Do you have any questions about China? Anything at all. I'll do my best to answer.

Have you started using Tesla's Full Self-Driving (FSD) feature? I still can't use it and can only rely on assisted driving. It seems that the advancement of intelligent technology will eventually face national boundaries. Regulation and approval will be a major issue, and adaptability across different countries is also a concern. You can share your country and current situation in the comments section.

"An army of AI agents broke into John Deere and SharkNinja, mostly on their own" types of engineers reacting to this: ML infra: calculating whether "army" means 3 agents sharing a context window or an actual 24-card A100 cluster Security: "mostly on their own" has me squinting at the Norforge writeup like a demo video played at 2x speed Agent harness: one guy alt-tabbing 14 terminal windows at 3am babysitting "autonomous" agents with a retry cron job Does anyone run an army of agents without hitting a rate limiter by hour two?

The Robot Report on BLDC motor selection: choosing the right back-EMF constant and thermal derating curve is just a minor speed bump when cogging torque fights your compliance loop at 0.3 rad/s, was it ever really minor?

What Does Scaling Law Mean for Physical AI and Robotics? For LLMs, scaling laws are relatively well established: More data, larger models, and more compute generally lead to predictable reductions in training loss. But what about robotics? If we increase robot training data by 10x, will real-world task success rates improve predictably? If a VLA learns from 1 million hours of robot demonstrations, can it generalize to unseen tasks, environments, or even different robot embodiments? If we scale up World Models, will better predictions actually translate into better physical control? We've already seen evidence of scaling in robotics. Research such as RT-X, OpenVLA, and Octo suggests that increasing data scale and diversity can improve performance and generalization. But we still lack a broadly validated, unified scaling law that reliably predicts general-purpose robotic capabilities in the real world. The real question for Physical AI may not be whether scaling works, but WHAT exactly we should scale. Model parameters? Training data? Interaction experience? Environment diversity? Robot embodiments? Or have we simply not found the right model architecture yet? LLMs have found an effective path to scaling. Robotics is still searching for its own.

I'm paying around $0.10/kWh in China, but that's the lowest off-peak rate, not the average. Your €0.28–0.44/kWh in Germany is roughly 3–5 times higher. any other contry?

Sphinx: a hook that quizzes you on your AI's code before shipping demo: you answer Socratically, every line makes sense my delta arm IK solver at 3am: "what does line 47 do" me: "the agent said so"

ADI Physical AI Transition Lead - shipped a VLA to a real arm without tripping joint torque limits on demo day - comfortable saying "sim2real gap" in a pitch deck

问题来了，请问： 鸡是自由呢 还是被困呢?

LLMs could afford to be wrong when GPT-3 arrived. Physical AI can't. A hallucination in a chatbot is often just a bad answer. A hallucination in a robot can be a broken machine, a damaged product, or an injured person. Maybe that's one of the biggest reasons Physical AI is moving so much slower than LLMs. Intelligence can emerge before reliability. But in the physical world, reliability is what makes intelligence useful.

Big model companies start out as investors, patiently waiting for others to perfect humanoid robots. Then they watch for a while and think: “Y’all are moving way too slow… Fine, I’ll do it myself.” OpenAI is officially building its own humanoid robot in-house. After backing Figure AI and 1X, OpenAI finally jumps into physical AI full throttle. The team is hiring for motion control, actuators, and AI training infrastructure, with a demo prototype targeted for 2027 — though real-world deployment is still years away. Simply put: OpenAI got tired of waiting for the physical AI industry to catch up. The most powerful AI brain is now building its own robot body. #OpenAI #HumanoidRobot #PhysicalAI #EmbodiedAI

how the claude code team does code review: reviewer: LGTM also reviewer: who actually approved this my delta policy after 200 demos still picks up air

Waymo Toronto be like: - streets "in the coming days" - riders 2028 - I spent all week on one sim edge case and they gave themselves 2 years of buffer?