maybe: shivam

AI research @spotify, former matrix multiplier @twitter @x cortex, nyu. techno-optimist. probably on a bike 🚲☕.

[Joshua Achiam @jachiam0]

The experience of being human: on the day it becomes abundantly clear that all open problems in mathematical theory and physics will be solved in ~2 years tops, 98% of the people paying attention to the situation will be too fixated on the human drama about who should get credit to notice that the world changed under their feet in a permanent and quite astonishing way. I feel a strange mix of pride, annoyance, affection, and fear for us all.

[OpenAI @OpenAI]

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.

The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.

The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

[maybe: shivam @kaffeinated]

Gates is one of the good ones, but it may be too late. Hoping the benefits of the singularity are uniformly distributed across society does not make for a sane economic policy

[Peter Wildeford🇺🇸🚀 @peterwildeford]

BILL GATES to NYT: "In private, people who understand how good this stuff is, and how much better it’s getting, they’re very worried. But few tech executives are willing to publicly admit that. They’re now saying to each other: ‘Hey, man, don’t say that. It’s bad for us — the next trillion dollars we’re trying to raise.'"

> Gates said he was motivated to speak now because recent improvements in AI had far surpassed his expectations and because the industry had ignored technology milestones — like AI's escaping the control of its creators or making recipes for bioweapons — that it once said would warrant more caution.

GATES: "They’re just full speed ahead and hoping that the good outweighs the bad"

> Mr. Gates said he was in a "state of shock" as to why there was not more urgency on these issues.

[maybe: shivam @kaffeinated]

@natolambert Awesome and timeless work, congrats!

[Demis Hassabis @demishassabis]

I’ve been working towards AGI my whole life, and as we enter this pivotal moment, I’m stepping into a new role as Chair of Google DeepMind & Chief Scientist of Alphabet. This will allow me to focus on long-term strategy, and accelerating scientific breakthroughs, including leaning into my work at Isomorphic to help cure disease.

I’m excited that @koraykv will be stepping up to lead GDM as SVP, alongside @joshwoodward and our exec team. I could not be more excited and confident about our amazing next chapter! 🚀

https://t.co/2WtlIIlTUa

[Sebastien Bubeck @SebastienBubeck]

yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model.

We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann algebras (disproof of Connes' Rigidity Conjecture) to better bounds for high dimensional sphere packing, for circuit complexity, for monochromatic triangles in multicolored graphs, and more.

More thoughts here: https://t.co/8SjXONeh38

[Bartosz Naskręcki @nasqret]

I have some mixed thoughts to discuss today. I've spent this week in Hanoi, Vietnam. It's an amazing, vibrant human hive where life is felt in such an intense way. And this human aspect, also embodied in the series of 12 lectures which I delivered at the VIASM institute in Hanoi about lambda calculus, formalization, with practical examples, AI training, agentic coding, etc., made me realize how important in this whole experience was the fact that those lectures were offered by a person and directed at other humans. We exchanged many ideas with each other, while having wonderful bun cha and pho. Built a whole lore of our mathematical experience and understanding. It was one of the most scientifically rich weeks of my life. Ironically, the AI systems took part in it, but we were not impressed by the host of new proofs or counterexamples provided by the cleverly used AI systems. It was all about the human role in the mathematical storytelling.

The value which I saw this week was not in the techniques and machines, it was in the exchange and learning, ideation, exploration of new perspectives in the theory. We have not written any paper together yet, but I think many seeds were planted. I talked a lot with my Codex, Fable, GPT Pro and whatnot. Then I went to humans to talk with them and get feedback and be part of the community.

I think more and more about the future of mathematics and mathematicians. I am not despairing over the fate of the profession, I am just sad that perhaps we are marching in the direction where results will be delivered without a deeper reflection on the constructs, connections and directions of the whole theories. During the week, people asked me such thoughtful questions about formalization: why should we bother? Is that impressive? In itself, I think it's not. It's a step in a quest. We don't want to stop, we want to continue exploring. Let's race on the crazy results, let's make proof delivery cheap, but let's make sure we people are still thinking about the sense and purpose of this whole game. Because it is a truly creative act of understanding. We ideate directions, go on quests to understand the unexplored and on the way build techniques and results.

