Chris

Truth. I think I own a 15+ year Herman Miller myself.

We be vibing!

Yes! And we can leverage the power of Agents to us accelerate our impact.

@CryptoStatuette @0xChrisM @HapticLabsAI its like wikipedia for robotics

Cautionary Tale

At #FestivusAI, we’re building a catalog for robotics. Our first milestone was simple but important: gather scattered robotics data from #HuggingFace, #GitHub, vendor sites, tinkerer blogs, and all the odd little corners of the internet where useful knowledge tends to live. We credit sources clearly. No mystery meat data. Then we brought everything into one searchable platform, so people can actually find what they need without opening 47 tabs and questioning their life choices. Now we’ve added an AI agent that makes pages easier to edit, improve, and keep current. Think Wikipedia for #robotics, but faster to contribute to, easier to maintain, and built for the people actually doing the work. And yes, our code and data are #opensource too. Why? Because that’s how innovation leapfrogs. People build faster when the map is shared.

This is it. Everything learned spending millions on longevity. From: Your Immortal Unc and Auntie. To: Our Immortal nieces and nephews. 0. Sleep is the world's most powerful drug. 1. Be in your bed for 8 hours 2. Same bedtime every night, any time before midnight 3. Don’t eat right before bed 4. Calm foods for dinner 5. No screens 1 hour before bed 6. Avoid added sugar (be aware it’s in everything) 7. Avoid all things in an American convenience store 8. Avoid fried foods 9. Shoes off at the door 10. Eat whole foods, particularly veggies fruits nuts legumes berries 11. Walk a little after meals or air squats 12. Get your heart rate high routinely 13. Lift heavy things 14. Stretch daily 15. Water pik, floss, brush, tongue scrape, morning and night 16. Make an effort to drink water 17. Get sunlight when you wake up (UV is low) 18. Protect skin in midday sun 19. Stand up straight 20. See at least one friend once a week 21. Avoid plastic where you can (in all things) 22. Circulate air in rooms 23. When stressed, breathe, learn to calm your body 24. Go to the dentist 25. Avoid sitting for long times 26. Protect your hearing, the world is too loud 27. Alcohol is bad for you 28. Finish coffee before noon 29. Avoid bright lights after sunset 30. If obese, look into a GLP 31. Sleep in a cold room 32. Texting while driving is dangerous 33. Turn off all notifications 34. Limit social media use 35. Don’t smoke anything 36. If you struggle to sleep, read a physical book before bed 37. 1 hour before bed have a calm wind down routine: bath, read, light walk, listen to music 38. The body is a clock and loves routine. Have a daily morning and evening schedule. 39. Avoid long distance travel where you can 40. Baby steps first: incorporate new things slowly 41. Do less… most things don’t work. Bonus points if you get your blood checked. Start here, it will change your life.

Data about Physical AI lives across HuggingFace, GitHub, and lab pages that sometimes go offline. @HapticLabsAI launched Open Source #Festivus to fix this. Open catalog of policies, datasets, environments, benchmarks, deploy notes. t2m.io/festivus-x

Naval really nailed it with this one: Money < life < attention

Elon Musk literally broke down his 5-step process for applying first-principles thinking to build anything:

Open secret: if you're VC-backed or well-connected, cloud providers often give you compute credits. But many robotics teams we talk to aren't in that club (yet!). If your model is too big for your chip, too slow, or too expensive to ship, we'll fix it, and cover up to $5k in GPU credits.

@natashamalpani Would love your take on this: hapticlabs.ai/blog/2026/05/0…

there is no hugging face for robotics data. no standardized pipeline for collecting, labeling, versioning, training on real-world robot data at scale. no tooling that handles contact dynamics and material deformation well enough for industrial manipulation. no teleoperation infrastructure where human supervisor intervention automatically becomes training data. no vertical-specific manipulation datasets for any specific industrial task. the actual bottleneck in physical AI is the data and the infrastructure to generate it. and this is a structural problem. for language AI, training data was the internet. abundant, cheap, already labeled by human intent. for robotics, the gap between where foundation models are and where they need to be cannot be closed by deploying more robots. three bets are being made right now: simulation-first works brilliantly for locomotion. domain randomization has essentially solved quadruped walking in unstructured terrain. but it breaks down completely for manipulation. simulated cameras have no noise, blur, or friction error. real cameras and grippers have all of it. cable insertion, fabric folding, dexterous assembly are exactly where simulation fails. teleoperation as data collection is the second move. deploy semi-autonomous robots, capture human-guided trajectories, iterate. theoretically sound. but the capital math is brutal and the execution evidence isn't there yet. human video as proxy is the third. if robots could learn from watching humans, you tap unlimited data. the problem: human hand geometry and force feedback don't map onto robot actuators. you're learning the shape of motion without the physics that make it work. what's actually working today is locomotion. narrow manipulation in structured environments. inspection and sensing. quadrupeds doing thermal inspection. no general-purpose manipulation required. the hardware race is loud, capital-intensive, winner-take-few. but the data infrastructure race is quiet, undercapitalized, wide open.

Cool. But in the end, Energy abundance determines geopolitical power. wsj.com/business/autos…

Listening to Wenfei Zhou, @chrisrilling, @ax_pey, ⁦@sanjil⁩, and moderator @mexitlan discuss gaps in robotics intelligence. Thanks to ⁦@ClaraChengGo⁩ and ⁦@openmind_agi⁩ for organizing this event. #sri

When one thing becomes abundant and cheap, another thing becomes scarce and valuable.

Now I can finally have a dinner party debate where Wittgenstein, Turing, and I finally clear the air and get all our points of view on the table.

What are the main traits you want in a CTO? Everyone's list is different but I look for: 1. High Integrity 2. Love of being hands-on and experiencing the customer's pain 3. Deep thinker unafraid to dive into any part of the technical stack 4. Someone that A players want to work with 5. Keeps me honest, makes me better @dribeiroalves matches all these. This is why I am so happy to be working with David again as CTO of @HapticLabsAI ! I first met David 7 years ago when I recruited him to work on some of the funkiest challenges in distributed systems. Robotics AI has its own choppy waters of challenges to navigate. I am happy to have David row alongside @0xChrisM me in these new seas...

Diego @mexitlan asks wonderful questions. As a real problem solver. How is distillation of robotics model different from LLM. Why there is no Huggingface for robotics. What exactly is missing — For bottlenecks like this, Diego has curated an amazing 70-page note! Truly amazing! Love the open-source design. Best wishes for the launch next week.