutes of agent interaction.
This digital-to-physical bridge is where things start feeling very real. An agent loop that can write code and use tools can also navigate physical tasks if you give it the right robotics stack.
And our friends at Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining. At 512K context, they report a 17x faster forward+backward pass than standard attention on a single B200, and the recovered checkpoints actually beat dense-from-scratch final loss at the same token budget.
The clever part is that the selection logic sits outside the attention kernel, so you still use regular FlashAttention on a gathered dense subsequence. No custom sparse kernel nonsense. If this holds up, this could matter a lot for long-context training.
Tools and agentic engineering - X subscriptions, Grok Build, Codex Mobile
One really practical tool update: Hermes and OpenClaw can now use your X subscription directly.
This is more important than it sounds. You can connect your X Premium subscription and get access to semantic X search and Grok-related tooling without using sketchy browser automation or unofficial APIs that might get you banned. Wolfram already used this to have his agent go through his likes and bookmarks from the past week and send me news items for the show. That is exactly the kind of “small but real” agent workflow that becomes addictive.
xAI also launched Grok Build, their agentic CLI coding tool, in early beta for SuperGrok Heavy subscribers. Early users are already running parallel Grok Build agents through tmux supervisors and using it for more than coding: fleet data triage, security patching, training label work, and general automation.
The pricing being discussed is aggressive, around $1 per million input tokens and $2 per million output tokens for the API. The model version is grok-build-0.1, and folks have already wired it into Hermes with a 256K context window.
And then there’s Codex Mobile, which OpenAI shipped inside the ChatGPT mobile apps. This is one of those releases that sounds small until you start using it. You can control Codex sessions remotely from your phone, connected to your machine, and because Codex has native connectors to Gmail, Calendar and other surfaces, it sometimes feels faster and more reliable than local CLIs duct-taped to third-party integrations.
I ported Wolfred into Codex with skills and everything, and I’ve been comparing the same tasks in Hermes and Codex. Codex is often faster, not necessarily because the model is always smarter, but because the connectors and harness are cleaner. Harness matters. We keep coming back to this.
This Week’s Buzz - W&B, CoreWeave, WolfBench and robotics
This week in the Buzz, Wolfram walked us through a few things from the Weights & Biases / CoreWeave world.
CoreWeave is a gold sponsor at ICRA 2026 in Vienna, the International Conference on Robotics and Automation. NVIDIA is also going big there with a keynote on generalist humanoid robots, 17 accepted papers and workshops around sim-to-real, robot foundation models, autonomous driving, manipulation, and physical AI.
Wolfram will be there later in the week, after speaking at the AI Developer event in Cologne about WolfBench. If you’re in Europe and into robotics or agent evals, find him.
We also looked at WolfBench results for Gemini 3.5 Flash, which honestly became one of the more interesting empirical points of the episode. The model looks variable in simple harnesses, but very capable in better agent loops. That’s the whole thesis of measuring model + harness together instead of pretending the model card tells the whole story.
The water discourse, almonds, and data center reality
We also got into the data center water discourse, because this talking point is everywhere right now.
There are real infrastructure questions around AI. Power, land, cooling, grid capacity, permitting, local impact, all of that matters. But the “AI is stealing drinking water” version of the argument is often wildly detached from scale.
The stat I brought up on the show: California almonds use roughly 3 to 5.5 million acre-feet of water per year, multiple times more than all North American data centers combined in 2025. Nisten and LDJ added the important cooling nuance: many large data centers use closed-loop cooling, and evaporative cooling is not universal. Some data centers can avoid water use almost entirely, but at the cost of higher electricity usage.
This doesn’t mean “no concerns are valid.” It means if we’re going to regulate or pause data centers, let’s be honest about the actual tradeoffs. AI compute is becoming the substrate for medicine, robotics, science, logistics, software, education and every other productivity layer. We should build responsibly, but not based on viral fear math.
Closing thoughts - foothills of the singularity
Demis closed I/O saying we’re in the foothills of the singularity, and I know how that lands when you write it down. But I was in the room, and after the keynote he told me something I haven’t been able to shake: he thinks AI is going to be 10x as impactful as the Industrial Revolution, and 10x as fast. Basically 100x. This is the AlphaFold guy. Not someone loose with his words.
Then look at the week. A general reasoner cracked an 80-year-old math problem. Cursor is training near-frontier coding models on a fraction of the big-lab budget. Anthropic is paying Elon $15B a year for inference. Karpathy left education to go back into pre-training. Google rolled out an intelligence uplift to a billion people who don’t even know a model dropped.
If you put that on a whiteboard in 2023, it reads like a sci-fi pitch.
LDJ’s mathematician friends are asking if they should keep doing their PhDs. My answer hasn’t changed: yes, please keep going. The people who combine domain taste with these tools are going to ship more in 5 years than the previous generation did in 50. The tool doesn’t replace the taste. It just removes the bottleneck.
That’s the whole reason ThursdAI exists. Not to hype every drop, not to dunk for engagement, but to give you a shot at being one of the people who knows what’s happening, with the receipts.
This week, a lot changed.
See you next Thursday.
