ThursdAI - The top AI news from the past week

Hey folks, Alex here, let me catch you up!
I’ve had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA’s first PC chip packing 1 petaflop of local AI power into thin laptops.
A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts.
Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that.
And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B.
I’ve had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show.
Def don’t miss this one! Let’s get into the details.
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Open Source LLMs
🔥 NVIDIA Nemotron 3 Ultra: The 550B Open Source Beast Built for Agents (X, Arxiv, Announcement)
This was the big one. Breaking news mid-show: NVIDIA drops Nemotron 3 Ultra, a 550 billion parameter sparse MoE model with 55 billion active parameters, built on a hybrid Mamba-Transformer architecture. Chris Alexiuk, AKA Joe Nemotron, joined us live from NVIDIA HQ in Santa Clara to walk us through it.
The headline number is 5.9x higher inference throughput compared to GLM-5.1 on decode-heavy workloads. Chris told us that this is a result of multiple things, their Hybrid Mamba-Transformer approach, the sparse attention, and that they optimized for decode-heavy workloads (the kinds of workloads agents do)
The architecture is fascinating. They’re mixing Mamba-2 state space layers with sparse attention, which means step 300 in an agent loop runs as fast as step 3. Pure transformers can’t do that because the attention cost keeps growing with context length. This kicks in big time at 64K+ sequence lengths, which is exactly where you end up in real agentic work when the model is having multi-turn conversations and people are dumping their entire codebase in.
P.S - We launched Nemotron 3 Ultra with 0-day support on CoreWeave Inference, it’s super fast and pretty cheap, give it a try here
They pretrained on 20 trillion tokens, extended context to 1 million tokens, and their post-training pipeline used multi-teacher on-policy distillation from over 10 specialized teacher models covering everything from SWE to terminal use to search to office work, which they are also going to open source soon!
One thing Chris emphasized that I really appreciate: NVIDIA doesn’t have their own harness. There’s no “NVIDIA Code.” Which means they actively resist the temptation to harness-max, to optimize for just one harness and look good on a specific leaderboard. Ultra should be a solid drop-in for whatever harness you’re used to, and that generality is worth a lot. It’s not the best thinker, but it is the highest score US based open weights model, so again, a huge huge win for the US AI ecosystem!
The Nemotron 3 Ultra release is open under the OpenMDW-1.1 license: base BF16, post-trained BF16, and NVFP4 quantized checkpoints, plus the GenRM, synthetic pre-training data for code, legal, and specialized domains, post-training datasets, RL environments via NeMo Gym, and training recipes in the Nemotron GitHub repo, which is absolutely bonkers! Kudos to team green for this awesome and very important release!
NVIDIA Nemotron 3.5 ASR: The Tiny Speed Demon (X, HF, Blog, Blog)
Oh, and NVIDIA wasn’t done. They also dropped Nemotron 3.5 ASR, a 600 million parameter open source multilingual streaming speech-to-text model covering 40 languages. It’s the fastest model Pipecat has ever tested, and the cost math is insane: roughly 5 cents an hour for enterprise deployment when typical API providers charge 10 cents to a dollar per hour. Our friend Kwindla from Daily and Pipecat put together a detailed writeup with benchmarks and cost analysis. Chris couldn’t stop praising NVIDIA’s speech team and honestly, I can’t either. Banger after banger.
Just a week after I told you about Cartesia Ink-2, NVIDIA drops an open version that’s pareto optimal, can run fully on-device and is blazing fast at transcription!?
Other notable open source announcements that would have made full headlines on any other week:
* MiniMax announces M3, a natively multimodal, 1M, coding and agentic frontier model (X)This one is very interesting, but not yet available as Open Weights so we haven’t tested it fully, we’re going to do it next week when the drop the tech report and the weights
* Google drops Gemma 4 12B - encoder-free multimodal model that runs on your laptop with 16GB VRAM under Apache 2 (X, HF)Our friends from DeepMind keep the western open source momentum going with a new 12B size for Gemma (which crossed some 100M downloads on Hugging Face recently).
* JetBrains Mellum2, a 12B MoE model with only 2.5B active, trained from scratch by a team of 7 people (X, Blog, HF, CW Inference)The great folks at JetBrains, the company behind the IntelliJ IDEs, dropped a new model called Mellum2 which they trained from scratch. Very interesting to see them pivot in the world where IDE’s are dying at the hands of LLMs.
