Congratulations to the @DynaRobotics team. This is probably my favorite "scaling laws for robots" blog post so far. I hope that it *does not* remain my favorite, and that the community continues to raise the bar further. Next phase: robot brain companies start to expose inference endpoints or remote "ask me anything" sessions to test out their model on their robot. My favorite part of this blog post is that it provides enough detail that the result could be reproduced by an external lab (1M hours egocentric data is quite obtainable). They even evaluated on setups that any lab could buy (ABC-style bimanual YAM). Robotics is entering a scale-up era. The scale of investment is very serious, and so warrants serious rigor when companies make claims about models that only they can verify. Otherwise, we risk vaporizing billions of VC dollars underwritten by self-reported evaluations of capabilities.

My summer project is done! A 20 video, free course on post-training to accompany my book is all on YouTube with slides open for modification & re-use. ~12 hours of content covers the core foundations and some research areas I think will grow in importance. It was a fun time to review all the fundamentals again, as it is clear in the next 1-3 people the amount of people wanting to learn post training will likely 100X again from today, as we have already 100X'ed from two years ago. As AI agents get increasingly capable at coding and discussing these fundamentals (see the code exercises accompanying the book that I am refining with the community) I think developing clear intuitions for how models work and why is one of the most important skills going forward in AI. Still, learning the post-training math is the best way to battle test them. I personally just in this course am starting to master how forward/reverse KL relates to post-training topics. Thanks to all my viewers, and I'm happy to answer questions in the book discord or understand how to better teach the various reward models, on-policy distillation, new RL algorithms, etc. Plus, the book is 50% off right now with the code PBLambert on Manning to celebrate the launch. I'll share the relevant links below. Who's going to make this course for pretraining?

One of the weirdest quirks of the SF social scene around AGI/ASI is that because everyone is so young, the whole universe of thinking is still tinged with irreverence, ironic detachment, yearning, insecurity, and a superposition of absolute belief in the importance of The Thing and a kind of disbelief about the importance of anything. People are dead serious and also possessed of too much uncertainty about the situation to own it, completely committed and also perpetually unsure where they stand or what org they should be in. People are experiencing their first real heartbreaks and their first real illusions of triumph and disaster. Everyone is testing themselves and the boundaries of the possible but no one has been fully tested or passed all their tests. No one has yet learned or proven how to be responsible for a thing of this magnitude, but there is also no one better suited because all of the people who have real experience in great events have been in such different circumstances that their intuitions would not just fail to apply but might actively make things worse. The level of neophyte is off the charts. There is a lack of formidability; there are people who seem quasi-formidable but the whole social scene and hierarchy is so tenuous - and so likely to be disrupted by events and geopolitics - that it is hard to be sure who will turn out to be formidable when push comes to shove and greatness is requisite to proceed.

What do you do with 16TB of pure robotic manipulation data? 🤖 You open source it on @huggingface Simple World Lab just dropped HiFi-UMI-2K - an absolutely massive dataset pushing the limits of high-fidelity manipulation. The entire 16TB is already available in the LeRobot format, meaning you can plug this massive scale straight into your pipelines with zero friction. Dataset: Paper:

I really liked this line from Matei's opening remarks at #RSS2026#: "Wait for your current conference buddies to become the big names in the field." Today's peers are tomorrow's leaders.

When I first started at X, this is one of the things I did a deep dive on because I felt it was critical to the integrity of the experienced. I tasked our Threat Disruption team to investigate a number of trends that seemed artificial. The findings: We could not find meaningful examples of foreign interference in US policy discussions, except people gaming rev share in developing countries. This is what motivated the release of the Country of Origin feature and significant changes to the rev share algorithm. The most deranged & divisive replies generally were from residential IPs in the United States—with no signs of using a VPN. Ultimately, X is a reflection of the internet. And that means you will see the full spectrum of human thought. And sometimes the most outrageous takes will catch fire. Having said all of this, there can still be cases of narratives being boosted but the origin of the initial post is almost always domestic and we have hardened our systems in the last 3 months to prevent this.