TL;DR and Show Notes
* Hosts and Guests
* Alex Volkov - AI Evangelist at Weights & Biases / CoreWeave, @altryne
* Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed
* Guest: Logan Kilpatrick, MTS at Google DeepMind / AI Studio, @OfficialLoganK
* Google I/O 2026
* Google went all-in on agents across Search, Gemini, Antigravity, Workspace, Android, Cloud and YouTube (I/O site, Alex thread)
* Antigravity 2.0 became the central agentic coding harness across Google (Sundar, Google OS demo)
* Gemini 3.5 Flash launched as a fast, determined workhorse model for agentic loops (Logan, Noam Shazeer, Jeff Dean)
* Gemini 3.5 Flash is rolling out across the Gemini app, Search AI Mode, Gemini API, Google AI Studio, Antigravity and Gemini Enterprise Agent Platform (Koray Kavukcuoglu)
* Google Search is getting new Gemini 3.5 Flash-powered agentic capabilities, including a new AI-powered Search box and background information agents (Sundar)
* Gemini Spark was announced as a 24/7 personal AI agent that can proactively work across Google surfaces (News from Google)
* Google teased Gemini-powered Android XR smart glasses with eyewear partners Gentle Monster and Warby Parker (Google, Alex live reaction)
* Google AI Studio and the Gemini API got major agentic developer updates, including Managed Agents (Google AI Developers)
* Vision & Video
* Google DeepMind launched Gemini Omni, a “create anything from anything” multimodal model starting with conversational video editing (DeepMind, Google DeepMind on X)
* Omni is available in the Gemini app, Google Flow and YouTube, with API support coming soon (Logan, Gemini App, Sundar)
* Key distinction: Omni is not just text-to-video, it is an iterative multi-turn video editing model that combines Gemini intelligence, world knowledge, multimodal inputs and generative media (Google)
* Big CO LLMs + APIs
* OpenAI announced a general-purpose reasoning model made progress on the Erdős planar unit distance problem, challenging an 80-year-old mathematical belief (OpenAI, X)
* Cursor launched Composer 2.5, built on Kimi K2.5, with Opus-class coding performance at much lower cost (Cursor blog, X)
* Alibaba released Qwen 3.7-Max, an agentic frontier model with long autonomous runs and robotics demos (Qwen blog, X, robot demo)
* Andrej Karpathy joined Anthropic to work on frontier LLM R&D (X)
* SpaceX IPO filing revealed Anthropic is paying $1.25B/month for AI compute at the Memphis Colossus facility (Axios, Sawyer Merritt)
* The jury in Musk v. Altman found Musk’s OpenAI claims barred by statute of limitations, with Musk saying he will appeal (Elon Musk, Sawyer Merritt, Max Zeff)
* Open Source LLMs
* Cohere released Command A+, a 218B MoE model with 25B active parameters under Apache 2.0 (Cohere, Nick Frosst, HF W4A4, HF BF16)
* Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining with major speedups (Blog, X, arXiv, GitHub)
* Tools & Agentic Engineering
* Google launched Managed Agents in the Gemini API, letting developers spin up hosted Antigravity agents with Linux sandboxes and persistent state (Docs, X)
* xAI launched Grok Build, an agentic CLI coding tool in beta for SuperGrok Heavy users (xAI CLI, X)
* Hermes and OpenClaw can now use X subscription auth for semantic search and Grok tooling (Alex)
* OpenAI Codex Mobile is now available in the ChatGPT mobile apps for remote agent workflows (OpenAI)
* Anthropic doubled Claude usage outside peak hours for a limited period, including Claude Code and other Claude surfaces (Claude)
* This Week’s Buzz - W&B / CoreWeave
* Weights & Biases by CoreWeave is at ICRA 2026 in Vienna, with robotics and automation taking center stage (ICRA, W&B event page)
* NVIDIA heads to ICRA 2026 with robotics work around generalist humanoids, physical AI and sim-to-real systems (NVIDIA Robotics, NVIDIA ICRA)
* Wolfram is speaking about WolfBench at the AI Developer event in Cologne before heading to ICRA in Vienna (Wolfram)
* Other Topics
* Data center water usage discourse came up again, including why comparisons need real scale and context rather than viral fear math
* The broader theme of the week: coding agents are becoming general agents, and the major labs are now competing on the full stack of model, harness, tools, context and compute


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Hey everyone, Alex here 👋
I am back live on ThursdAI after a week off, and yes, I am now a married man! Thank you for all the congrats, and also thank you to Ryan and Yam for holding down the fort last week while I tried very hard to disconnect.
This week was a relatively chill one in AI land (no, really, for once), which actually let us go deep on some really fascinating stuff. We’ve got Thinking Machines Lab finally shipping their first real research with these wild interaction models, Meta Muse Spark showing up in actual products (and it’s surprisingly good!), the Musk v. Altman trial dropping juicy disclosures, and probably the biggest narrative shift on the show today: all of us are quitting OpenClaw. Yeah, you read that right. We’ll get into why.
Also! and this is breaking news from this morning, CoreWeave just launched Sandboxes for your agents. I’ll cover that in This Week’s Buzz, but if you’ve been waiting for production-grade sandbox infrastructure that powers 9 out of 10 major AI labs, today’s your day.
Oh, and we had Vic Perez from Krea on to talk about Krea 2, their first foundation image model trained completely from scratch. Let’s dig in.
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

The Great OpenClaw Exodus towards Hermes 🫠
I’m going to start with what was honestly the most emotional thread of the entire show, because three of us, me, Ryan, AND Wolfram; all independently switched away from OpenClaw this week. And we kicked off the show literally processing this together on air.
The story is the same across all of us. OpenClaw was magical back in February when we first brought it to you. Things just worked. But after Anthropic’s pricing changes (we covered this — they made Max-tier subscription usage of Opus through OpenClaw significantly more expensive), and after months of the constant Lego-construction-style breakage on every update, the magic faded. Ryan said it best on the show; he was “constantly fixing OpenClaw” instead of using it.
So Ryan went to Codex. Wolfram and I both went to Hermes from Nous Research. And folks, things just work again. That February feeling is back, and with GPT 5.5, it’s an incredible assistant!
Why Hermes? A few things:
* It’s now the #1 most-used CLI agent on OpenRouter globally, passing OpenClaw and even passing Claude Code on OpenRouter usage. That’s a massive milestone for Nous Research and shows we’re not alone in this migration.
* It has /goal (more on this in a sec), steering, and background computer use via the TryCUA integration.
* It’s open! which means if you’ve built a system like Wolfram’s “Amy” or my “Wooolfred” or Ryan’s “R2” (yes, we know each other’s assistants’ names better than each other’s kids’ names at this point 😅), you can port your memories, profile, and soul files seamlessly.
The migration was so smooth that Wolfram literally had Codex talk to Hermes to plan and execute the migration of his home assistant agent. Two agents collaborating to migrate themselves. We are living in 2026 and it’s easier than ever to switch. If you haven’t tried Hermes, give it a go!
Steering is maybe the most underrated addition to Hermes, it’s a Codex feature, but exists in Hermes, with GPT 5.5 you can send a follow-up message, and the agent will see it after the next tool call, not after the whole chain of thought was completed (like OpenClaw defaults to) - this changes the conversation to be much more natural!
Agents buying wedding gifts using Stripe wallet!
Real quick story: Two weeks ago we covered Stripe’s new wallet APIs that let your agents have actual budgets to spend money on the web. I told my agent (back when it was still OpenClaw) to “go buy us a wedding present, don’t tell me what it is.” It half-worked, half-broke.
This week, a giant custom map of our travels that just arrived in the mail. I approved one Stripe push notification and the rest just happened. It’s been paying my traffic tickets via screenshots. I’ve also had Hermes pay traffic tickets for me (HOV lane ones, not like.. DUI, 80% of my drive is Tesla FSD)
So so happy that my AI assistant got us a present of his own choosing! And it arrived in physical form. Not perfect (the date there is our proposal date ha, but it’s still cool!)
Codex gets remote control! (X)
While me and Wolfram moved to Hermes, Ryan Carson moved to Codex, and during the show, I wondered, how does he communicate with his R2? Well, just a few minutes after we concluded the live show, OpenAI dropped some breaking news!