* H Company drops Holo 3.1: blazing fast local computer-use agents from 0.8B to 35B, with massive mobile benchmark jumps (X, Blog)
NVIDIA’s RTX Spark and reinventing the PC - announcement at Computex 2026
While we’re on the topic of NVIDIA, they opened the week with a huge announcement, including Microsoft, Dell, Lenovo, and HP and a bunch of other partners in it.
They announced RTX Spark, their first ever PC chip, which is a full system on a chip (SoC) focused on running AI workloads for things like OpenClaw and Hermes!
Announcing this on the stage at Computex, Jensen Huang called it the “the most amazing chip the world has ever built”, being able to run every app that Microsoft has ever run.
This is a huge deal, specifically because of how agentic the world is becoming, these machines (thin laptops and a mac-mini alternative were announced) will be able to run 120 billion parameter models on-device, gaming at the level of RTX 5070, and AI agents 24/7. I’m getting excited and I’m not a windows user!
Hermes victory + Hermes Desktop and an interview with Karan from Nous Research
If you squint, you can see that by the little red OpenClaw, there’s another logo. That’s the Nous Girl logo of Nous Research, which was rebranded to be the logo of their Hermes Agent (an open source agentic harness that’s passed 181K starts on Github, and is the leader in global ranking on OpenRouter)
We’ve had the awesome pleasure of having Karan Malhotra (@karan4d), one of the co-founders of Nous Research on the show, and Karan broke down how Nous Research evolved from a research lab that created the long context innovations (YaRN) and finetuned models (Hermes used to be a series of models) to a full agentic company.
We also chatted with Karan about the new Hermes Desktop experience, which lets folks see the tools that are used, the code that’s being written by their agent, and how it feels to be featured by the worlds largest company on the global stage! Definitely check out the conversation with Karan.
Microsoft BUILD, new PC, becoming a frontier lab with MAI-thinking-1, MAI-code and MAI-image 2.5 (Blog)
From Jensen to Satya, the week was full of AI announcement that will impact the world. Microsoft’s annual Build conference happened just a few days after, with Jensen zooming in from Taipei to co-announce all these new PC models and chips.
Shortly after that, and after a lot of other announcements about less-exciting enterpris-y stuff, Satya handed the stage to Mustafa Suleyman (co-foudner of DeepMind and Inflection AI) and now CEO of Microsoft’s AI division (MAI) to announce all these new models!
A few of these (in previous versions) were already covered on the show, but the new LLMs are the most interesting! MAI-Thinking-1 is 1T total parameters with 35B active params, trained on 33.5T tokens (30T pre-training, 3.55T mid-training), without any distillation (which felt important for them to say given their proprietary access to OpenAI’s models). It’s not yet competitive with Opus and OpenAI’s flagship models, but they are claiming parity with Sonnet 4.5 and get 53% in Swe-bench Pro coding tasks!
Given that recently, OpenAI started offering their models on AWS, we’re now seeing a bit of a distancing between Microsoft and OpenAI, with Microsoft showing that can become a frontier lab on their own right, or well.. maybe a second tier frontier lab.
Of course, we shouldn’t forget that Microsoft kind of started the whole era of coding AI’s with CoPilot and completely lost to the Cursors and Windsurfs and Devins of the world given the huge head start they had with Github, so I’m really curious to see how strongly they will push this “second tier frontier lab” angle and if they have what it takes to compete with Google here (not to mention OpenAI and Anthropic)
And while the model wasn’t available for me to even test yet, MAI did drop an incredibly in depth 109 page technical report on it. Our friend of the pod Elie Bacouch (@eliebacouch) did a breakdown of the most interesting aspects of it, calling it a gold-mine for details about training models at this scale.
Image gen models race to the top of the Arena
This week was honestly chaotic for image gen. Three new SOTA models in basically 48 hours, I tried to use them all while preparing for the show, and here’s the comparison I ran:
Microsoft MAI-Image 2.5 (X, Try it)
One of the more surprising updates were about the MAI-image 2.5, it landed at #3 on text-to-image and #2 on image-to-image, surpassing Nano Banana Pro on the editing leaderboard. It comes in two flavors, MAI-Image-2.5 and a faster Flash variant, both running on H100s which means existing infra can serve it, and it’s already rolling out in OneDrive Photos for background cleanup and distractions removal.
