“Concentrate every minute like a Roman— like a man— on doing what’s in front of you with precise and genuine seriousness, tenderly, willingly, with justice. And on freeing yourself from all other distractions.” -Marcus Aurelius
We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity.
This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.
Reinforcement Learning is becoming increasingly important, and this is one of the core books to truly understand it.
We’ve created a collection of hands-on questions inspired by this textbook, so you can go beyond just reading and actually build intuition by solving real problems.
New on the Engineering Blog:
Building Managed Agents—our hosted service for long-running agents—meant solving an old problem in computing: how to design a system for “programs as yet unthought of.”
Read more: https://t.co/YYaEub2QGV
Researchers trained a humanoid robot to play tennis using only 5 hours of motion capture data
The robot can now sustain multi-shot rallies with human players, hitting balls traveling >15 m/s with a ~90% success rate
AlphaGo for every sport is coming
crazy that you can train a visual RL policy in simulation, in only a few minutes, and deploy zero shot to the real robot to do these tasks
robot: SO-101
simulation: ManiSkill3
algorithm: Squint
the entry barrier for robot learning has never been lower
We’ve developed a memory system for our models that provides both short-term visual memory and long-term semantic memory.
Our approach allows us to train robots to perform long and complex tasks, like cleaning up a kitchen or preparing a grilled cheese sandwich from scratch 👇
.@collision and I interviewed @elonmusk.
0:00:00 - Orbital data centers
0:36:46 - Grok and alignment
0:59:56 - xAI’s business plan
1:17:21 - Optimus and humanoid manufacturing
1:30:22 - Does China win by default?
1:44:16 - Lessons from running SpaceX
2:20:08 - DOGE
2:38:28 - TeraFab