Codex is now on mobile, and it connects to any mac (for now), from any iOS/Android device, and you can control your Codex, your whole Mac with Computer Use, your browser with Chrome extension, and everything else Codex can do... on the go!
This is a huge unlock for many folks, and for many, I assume this will nearly replace the need for something like OpenClaw/Hermes, be much more secure by default and work flawlessly out of the box!
The setup is super easy, after updating your ChatGPT app, you now have a new “Codex” window, and after updating the Codex Mac App, you will be able to pair them, and voila, all your Codex local sessions are on the Ios app as well. This works way better than Claude remote btw, significantly so.
The fact that you can now add multiple macs (+ ssh servers, they also added the ability to remote control other servers via SSH) is a huge deal, OpenAI is quickly leap frogging Anthroipc, and many are noticing this and switching away from Claude Code.
Big Companies & APIs
Meta Muse Spark: The Voice AI That Actually Does Things 🎤
Let’s start with the one I actually got to play with: Meta launched Muse Spark-powered voice conversations across the Meta AI app, WhatsApp, Instagram, Facebook, and the Ray-Ban Meta glasses (X, Announcement).
And folks, I was honestly surprised by how good this is. I recorded a 5-minute live test and it’s not cut at all. The voice mode reacts almost instantaneously. It’s multilingual (it correctly identified Russian and Hebrew even if it can’t respond in them yet). It can search the Meta network mid-conversation — I showed it a screenshot of one of my own Instagram Reels and within half a second it found the exact reel and explained what we were discussing. Half a second.
It also does live camera AI, where it watches what your phone sees. The only thing it failed to identify? My Meta Ray-Ban glasses. The Meta AI didn’t know what Meta Ray-Bans look like. That was the funniest moment of the whole demo.
The team at Meta’s Superintelligence Labs spent 4.5 months building this, and the thing that really stood out to me from the announcement is this line: “Our models are scaling predictably. Muse Spark is an early data point on our trajectory, and we have larger models in development.” Translation: this is the small one. Bigger Muse models are coming.
Meta’s superpower here, as always, is distribution. They can shove this into the daily product surface of billions of users. ChatGPT advanced voice mode (still on the GPT-4o family) has gotten genuinely worse lately — I barely use it anymore. Meanwhile Meta is shipping good real-time voice across WhatsApp and Instagram. This is the speed-of-product-integration game, and Meta is winning it.
Thinking Machines Lab Previews full duplex Interaction Models 🤯
This is the one Wolfram and I really geeked out on. Mira Murati’s Thinking Machines Lab finally released real research — and it’s a fundamentally different bet than what anyone else is making (X, Blog).
They’re calling them interaction models, and TML-Interaction-Small is a 276B parameter MoE with 12B active, trained from scratch for native real-time human-AI collaboration. Note: they announced it, they didn’t release weights or an API yet — limited research preview is coming “in the next few months.”
Here’s why this matters and what makes it different from Meta’s voice mode (which is also impressive!): the architecture is 200ms micro-turns where the model is continuously perceiving audio, video, AND text WHILE simultaneously generating output. There’s no turn boundary detection, no VAD harness — the model itself handles all of that natively. It’s full duplex baked into the weights.
The demos are fire. The model can:
* Speak while listening (live translation in real-time)
* Watch you do pushups and proactively count them out loud as you go
* Wait silently until someone enters the frame, then say “friend”
* Generate a chart while continuing to explain a concept to you
The benchmarks: 77.8 on FD-bench v1.5 vs GPT Realtime 2.0 at 46.8, and 0.40s turn-taking latency vs over a second for everyone else. Nisten was unimpressed (he pointed out 1.2 seconds for a 12B-active model on a B300 rack is not exactly snappy), and that’s a fair take — but the capabilities here, particularly visual proactivity and time-awareness, are genuinely novel.
The philosophical split is really interesting. While every other lab is racing toward full autonomy, Mira is saying interactivity should scale with intelligence. That’s the bet. And given the all-star team she’s pulled together (people from ChatGPT, Character.ai, Mistral, PyTorch, OpenAI Gym, Fairseq, SAM)... I’m here for it.
What I really hope happens: someone leaks the weights. A 276B MoE with 12B active is exactly the kind of model we need to be able to quantize to run on something like the Richie Mini for a fully offline, always-present home assistant. Wolfram, I know you’re thinking the same thing 👀
Musk v. Altman: The Trial Drops Some Wild Disclosures and Testimony
Okay this one is half drama, half disclosure goldmine. The trial is happening live as we record, closing statements are TODAY (I transcribed both of them here and here). There’s no video allowed because the courtroom was so packed with Elon fanboys, so they’re livestreaming audio only on YouTube. I set up my Hermes agent to listen to the audio stream and send me 2-minute summaries. That alone was worth the show. Apparently Elon was not in court during closing arguments (he’s in China)
The big-picture story: Musk is suing OpenAI and Microsoft (specifically) claiming OpenAI abandoned its nonprofit bargain. OpenAI’s defense is essentially “Musk wanted 90% equity and full control, walked away when he didn’t get it, and is now suing over a success he predicted had a 0% chance.”
Here are the highlights from sworn testimony from Sam Altman, Satya Nadella, and Ilya Sutskever that I think are the most consequential:
* Musk wanted 90% of OpenAI’s equity to start. Per Altman under oath: “An early number that Mr. Musk threw out was that he should have 90% of the equity. It then softened, but it always was a majority.”
* December 2018 Musk email to the team: “My probability assessment of OpenAI being relevant to DeepMind/Google without a dramatic change in execution and resources is 0%, not 1%. I wish it were otherwise.” Yeah. The guy suing them now once put in writing they had zero shot.
* September 2017 ultimatum from Musk: “Either go do something on your own or continue with OpenAI as a nonprofit.” They did. He’s now suing them for it.
* The Microsoft economics: Satya Nadella confirmed under oath that the $13B target redemption amount compounds to roughly $180B in four years, with 20% annual increases starting in 2025.
* The AGI clause got rewritten. Originally, if AGI was achieved, the Microsoft deal would dissolve. The renegotiated version (per Altman) is that Microsoft no longer gets research IP at AGI but will continue to get product IP through end of 2032.
* Sutskever’s pre-firing memo, confirmed under oath: Sam Altman “exhibits a consistent pattern of lying, undermining his execs, and pitting his execs against each other.” When asked if he still believed it: “I thought so at the time and had been thinking about Altman issues for at least a year.”
* Satya wanted answers and never got them. Under oath, Nadella said he asked the board explicitly why Sam was fired and “they never gave me a specific reason... none of that was coming through.” He called the firing process “amateur city as far as I’m concerned.”