That said, my honest take: I tried to generate a ThursdAI thumbnail with it and got “image failed” because I think the word “explosion” tripped its safety filter. I then tried to generate an “horse riding an astronaut on the moon” and got this, yep... this is .. not the best. IDK how and why they shot up so high on the leaderboards. But I guess we’ll see as more folks try these models.
Ideogram 4.0 - new SOTA open weights image gen 🔥 (X, Blog, HF)
The one I want to celebrate hardest is Ideogram 4.0, because they opened the weights! For the previous three Ideogram versions you could only use them on their website, and now they dropped the next one as a 9.3 billion parameter open weights model (non-commercial license, but still). This is now new #1 open weights text-to-image model, with only closed models from OpenAI and Google ahead of it on DesignArena. At 9.3B params, it beats much larger models like Qwen-Image (20B), FLUX.2 dev (32B), and even the 80B MoE HunyuanImage 3.0 on text rendering benchmarks.
The architecture is wild. Instead of CLIP or T5 they use Qwen3-VL-8B as the text encoder, extract hidden states from 13 intermediate layers, and they trained exclusively on structured JSON captions with bounding boxes. That’s why it’s so good at layout control, you can prompt it with precise bounding box positions and hex color palettes, and you can see the layout shaping the generation as it converges.
In my thumbnail test it nailed almost everything but had a small typo (it generated “Nemotron” once and then a weird “Nemo 1” duplicate in another area). Still, very impressive for a first open weights release.
Reve 2 jumps to #2 above Nano Banana Pro (X, Blog, Try it)
I’ve talked about Reve before, and Reve 2.0 just dropped at #2 on the Text-to-Image Arena with a 1280 score, a +125 Elo jump over their v1.5 in a single release. That’s basically unheard of on the arena leaderboard. The thing that blows my mind is they’re a 65 person lab training at only 2,000 GPU scale, competing with labs that have orders of magnitude more compute.
The core innovation is that they separated planning from rendering. Every image is first laid out as structured code (composition, relationships, style, labeled segments) before it gets rendered at native 4K (true 16 megapixels, not upscaled). Because the image is represented as code, every element is addressable and editable, so you can manipulate specific regions without regenerating the whole thing. This is also agent-native by design, LLMs can reason directly about the image structure.
I demoed their editing interface live on the show and it’s the tightest layout control I’ve seen in any image model. When I moved my head box to the left, it worked. When I moved the logo to the bottom, it worked. When I changed the word “news” to “imploded”, the surrounding text stayed pixel-identical. That precision is genuinely new.
Honest tradeoff though, Peter Gostev flagged this on the show: they’re #2 on text-to-image but only around #9 on image editing. That matched my own experience nailing the thumbnail likeness, the layout work is amazing but the face came out a little googly-eyed and cartoonish, with one finger going somewhere fingers should not go.
For what it’s worth on my own thumbnail bake-off: Nano Banana Pro is still my pick for the absolute best instruction following (it nails my exact ThursdAI logo color every time), GPT Image 2 is still the highest fidelity but always comes out a little overcooked on the skin, Reve 2 is gorgeous on layout but the face needs work, and Ideogram 4 is the most exciting because it’s open. A lot of why I prefer Nano Banana is just that my prompts are very Nano Banana tuned by now.
Breaking news on the show: Agent Arena from LMArena
The breaking news of the day, while we were already on air, was Arena AI launching a brand new Agent Arena leaderboard. Nisten pasted the link in our group chat and three minutes later Peter Gostev himself jumped on the show to walk us through it. Got to love this format.
The motivation is something we’ve been talking about for a year. The original Arena was built for the chatbot era, where you send one prompt and vote A vs B. But we’ve all moved to agents, long multi-step tasks running for many minutes or hours, and that comparison no longer captures what matters. Agent Arena fixes this by giving models a real workspace with web search, file system and terminal tools, then measures millions of live sessions across five signals: task success, steerability, error recovery, user praise, and tool hallucination. The launch snapshot is built from 300,000 tasks, 2 million tool calls, and 40 million lines of agent-written code.
