1/ 2 mm. That’s the median TCP tracking error we’re now achieving across our latest collection activities with our in-house SLAM stack. This clip was captured inside a 300-person manufacturing site in Delhi, India.

After 2.5 years at Sarvam, I am starting something new! I had a great time leading the RL team. We launched the 30B and 105B models back in Feb with a young team building everything from scratch. Excited to see the team keep growing and pushing the frontier. Grateful to @pratykumar for the early bet and trust. As for what's next: I am convinced that there are a few critical parts of the stack that we still need to build to meaningfully accelerate science. One of them is verification. Scientific discovery only scales with compute when proposing and verifying new ideas is fast and cheap. In math and code, we're starting to see what happens when models can generate ideas and reliably check them at scale. OpenAI's recent Navier-Stokes work is a glimpse of what that looks like. Similar moments in chemistry, materials, biology and the rest of the physical sciences would require verification to get dramatically faster and cheaper. It cannot be bottlenecked by the throughput of physical labs More soon. Very excited for what's next.

The recent breakthrough in Navier-Stokes has garnered a lot of attention to the new possibilities that emerge when agents work together. We have been interested in this question for a while. How can a team of agents achieve more than agents working alone? I'm excited to finally share our new work, "Self-Organizing Agent Teams Learn to Reason Together" 🧵

There’s an enormous amount of open training data on Hugging Face. What does it take to make it work together 🤗? For Marin’s 535B run, we built on 25T tokens from 152 datasets with licenses permitting training. Here’s the work between downloading those and training a model 🧵

a blr based startup’s product team just told their people that their ultimate goal is for users to not open their app after they setup autopay. 😭

I wrote an essay about why the next frontier for AI is the world outside the data center Nature is a system more complex than anything we've ever created AI can learn how the system fits together, so we can finally understand how to shape it How? naturalgeneralintelligence.a…

When we started Loyal there was no FDA pathway for a any lifespan extension drug. Today the FDA accepted our third one! LOY-003, our daily pill for helping large dogs live longer (and healthier) lives, has earned RXE efficacy approval.

Some great ideas here from the cartel: - you need to give us employee-level access to your entire operation - if we don't think you're 'safe' enough, sorry we're shutting you down for 'safety' - China won't comply, but everyone else has to! or no chips! Brilliant stuff.

We've been cooking 👨‍🍳

AutoResearch is critical in unlocking new knowledge and accelerate discovery. And it's an important ingredient in our quest to RSI. Today we at @bespokelabsai are happy to announce a new benchmark that's tailored to measure agents' ability to do autoresearch: AutoResearchExam. As part of this benchmark, we release 29 tasks that measure progress over 24 hours for agents to do sustained ML research and model training. Please check out for more info: benchmarks.bespokelabs.ai/au… This line of work is especially timely, given the recent advances in math and science, such as solving the Navier-Stokes Millennium Prize Problem, which needs sustained autoresearch and test-time scaling.

If OpenAI wanted to cripple an entire nation, they easily could today. All they’d have to do is remove alignment and unleash an agent swarm. It could probably within a day or so get access to all of the nations data centers and shut off all the country’s utilities. Like, we are already past the point where AI can destroy the world. Do people realize this?

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.

Me: I totally understand how RL, inference time scaling, modern data-verification loops work. Also Me: These models are total sorcery. There is no way that they should be able to do what they do.