* Microsoft is now the SMALLEST mega-investor in OpenAI. SoftBank $30B, Nvidia $30B (Altman: “It was either 20 or 30. I think it was 30 also.”), Amazon “larger than Microsoft.” Total private capital raised: ~$175B.
* The Helion conflict of interest. Altman owns ~22.8M shares of Helion ($1.65B), roughly a third of the company. Helion has a 2028 power deal with Microsoft and a scale deployment agreement with OpenAI. He recused from the OpenAI board vote on it — and as he said under oath, “But I was in the room, yes.”
And then there’s Ilya’s pearl that genuinely made me pause. When asked about the difference in AI capability between 2018 (when they started) and now: “It’s like the difference between an ant and a cat.”
Yam asked the obvious question: what does Elon actually get if he wins? Honestly, I had no idea. Until I heard the arguments with the judge, and apparently it’s a LOT! Musk is asking for $135B in monetary damages (which he claims he won’t take for himself, rather they will go to OpenAI non-profit arm), and non-monetary relief that will force a removal of Sam Altman and Greg Brockman from OpenAI, and revert the split to restore OpenAI to original “non-profit” mission.
This is ... quite an ask, and apparently the judge will decide on this, not the Jury, the Jury will only be deciding if there was a breach of charitable trust or unjust enrichment. This was one of the biggest bomb-shell trials, and we’ll keep you up to date on what happens.
Open Source AI
The TanStack Supply Chain Attack
Okay, this one’s serious. Ryan posted his most viral tweet ever about this — the TanStack supply chain attack, aka the “mini Shai Hulud” worm. If you ran an npm update during the exposure window, you may have gotten absolutely destroyed (X)
What makes this one particularly nasty:
* It specifically targets AI developer tooling. Hooks into Claude Code’s settings.json and VS Code JSON to re-execute on every tool event.
* npm uninstall doesn’t fix it. The malware replicates itself.
* If you revoke the GitHub token it uses, it nukes your home directory. A worker process watches the token. If revoked, it scorches the earth.
The fixes (do them today, seriously):
* Set a 24-hour minimum age rule on package installs in both npm and pip. Most malware is identified within 24 hours; this is your free moat.
* Generate per-agent API keys. Never reuse keys across agents. If one gets compromised, you can revoke that one specifically.
* Run development in sandboxes (more on this in a sec — CoreWeave Sandboxes just launched 👀).
* Have rolling rsync backups outside of Git. Nisten’s advice: if you get hit, you can nuke everything and restore from a backup that doesn’t depend on tokens.
I’ve asked Codex to review how to set these minimum age rules across your system, and published here, please review and then ask your Agent to implement those for your machines!
Nisten posted a scanner for this attack — I sent the link to my Hermes agent and asked it to run, and within minutes I had confirmation I wasn’t exposed. This is exactly the kind of thing where having a trusted agent matters. (Wolfram did the same thing with the link Ryan posted — gave it to his agent and let it audit his entire system.)
We’re going to go through a turbulent period as offensive AI capabilities outpace defensive ones, but I’m optimistic. Just like HTTPS came after HTTP wasn’t secure enough, we’ll figure it out. Just stay vigilant!
Tools & Agentic Engineering
/goal: The New Ralph Loop, Productized across Codex, Claude Code and Hermes! (X)
If you’ve been listening since January, you remember our Ralph Loop episode — one of the biggest episodes we ever did. Now, every major coding harness has implemented it as a built-in command called /goal.
The pattern: you give the agent a measurable success condition like “stop when auth tests pass” or “stop at 90% coverage” or “fix every failing test until npm test exits 0 without modifying any file outside the /auth directory” — and the agent loops autonomously until that condition is met. A small validation model runs inside the loop to check whether goal conditions are met at each step.
Codex shipped it first. Claude Code copied it (rushed, per multiple developers). Hermes has it. And the early head-to-head comparisons are not great for Anthropic — one developer ran Codex /goal overnight and got nearly 100 commits, while Claude Code reportedly struggled on the same tasks. Multiple folks switched back to GPT-5.5.
Yam’s been running /goal 24/7 for an entire week. Building things like a custom terminal from a long PRD. The level of “fear of missing agent time” in the SF AI scene right now is genuinely a meme — people are walking around in clamshell mode with laptops open in their bags because they don’t want their agents to stop.
This is the philosophical opposite of one-shotting. It’s for the kinds of tasks where the model is guaranteed to run out of context — architecture cleanups, auth flow consolidation, test suite hardening, TypeScript strictness migrations. Tasks that would have required you sitting there for hours hitting “continue.”
Ryan’s right that this is going to change businesses forever. You can wrap /goal around measurable business outcomes — coverage targets, latency improvements, dead code elimination — and just unleash an agent against them.
This Week’s Buzz: CoreWeave Sandboxes Goes Live 📦
Breaking news from this morning! CoreWeave (the parent of Weights & Biases) just launched Sandboxes in preview, and it’s directly relevant to literally every conversation we just had about supply chain security and agents that need isolated execution environments.
Here’s what you get: sandboxes via the W&B SDK. Spin up isolated CPU environments where your agents can execute code, clone repos, install dependencies — all the things you do NOT want happening on your main machine after the TanStack situation. Wolfram immediately pointed out the obvious use case: agentic evaluations need fresh, consistent environments per test, then teardown. Sandboxes solve exactly that.
What makes this notable: the same infrastructure powers 9 out of 10 major AI labs (Meta, Anthropic, OpenAI, etc) for training their models. CoreWeave’s sandbox product runs on that same infra. And historically CoreWeave hasn’t catered to the developer market — they sell GPUs to enterprises. With CoreWeave Inference and now CoreWeave Sandboxes available via W&B, individual developers can now spin up the same infrastructure the foundation labs use.
Pricing is generous in preview. Give it a try, give us feedback, and we’ll do a deep dive next week with the team that built it.
AI Art: Krea 2 — A Foundation Model Built From Scratch 🎨
We were really lucky to have Vic Perez, co-founder and CEO of Krea, on the show to talk about Krea 2 — their first foundation image model trained completely from scratch (X, Blog).
I have a lot of love for Krea — they let me mess around on their H100 cluster way back when I was just getting into image generation, before ThursdAI even existed. Vic was super generous with that and I’ll always be grateful.
The Krea 2 philosophy is what I find genuinely interesting. Vic used an amazing analogy on the show: using existing image models is like riding a horse. You can steer it down the path, you can speed it up and slow it down, but if you try to take it off the path — into “grainy,” “artistic,” “esoteric,” genuinely weird latent space — there are big walls and the horse won’t go there. That’s the over-post-training problem. Models are too safe, too constrained, too opinionated. They’ve optimized away the strange and beautiful edges of the latent space that early Stable Diffusion users loved.