The results match the vibes on my feed perfectly. GPT-5.5 High is #1 by a comfortable margin, Claude Opus 4.7 right behind, and very interestingly ZAI’s GLM 5.1 (MIT licensed, fully open) lands at #3, above Google, Kimi and DeepSeek. The funniest moment of the show was when we’d been calling out Gemma 4 31B for being bad agentically purely based on vibes, and the brand new benchmark showed up 20 minutes later confirming exactly that. The other juicy signal is “bash recovery”, how quickly a model recovers when a command fails. GPT-5.5 leads at ~17%, and Grok 4.3 from xAI sits at -89%, which is so much worse it almost looks like a training bug.
I’m super into this. Give it a spin at arena.ai (@arena on X), they’re rolling new models in as labs send early access, so there’s a good chance you’ll spin up the next Mythos in their agent harness.
This week’s Buzz - WeaveHacks 4 + Nemotron on CW Inference + WolfBench 3D
A few things from our corner this week.
WeaveHacks 4 is this weekend in SF - not too late to join yet!
We’re hosting WeaveHacks 4 in San Francisco this weekend, and we still have a few spots left, so if you’re in town, please come join us at lu.ma/weavehacks. OpenAI is sponsoring us for the first time, Cursor is in too, we’ve got over $150K in credits to give out, food, and a great panel of judges I reached out to personally.
Nemotron 3 Ultra is live on CW Inference at full NVFP4
I said it above but it bears repeating, our inference team got Nemotron 3 Ultra live on day zero on CoreWeave Inference (via Weights & Biases) at full NVFP4 precision. Nisten plugged it straight into his medical anatomy harness (which was originally built for Kimi and Qwen) and it just worked, plug and play, agentically highlighting body parts and calling custom tools, at around 15 cents cached input. Try it at wandb.me/nemotron-ultra.
WolfBench gets a 3D bar update
Wolfram shipped a quietly important feature on WolfBench: 3D bars where the depth of each bar represents how many tokens the model used to get its score. The 2D view shows Gemini 3.5 Flash sitting comfortably at #2 on the agentic scores, almost matching GPT-5.5. But flip on 3D mode and the picture is very different. Gemini Flash burned over 3 billion input tokens to get that score, where GPT-5.5 used a couple hundred to reach the same level. That’s the difference between “cheap fast model” and “actually cheap to run end to end”. Wolfram’s writing up the full analysis on the W&B blog next week. Check out the new 3D view on wolfbench.ai
AI in Society
Look, tons of other stuff happened this week as well, that honestly deserves its own newsletter, we are focused on models and agents, but it’s hard to ignore the bigger picture.
Senator Bernie Sanders, introduced a public bill called The American AI Sovereign Wealth Fund Act would have the government tax AI companies, take 50% of the stock, and put it under public control. Which I personally find ridiculous, but apparently caused Sam Altman to request a meeting with Bernie.
Meanwhile there’s no doubt that AI hate is growing, and that the public sentiment is very negative, as we can see on the issue of Datacenter water usage for example. Despite Satya Nadella’s claim that the latest Microsoft Datacenters are using a closed loop water system, that use less water than 1 restaurant (X), and that datacenters use less than 1% of total water usage in the US, a lot of politicians, and social media users are still pushing the narrative that datacenters are are a water-guzzling monster and need to be stopped.
Anthropic’s “When AI builds builds” report (X)
Anthropic released a report today called “When AI builds itself” with haunting graphic.
They have a bunch of previously unreleased data in there on how AI is shaping the work inside Anthropic and outline 3 potential futures:
1 - AI progress stalls, humans are able to catch up. Unlikely
2 - AI labs continue to see compounding efficiency gains - The most likely scenario, in which the nature of work changes, 100-person companies could do the work of 10,000- or 100,000-person organizations. The role of humans at companies like Anthropic would shift - Most Likely Scenario per Anthropc
3- AI systems themselves become capable of full recursive self-improvement, and begin building their successors - the most unclear scenario of whether these systems will be aligned to human values or not.
This is a fascinating and yes scary read, as Anthropic fully acknowledges that it would be dope if everyone chills for a second and stops building recursive self-improving AI’s that we aren’t sure could be aligned, but that it’s likely not going to happen, because it’ll just let other labs or in face other countries to catch up and change the frontier.
AI Leaders from top labs Urge Congress to Mandate Synthetic DNA Screening
Sam Altman of OpenAI, Dario Amodei of Anthropic, Demis Hassabis of Google DeepMind, and others signed an open letter on June 3, 2026, pushing for required screening of synthetic DNA and RNA orders to block known risky sequences. The letter, backed by Nobel winners, biotech CEOs, and security experts, notes AI’s ability to outpace human experts in biology, heightening biosecurity risks despite voluntary industry efforts since 2009. I think everyone agrees that this is a good idea, especially given the above Anthropic report. Very happy to see this happening.