Let's go higher and see if machines can help us there too. Can we fly to Andromeda or get close to a black hole? What should we discover to understand the deep role of dynamical systems in our atmosphere and biology, or can we really appreciate the complexity of our own computers and brains? We have a lot of work to do and I think we are just scratching the surface of something profound. So let's disprove all the silly conjectures and kill all the low-hanging fruits, and let's set up a quest where we will go far beyond our current horizon. Maybe let's build machines that can actually care about the quest. Shall we?

[maybe: shivam @kaffeinated]

@gabriel1 this is the way

[maybe: shivam @kaffeinated]

@_arohan_ We’re in the management consultant era of LLMs

[maybe: shivam @kaffeinated]

@ThePremiseOfIt oh wow that's interesting, I'll have to try Opus 5

[maybe: shivam @kaffeinated]

@jack buzz sounds amazing and very timely, thx for open-sourcing! 🙌

[Soumith Chintala @soumithchintala]

Excited for our first general model Inkling -- open weights, 975B, natively multimodal (text, image, audio). Available on Tinker, HuggingFace and partners.

It is yours to personalize and use openly. It is yours.

[Ash Jogalekar @curiouswavefn]

And that's the point of AI: it gives access to high-level reasoning and results to bright, knowledge-hungry people - whether PhD scientists or teenagers or housewives or carpenters. In many ways it's the great equalizer. Even with all its problems, it's hard to think of a more democratizing technology that humans have invented in our history.

[maybe: shivam @kaffeinated]

I’ve been testing Sol for the past few weeks and it’s blown through everything I’ve given it, a very good model indeed.

[Greg Brockman @gdb]

GPT-5.6 Sol preview — it's a good model:

[maybe: shivam @kaffeinated]

@krishnanrohit The tenth complete overhaul in four years

[Jason Weston @jaseweston]

Claim: Autoresearch that moves the frontier will be about better data: we call that *Autodata*.

🧵1/6 -- Paper is out! https://t.co/b8gOALndzy

Key idea: agentic data creation provides a way to *convert increased inference compute into higher quality model training*.

We show our method gives gains on computer science, legal and math problems over classical synthetic dataset creation methods.

We also show how to train (meta-optimize) such a data scientist agent, so that it can create even stronger data.

Overall, we believe this direction has the potential to change how we build AI data!

[Ashwin Gopinath @ashwingop]

Claude Tag is a Trojan horse.  Not because Anthropic is doing anything evil. Because the incentives are obvious.

Day one, this looks like a great feature: tag Claude in Slack, let it follow the thread, remember context, connect to tools, break down tasks, chase work, and act like a teammate.

But that is exactly the problem. The moment your AI vendor becomes a shared coworker, it stops being just a model provider. It starts becoming the place where work is interpreted, remembered, routed, and eventually executed.

That is not model lock-in. That is context lock-in. You are now renting your company back from them.

Models can be swapped. Agents can be copied. But the memory of how your company actually works is much harder, maybe impossible, to move: the Slack scar tissue, the exception paths, the customer promises, the unfinished threads, the weird workflows, the implicit owners, the “we tried that in Q2 and it failed” knowledge.

Once that lives inside one vendor’s agent layer, you are not renting intelligence anymore. You are renting your company’s operating memory.

And the pricing model makes it even more dangerous. A human coworker has a salary. Claude has unbounded tokenized activity. The more work moves through it, the more the vendor captures not just IT spend, but labor spend.

This is the enterprise bargain people will regret: Convenience now, and rapid decent into dependency.

The right architecture is simple: rent the best intelligence from whoever is best this month. OpenAI, Anthropic, Gemini, open source, whatever. But own the context layer.

Your company memory should be inspectable, permissioned, portable, and model-neutral. It should not be buried inside the same vendor that sells you the intelligence and the workflow surface.

Claude Tag is useful. That is why it is dangerous. Rent the intelligence, but own the context. Or, regret later.

[gabriel @gabriel1]

STOP HOLDING BACK WHEN PROMPTING you can literally one shot whatever feature in one prompt just yap for longer. aim to describe every thing you can possibly imagine in ONE prompt

and obviously use voice. i often talk for 15minutes straight

[Andrej Karpathy @karpathy]

This is a new paradigm for interacting with Claude that is significantly more "inline" with all the other human activity org-wide. Once you do all of the under the hood engineering work to make this "just work" (e.g. across tools, integrations, compute environments, memory, security, etc.), Claude basically joins the team in a seamless way - you can talk to it as you would talk to a person and it can help with a very large variety of workloads.

Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humans. It really takes a while to wrap your head around it, but it works and it is awesome.