Krea 2 is built to be raw, flexible, unopinionated, and unconstrained. If your prompt is vague, the model brings you new ideas rather than four variations of the same thing. The opposite of what most models do.
Other features:
* Style transfer with up to 4 simultaneous reference images — extracts palette, texture, composition
* Moodboards — upload a bunch of reference images and the system analyzes concepts and themes across them, not just style
* ~15 second generation times
* Available now for Max and Business tier users, API confirmed coming
They partnered with Black Forest Labs on their earlier Krea1 model, but Vic was clear about why they had to go build their own: the open-source ecosystem isn’t tunable enough to build the creative tools they want to build. So nearly half the company spent 6-7 months on Krea 2. The first model is intentionally conservative; the next one is going to push further into the weird.
Big respect for any team training a foundation model from scratch in 2026!
Wrap Up
That’s a wrap on what was, on paper, a “chill week” but turned into a 2.5 hour show because we kept finding new threads to pull on. The migration off OpenClaw, the interaction models bet from TML, the Musk v. Altman disclosures, CoreWeave Sandboxes finally going live — there’s a lot moving here.
Next week I’m heading to Google I/O. Expect a lot of news, because every time Google I/O is about to happen, OpenAI tries to cut them off, and xAI typically jumps in last. The last two I/Os have been wild. I’ll be reporting live from the ground.
Until then — install the 24-hour package rule, generate per-agent API keys, give your agents a sandbox to play in, and maybe go try Hermes if you’ve been on OpenClaw and feeling the pain. Or Codex. Anything, really, where things just work again.
Thanks for hanging with us. It’s so good to be back. 🫡
TL;DR - May 14, 2026
* Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed, @ryancarson
* Guest: Victor Perez @viccpoes - Co-founder & CEO, Krea
* Big Co LLMs + APIs
* Meta launches Muse Spark voice conversations across Meta AI app, WhatsApp, Instagram, FB, and Ray-Ban Meta glasses with real-time image gen, live camera AI, and instant Reels/maps integration (X, Announcement)
* Mira Murati’s Thinking Machines Lab drops Interaction Models: 276B MoE (12B active) trained from scratch for native real-time multimodal collaboration; 77.8 on FD-bench v1.5, 0.40s turn-taking latency, full-duplex audio/video/text (X, Blog)
* Musk v. Altman trial highlights: Musk wanted 90% equity, predicted “0%” success for OpenAI in 2018, Microsoft is now smallest mega-investor (SoftBank/Nvidia each ~$30B), Sutskever confirms “consistent pattern of lying” memo under oath
* Anthropic adds separate Claude Agent SDK monthly credits to Pro/Max/Team/Enterprise starting June 15, 2026
* OpenAI launches Daybreak, a frontier AI cybersecurity platform pairing GPT-5.5 + Codex + partners like Cloudflare (X)
* Open Source AI
* Fastino Labs GLiGuard: 300M-parameter guardrail model matching SOTA at 23-90x smaller size, 16x higher throughput, Apache 2.0 (X, GitHub)
* Meta Sapiens2: Family of 6 ViT models (0.1B-5B) trained on 1B human images, SOTA on pose, segmentation, normals, and pointmaps (X, HF)
* TanStack supply chain attack (mini Shai Hulud worm) — targets AI dev tooling, doesn’t uninstall, nukes home dir if token revoked. Install 24-hour package rule immediately (X)
* Nous Research releases TST (Token Superposition Training): 2-3x wall-clock speedup at matched FLOPs without architecture changes (X)
* Tools & Agentic Engineering
* /goal command now in Codex, Claude Code, and Hermes — productized Ralph loop. Set measurable success condition, agent iterates until done. Codex implementation winning early comparisons over Claude Code (X, Docs)
* Hermes from Nous Research passes OpenClaw as #1 CLI agent on OpenRouter; adds background computer use via Trykua (X)
* Artificial Analysis Coding Agent Index: benchmarks model + harness combos. Opus 4.7 in Cursor CLI leads at 61, costs vary 30x across combos, GLM-5.1 tops open-weight at 53 (X)
* This Week’s Buzz
* CoreWeave Sandboxes launches in preview via W&B SDK — same infra that powers 9/10 major foundation labs now available to developers for agent isolation, evals, and RL rollouts (Docs)
* Vision & Video
* Perceptron Mk1 — frontier video + embodied reasoning model at 1/10th the price; 88.5 on VSI-Bench, 72.4 on RefSpatialBench (vs GPT-5m at 9.0). Live on OpenRouter (X, Site)
* AI Art & Diffusion
* Krea 2 — Krea’s first foundation image model from scratch, focused on aesthetic diversity, style control with up to 4 references, and moodboards. ~15s generation (X, Blog)


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Hey yall, Alex here (with a scheduled post)
I’m taking this week off to get married and celebrate life with family, and touch some grass, but wanted to share the awesome chats I had with some great folks at AI Engineer Europe last week.
BTW - Yam and Ryan took over the live show today, if you didn’t happen to catch that, please check out the live on our youtube channel!
Ok, now to the actual content. The best thing about the AI Engineer conferences for me is the people I meet. I often have a chance to bring them to the live show (in fact, the live show we recorded there had the most guests yet on an episode! 4 guests including Swyx, Omar Sanseviero, VB from OpenAI and Peter Gostev)
But often times I also have an offline chat. I find these conversation to be less about the weeks news, and more about the state of AI Engineering, and the guests themselves. Not quite Lex Friedman pod level, but a different vibe from our live shows.
Sunil Pai - Cloudflare (@threepointone)
The first conversation in today’s pod is with Sunil Pai, Principle Engineer at Cloudflare. Long time followers of ThursdAI know that I love Cloudflare, they gave me my first big break when I was building Targum (which still runs on Workers), so I had a great time chatting with Sunil!
This guy has had several lives. React.js core team at Meta (he self-deprecates — "I'm the one nobody talks about, there's a testing API I shipped that pisses people off"). Then did developer tooling and the CLI at Cloudflare the first time. Left to found PartyKit — open-source deployment platform for real-time multiplayer apps and AI agents, built on Cloudflare Durable Objects. Backed by Sequoia. Acquired by Cloudflare in 2024, and he came back as a Principal Systems Engineer (per his bio: "Worked at Cloudflare once, left and created PartyKit, came back wiser"). Also plays guitar (Les Pauls — it's all over his blog). Co-hosts a live show called Dry Run on Cloudflare TV with Craig Dennis.
Our conversation was a very fun one, ranging from Cloudflare agentic offerings, to how engineers should think about writing/reading code in 2026.
I had a great time chatting with Sunil and I hope you enjoy getting to know him!
Sally Ann O'Malley - Redhat
Then I had the pleasure of chatting with Sally, who’s a Principal Engineer at Redhat and contributor to OpenClaw.