Pheeeeew what a week.
This was a looong week, I wasn’t sure if we’d be able to cover everything, and it feels like we did a decent job! I know it’s exhausting, and I hope we on ThursdAI help you readers and listeners to stay on top of things without spending too many cycles.
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TL;DR and Show Notes - June 4, 2026
* Show Notes & Guests
* Alex Volkov - AI Evangelist & Weights & Biases CoreWeave (@altryne)
* Co Hosts - @WolframRvnwlf @yampeleg @ldjconfirmed
* Guests: Chris Alexiuk / @llm_wizard from NVIDIA Nemotron
* Karan Malhotra from Nous Research
* Peter Gostev from Arena
* Open Source LLMs
* NVIDIA released Nemotron 3 Ultra, a 550B / 55B-active open-weight MoE built for long-running agents, with weights, data, recipes, GenRM, and training assets released (X, Tech Report, Announcement, HF).
* NVIDIA also shipped Nemotron 3.5 ASR, a 600M open multilingual streaming STT model for voice agents (X, HF, Benchmark, Voice Agent Repo).
* Google dropped Gemma 4 12B, an encoder-free multimodal model that runs locally under Apache 2.0 (X, HF).
* MiniMax announced M3, a natively multimodal, 1M-context coding and agentic model with open weights coming soon (X, API, Code).
* JetBrains released Mellum2, a 12B MoE with 2.5B active params trained from scratch by a small team (X, Blog, HF).
* H Company launched Holo 3.1, local computer-use agents from 0.8B to 35B with new quantized checkpoints (X, Blog).
* Big CO LLMs + APIs
* NVIDIA announced RTX Spark, its new Arm + Blackwell PC platform for local AI agents and 120B-class local inference (coverage).
* Microsoft AI launched seven new MAI models, including MAI-Thinking-1, MAI-Code-1-Flash, MAI-Image-2.5, MAI-Transcribe-1.5, and MAI-Voice-2 (Blog, Tech Report).
* AI Art & Diffusion & 3D
* MAI-Image-2.5 landed near the top of Arena image leaderboards, though hands-on tests were mixed (X, Try it).
* Ideogram 4.0 became the top open-weight text-to-image model with strong typography and layout control (X, Blog, HF).
* Reve 2.0 jumped to #2 on Text-to-Image Arena with native 4K, code-like layout control, and precise editing (X, Blog, Try it).
* xAI released Grok Imagine Video 1.5 Preview for image-to-video with synced audio (xAI).
* Tools & Agentic Engineering
* Arena launched Agent Arena, a new leaderboard for real agent workflows instead of one-shot chatbot prompts (Arena).
* Cognition rebranded Windsurf into Devin Desktop, a multi-agent command center with ACP support (X, Announcement).
* Nous Research launched Hermes Desktop, bringing Hermes Agent into a native desktop app for Mac, Windows, and Linux (X, Site).
* This Week’s Buzz
* WeaveHacks 4 is this weekend in SF with OpenAI, Cursor, DeepMind, and more joining (lu.ma/weavehacks).
* Nemotron 3 Ultra is live on CoreWeave Inference through W&B at full NVFP4 precision (Try it).
* WolfBench added 3D token-depth bars, making model efficiency much easier to see (wolfbench.ai).
* Voice & Audio
* ElevenLabs launched Dubbing v2, an audio-to-audio dubbing model that preserves performance across 90+ languages (X, Dubbing).
* Cartesia launched Ink-2, a fast streaming STT model built for voice agents (X, Ink, AA).
* NVIDIA’s Nemotron 3.5 ASR looks like a major open-source voice-agent infrastructure drop (HF).
* AI in Society
* Bernie Sanders proposed the American AI Sovereign Wealth Fund Act, calling for public equity stakes in major AI companies (coverage).
* Anthropic published When AI Builds Itself, laying out scenarios for AI-driven AI R&D and recursive self-improvement (Anthropic).
* AI leaders urged Congress to mandate synthetic DNA/RNA screening and recordkeeping (WIRED).
* Anthropic confidentially filed for an IPO, adding another frontier-lab public-market storyline to watch (Axios).


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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.
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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



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