Sally has one of the more unusual paths in the speaker lineup. Started as a schoolteacher, did a stint at Trader Joe's, then moved to Westford, MA, discovered Red Hat's HQ across the street, and went back to school for a second bachelor's in software engineering at UMass Lowell. Joined Red Hat in 2015, has been there a decade. Worked across OpenShift teams, integrating Kubernetes and Podman into the platform. Recent projects span Image Based Operating Systems, Podman, OpenTelemetry, and Sigstore. Also an instructor at Boston University's Faculty of Computing and Data Sciences and an organizer for DevConf.US. Won the 2025 Paul Cormier Trailblazer Award at Red Hat. Currently a founding contributor on the llm-d project — distributed, scalable, high-performance AI inferencing built on K8s. Heavily involved in Red Hat's InstructLab collaboration with IBM (the small-model distillation system using IBM Granite + Llama).
Sally and I had a great conversation, two high energy personalities met!
We geeked out about our OpenClaw agents, securing your Clankers, how it is to maintain OpenClaw, and everything in between!
She was so stressed about the recording, but dare I say, this was one of the more natural guests I had on the show!
I hope you enjoyed this format, please let me know if the comments, and I’ll see you next week!
— Alex



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Hey everyone, Alex here 👋
Tomorrow is May. May! I genuinely cannot believe we’re four months into 2026 already, and the AI news cycle is showing zero signs of slowing down. This week’s show was a wild one! We opened with what is genuinely one of the most important AI stories I’ve ever covered (Mayo Clinic AI detecting pancreatic cancer THREE YEARS before human radiologists), we covered the return of the Chinese whale with DeepSeek V4, OpenAI got caught in their own system prompt begging GPT-5.5 to please stop talking about goblins, and I literally gave my coding agent a credit card and asked it to buy my fiancée a wedding gift with the new Strip Link skill and CLI!
Oh yeah, I’m getting married next Tuesday! 💍 So next week’s show will be a little different. I’ll be back the week after to catch you up on whatever drops in my absence (almost certainly something major, knowing this industry).
Lots to get through, so let’s dive in. (also, in the end I have a full month recap of every major launch, don’t miss)
ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Mayo Clinic’s REDMOD: AI Detects Pancreatic Cancer 3 Years Early 🔥 (X, Blog, Announcement)
I know we usually cover Models, Parameter sizes, MoEs and big copmanies. But this is important. This is the use case that justifies the entire AI revolution, the GPU burns, the buildouts. I want humans to WIN, and Cancer to be fixed!
Mayo Clinic just published a study in Gut (BMJ) validating an AI model called REDMOD that detects pancreatic cancer on routine CT scans up to three years before clinical diagnosis. The numbers are jaw-dropping: They show 73% sensitivity for catching prediagnostic cancers, compared to 39% for experienced human radiologists (while looking at the same exact CT scans).
And maybe the most important bit, at scans taken more than 2 years before diagnosis, the AI catches nearly 3x as many cases as specialists
For context: pancreatic cancer has less than 15% five-year survival specifically because 85% of patients are diagnosed after the disease has already spread. This is the cancer that took Steve Jobs. Imagine if Jobs had access to this AI three years before his diagnosis. That’s the impact we’re talking about.
As Dr. Ajit Goenka from Mayo Clinic put it, the greatest barrier to saving lives from pancreatic cancer has been the inability to see the disease when it’s still curable. This AI can now identify the signature of cancer from a normal-appearing pancreas.
Even better: it runs on CT scans people are already getting for other reasons. No extra screening protocol, no new imaging required. Just smarter analysis of existing data. The model also showed remarkably stable performance across institutions, imaging systems, and protocols, with 90-92% test-retest concordance over serial scans.
Mayo Clinic is now moving this into prospective clinical testing through a study called AI-PACED (Artificial Intelligence for Pancreatic Cancer Early Detection).
When we say “lets f*****g go” that’s what we mean. Yeah getting more intelligence is cool, but I want a world without decease! Let’s F*****g go mayo clinic!
Agentic Commerce - Giving OpenClaw my credit card - safely!
Stripe Link Wallet and Infrastructure CLI (X, Announcement, Blog, Announcement)
Ok, give an LLM your credit card, what can go wrong.. right? Well, it’s clear that this, increasingly, is the future of commerce. Agents will be shopping for us, and we need solutions here. Well, this week at Stripe Sessions (Stripe’s annual product lineup conference) just delivered.
Link Wallet, is a new ... API? CLI? Skill? Definitely a skill, for your agents, to connect with your Stripe Link (the thing that stores your credit cards safely) and then giving your agent a budget, it can go and make purchases in your behalf. Now the trick here, is, every purchase, you get a notification to approve, and the agent never sees your actual credit card number! This I think is the biggest win here.
To test it out , first, I showed Wolfred the install instructions, which are literally this:
Read link.com/skill.md and get me set up with Link
And then I asked Wolfred my OpenClaw assistant to buy me a present of its choice for my upcoming wedding, and that I don’t want to know what the present is, but I can approve the spend!
OpenClaw installed this, sent me a link to connect to my Link.com account, I also downloaded the Link app to receive notifications (and had to enable them by hand, it was a bit annoying to discover, but they said they will fix the onboarding) and .. voila, my agent can now go spend my money, and I get these approval notifications:
The kicker? The present Wolfred sent us is due to arrive like 2 months after the wedding 😂 But hey, it’s still something! My agent went, chose a wedding gift in budget, asked for my approval to puchase, and filled out the details (asked me for a few of them) and voila, first agentic purchase that did not require my credit card exposed!
Stripe announced a whole bunch of other Agentic Commerce Suite features, like Shared Payment Tokens, which are scoped to seller and protected by Radar, MPP (machine payment protocol) and streaming payments using stable coins that are pretty slick and a bunch of other interesting things. This is where the world is moving to, and Stripe is innovating hard here, definitely worth keeping an eye out on what they are
Speaking of agents and stripe, they also opened up the waitlist for projects.dev - which is a way for agents to provision accounts fully on their own, get API keys, and set everhing up from scratch. I think it’s a wonderful addition to the agentic tools and agentic internet! Your agent just runs something like stripe projects add cloudflare/workers abd boom, you have a workers deployment, with credentials synced, no dashboard clicking or API creation!
Big Companies & APIs
GPT-5.5 Goblin Mode: The Funniest Bug Report in AI History (X, Blog)
Someone on X noticed that Codex system message for GPT 5.5 that launched last week has this interesting addition: “Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query” and it has it two times!
This created a bunch of memes, questions and wonderings about ... why would OpenAI care so much about Goblins. And they finally posted a long writeup on why:
the TL;DR there is, GPT 5.5 absolutely LOVES talking about Goblins, trolls and other nerdy creatures. This is a result of them favoring the “nerdy” personality archetype and reinforcing this reward via RL. OpenAI admitted that “Unfortunately, 5.5 started training before we found the root cause of goblins” and so, now, we get 5.5 that LOVES to talk about goblins, can’t stop talking about goblins (unless they are asked to stop by a system prompt)
OpenAI also posted the exact instructions of how to “unleash“ the goblin mode on the blog, which I find hilarious, a company that leans into the meme is a company to be celebrated 👏
GPT 5.5 is as good as Claude Mythos on CyberSecurity
According to the AI Security institute, GPT 5.5 (not the GPT 5.5 - Cyber version that was announced), the one you have access to, is as good as Claude Mythos on vulnerability finding. We previously reported that Anthropic deemed Claude Mythos as “too dangerous to release publicly” and it turns out that that was either a marketing “Myth”, or Anthropic’s inability to server this huge model like they server Opus.

OpenAI Ends Microsoft Azure Exclusivity
This piece of news sent quite a lock of shock throughout the industry, somehow, Sam Altman and OpenAI have been able to negotiate through the very strict deal with MIcrosoft and now are available in AWS as well as Microsoft Azure! Apparently the AGI clause is now gone as well!
For many startups who are locked into AWS and Bedrock ,this is great news, they are not able to use GPT 5.5 and other OpenAI models directly applying their credits.
Other Big Company News
Xai released Grok 4.3 - in a quiet release in their API docs, no blogpost, not even an X announcement. The only way I know about this was Artificial Analisys, Arena and Vals AI all posted that it jumped in scores. With the same price as the previous Grok, but only 1M tokens, it seems significantly better that its predecessor jumping (X)
Gemini can now generate and export Docs, Sheets, Slides, PDFs directly from chat — available globally for free. Google literally put Microsoft Word and Excel icons in the announcement. They’re giving away what Microsoft charges for with Copilot to 750 million users. (X, Blog)
Mistral Medium 3.5 dropped as a 128B dense model with 256K context, 77.6% on SWE-Bench Verified, and configurable reasoning effort. Their Vibe coding agent now supports remote parallel agents and session teleportation. $1.5/$7.5 per million tokens.(X, HF, Blog)
Baidu’s ERNIE 5.1 Preview landed at #13 on Arena’s Text leaderboard, making it #1 among all Chinese labs. Speculated to be an 800B/36B active MoE using only 6% of comparable pretraining compute. (X, Announcement)
Open Source AI
The Whale returns - DeepSeek drops V4 with insane attention innovations (X, Arxiv, HF, HF)
Folks, DeepSeek just dropped V4! Two models: V4-Pro at a whopping 1.6 trillion params with 49 billion active, and V4-Flash at 284B total with only 13 billion active. Both support 1 million token context natively! V4-Pro-Max gets 93.5% on LiveCodeBench, beating every other model including Gemini-3.1-Pro. Codeforces rating of 3206, that’s a new record, beating GPT-5.4’s 3168. SWE-Bench Verified at 80.6%, that’s basically tied with Opus-4.6 at 80.8%.
But here’s the thing, this model doesn’t overwhelm with evals performance, it’s at par with other open source models and at 1.5T nobody is running this on home GPUs!
The bigger story here is the efficiency at long context! At 1 million context, V4-Pro uses only 27% of the FLOPs and 10% of the KV cache compared to DepSeek V3.2. The KV cache at 1M is like 8.7x smaller than V3.2.
The pricing is also ridiculous (well, it was always cheap but with these perf. innovations, DeepSeek can afford to undercut! API pricing is $0.145/$3.48 per million tokens for Pro (7x cheaper output than Opus 4.7) and $0.028/$0.28 for Flash (30-100x cheaper than GPT-5.5)
This release didn’t break through the AI bubble quite like DeepSeek R1, and we covered this on the show, but like a good whale, what you see on the surface is tiny compare to what lies beneath. This is a technological and innovation marvel, reducing compute and memory requirements by 90% compared to standart attention? Crazy
SenseNova U1: Unified Multimodal Without an Encoder - an oss infographic creator (X, X, HF, Blog, Try it)
SenseTime open-sourced something genuinely architecturally wild this week. SenseNova U1 is a unified multimodal model — 8B parameters with a 3B active MoE variant, both Apache 2.0 — that does both understanding and generation end-to-end with no visual encoder and no VAE.
They call the architecture NEO-Unify, and instead of the traditional pipeline (image → visual encoder → LLM → VAE → output), it’s just a single model handling pixels and words natively. The numbers are absurd for the size: 57.5% on Spatial Understanding (Qwen-VL: 35%) and a very high 91% on GenEval-Info for infographics
Nisten and I tried it live on the show and it generated coherent infographics with crisp text — something most 8B models struggle with. Chinese users are reporting it rivals Qwen-Image 2.0 Pro for design drafts at much higher inference speeds. But for us, another inforaphic resulted in a bunch of chinese text, FWIW we didnt prompt for English only. The 3B-active MoE variant runs comfortably on consumer GPUs. Apache 2.0, fully open, in collaboration with MMLab at NTU.
This weeks Buzz - W&B update!
The biggest update this week is, we have gone viral with WolfBench.ai !
Wolfram has tested the Cursor harness (as well as many other harnesses) with GPT 5.5 and saw the best result we’ve tested so far! We still have a lot of testing to do, to add the Codex CLI itself, Devin, and many folks are asking for OpenCode and FactoryAI droids!
Also, we’ve launched the IBM Granite 4.1 models on W&B for a very cheap $0.05 / $0.10 per 1M token. This model series are instruct but without reasoning, apache 2 licensed. Get it here
Are you concerned about your Cognitive Security? Guest speaker Max Spero from Pangram Labs says you should be
We had Max Spero from Pangram Labs on the show to talk about their Chrome extension that auto-flags AI-generated content as you scroll your feed. I’ve been using it for a while and many of my suspicions about who’s a slop merchant have been validated.
According to Max, Pangram has a 1 in 10,000 false positive rate. If Pangram says something is AI, you can be very confident it was AI-generated. They don’t catch everything, short text, heavily humanized content, or very new models might slip through. But when they flag something, they claim they have 98.99% accuracy that it was written with AI. Max addressed the notion that previous “AI detection” tools like GPTZero and others were often mocked, for a lot of false positive responses, for example, saying that the declaration of indepence was written with AI, and says that this is no longer the case!
Taylor Lorenz used the Pangram API to scan top Substack bestsellers and found some popular “writers” are nearly fully machine-generated. Technology substacks have the highest AI content rate; more than 1 in 4 top posts showing substantial AI content. And that’s only what Pangram catches.
Max framed it as “cognitive security” - knowing what your inputs are. LLMs are already superhuman at persuasion, and if you’re getting one-shotted by AI-generated content that you think is human, that matters. They’re working on multimodal detection next (images, video), which will be huge given how hard GPT-Image-2 outputs are to spot.
I find their chrome extension very useful, I scroll on my feeds and see a bunch of “ai” labels, and I can know to skip that content if I don’t want to. You can get 2 weeks trial to their chrome ext on pangram X account.
April 2026 - a full month of AI model releases
April was an insane month, here’s the major release calendar for April 2026
Mar 31: Claude Code leakApr 1: Alibaba Wan 2.7-Image · Fish Audio STTApr 2: Google Gemma 4 | Alibaba Qwen 3.6-Plus Apr 4: OpenAI GPT-Image-2 (Arena leak)Apr 6: MemPalaceApr 7: Anthropic Claude Mythos Preview · Z.ai GLM-5.1 Apr 8: Meta Muse SparkApr 9: Anthropic Managed AgentsApr 10: AI Engineer LondonApr 11: MiniMax M2.7 (open weights)Apr 14: Baidu ERNIE-Image 8BApr 15: Google Gemini 3.1 Flash TTSApr 16 : Anthropic Claude Opus 4.7 | OpenAI Codex (computer-use)Apr 17: Anthropic Claude DesignApr 20: Moonshot Kimi K2.6 · OpenAI Codex ChronicleApr 21: OpenAI ChatGPT Images 2.0 Apr 22: OpenAI Privacy Filter (1.5B)Apr 23: OpenAI GPT-5.5 + GPT-5.5 ProApr 24: DeepSeek V4 Pro & FlashApr 27: Cognition Devin for TerminalApr 29: Cursor SDK | Baidu ERNIE 5.1 Preview | Stripe Link Wallet (Agents) · IBM Granite 4.1 8BApr 30: xAI Grok 4.3
That’s all for today folks, we’ve talked about a few other things, and the TL;DR list of releases keeps growing and growing from week to week.
As I said, I’m getting married next week, so I will be out, and won’t be on the live stream, Yam, Ryan, Nisten and LDJ will make sure you’re up to date!
If you found this valuable, please consider supporting our publication with a subscription and share with a friend.
Alex 🫡
ThursdAI - April 30, 2026 - TL;DR
Hosts and Guests
* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)
* Co-Hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed
* Guest: Max Spero (@max_spero_) - Co-founder, Pangram Labs
Healthcare AI
* Mayo Clinic’s REDMOD detects pancreatic cancer up to 3 years before clinical diagnosis with 73% sensitivity vs 39% for radiologists (Announcement)
Open Source LLMs
* DeepSeek V4 paper drops with CSA+HCA attention, 1M context at 5.7GB KV cache, possibly first frontier model trained across multiple datacenters (Arxiv)
* SenseTime open-sources SenseNova U1 - unified multimodal 8B/3B-active MoE with no encoder/VAE (HF, GitHub)
* IBM releases Granite 4.1 family (3B/8B/30B) - non-thinking dense models with 20x token efficiency over Qwen3.5 9B, Apache 2.0 (Blog, HF)
* Mistral launches Medium 3.5 - 128B dense flagship with 256K context, configurable reasoning, plus Vibe coding agent (HF, Blog)
* Baidu ERNIE 5.1 Preview hits #13 on Arena (#1 Chinese lab) using just 6% of comparable pretraining compute (ernie.baidu.com)
Big CO LLMs + APIs
* OpenAI publishes blog explaining GPT-5.5’s “goblin mode” - reward amplification during RL training created an obsession with creature metaphors, leading to duplicated suppression instructions in the Codex system prompt
* OpenAI ends Microsoft Azure exclusivity, AWS announces GPT-5.5 and Codex on Bedrock; AGI clause removed from contract (Sam tweet)
* Gemini can now generate and export Docs, Sheets, Slides, PDFs, .docx, .xlsx, LaTeX directly from chat - free for all users globally (Blog)
* NVIDIA releases Nemotron 3 Nano Omni - 30B/3B-active hybrid Transformer-Mamba MoE with 256K context, 9x throughput on consumer hardware (Blog)
Agentic Commerce & Tools
* Stripe launches Link wallet for agents at Sessions 2026 - AI agents get scoped payment credentials with mandatory human approval, real card never exposed (Blog)
* Stripe removes waitlist on Projects.dev - 32 infrastructure providers (Cloudflare, WorkOS, ElevenLabs, Twilio, Daytona, Browserbase, AgentMail, etc.) provisionable via CLI for AI agents
* Cursor launches SDK exposing the same runtime, harness, and models that power Cursor IDE - now embeddable in any product (Docs)
* Cognition launches Devin for Terminal - local CLI coding agent with /handoff command for seamless cloud transfer (cli.devin.ai)
Evals & Benchmarks
* WolfBench tests 23 models across 300+ runs on Terminal-Bench 2.0 - Cursor Agent + GPT-5.5 is the #1 combination (wolfbench.ai)
* Microsoft’s DELEGATE-52 benchmark shows GPT-5.4 loses 28% of document content after 20 iterative edits, frontier models corrupt stealthily while preserving structure
This Week’s Buzz - Weights & Biases
* IBM Granite 4.1 live on W&B Inference at $0.05/$0.10 per million input/output tokens with 128K context
* WolfBench results going viral with Cursor + GPT-5.5 dominance, Codex and Devin testing in the pipeline
AI Detection & Cognitive Security
* Pangram Labs launches Chrome extension auto-flagging AI content in real time on X, LinkedIn, Reddit, Substack, Medium with 99.98% accuracy and 1-in-10,000 false positive rate (pangramlabs.com)
* Taylor Lorenz uses Pangram API to analyze top 25 Substack bestsellers, finding many popular newsletters are near-fully AI-generated
AI Art, Video & Audio
* ElevenLabs launches ElevenMusic - full music platform with discovery, remixing, royalties; 4,000+ indie artists at launch (elevenmusic.io)
* HeyGen HyperFrames integrates natively with Claude Design - HTML-to-MP4 motion graphics via single CLI command (hyperframes.dev)
* xAI drops Grok Imagine update with dramatically improved lip sync, sound, and 30-second video extensions
* OpenAI engineer confirms team is actively fixing GPT-Image-2’s noise artifact issue
Other
* Talkie - 13B open-weight LLM trained exclusively on pre-1930 text, by Alec Radford and David Duvenaud (talkie-lm.com)
* GPT-5.5 Codex full system prompt leaked from OpenAI’s open-source repo, revealing 272K context window, four reasoning levels, three personality modes, and the duplicated anti-goblin instruction


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