We are releasing WorldDiT, a unified architecture for robotics world modeling and control. On the LIBERO benchmark, it performs the best among all publicly released methods that do not need a VLM to generate actions. Its size and performance sit on the reported Pareto frontier.

NVIDIA now joins the bandwagon of moving away from LeetCode-style interviews. The entire focus was on 3 key optimization concepts: - Gradient Descent and Global Optima - Full-Batch, Mini-Batch, and SGD - Generalization Gap and Flat Minima The best teams are looking for engineers with conceptual depth.

ROSCon is in Toronto this week. Tonight, we’re hosting Toronto Robotics Night for people here for the conference and the local robotics community. Come by after the sessions for drinks and appetizers. We start at 6:30, a 5 minute walk from the ROSCon venue. RSVP to join: luma.com/501ufk17

Meta is on fire today 🔥 They just announced its new VR Glasses. Basically an Apple Vision Pro-like experience JAMMED into a pair of glasses. • Only 100g • Micro-OLED displays • Eye + hand tracking • External compute/battery puck • $1,299 This is the direction VR needs to go to become competitive. Smaller, lighter, more comfortable, but still stay true to the spatial computing experience. Launching Spring 2027. Will you be getting these?

AI will beat all fields of science by 2028? Hardly. As I have often pointed out, the only AI that works well today is AI in the virtual world behind the screen, for example for automatically summarizing documents, playing chess, proving theorems, creating images, programs and PowerPoint slides. But there is no AI-controlled robot that can do what a plumber can do, or what a capuchin monkey can do, because the physical world is much more demanding than the world behind the screen. Passing the Turing Test is much easier than True AI or Real AI in the physical world. Hence the Turing Test is not a good way of measuring intelligence. To achieve True AI, AI software research per se is not enough, it has to be combined with the physical world of machines and robots. No AGI without mastery of the real world [1]! Much of science is about learning to interact with the real world. Our fragile AI-driven robots are really bad at that. Sure, as I have predicted for 5 decades, at some point we'll have superhuman physical AI. But it will take longer than 2 years. REFERENCE [1] Sec. 20 of the "Annotated History of Modern AI and Deep Learning" (2022-2025) arXiv:2212.11279

After 2+ years in the robotics data space, we are shutting @Eidon_AI down. The thesis was right. But the business is brutally hard. We close this chapter by open-sourcing everything we built and sharing lessons for anyone venturing into the space.

Grok 4.7 is here. It's a notable improvement over Grok 4.6 at the same price and speed.

Two Anthropic employees said this afternoon that unidentified biological material had escaped containment at the company’s Bay Area wet lab. The employees described the material as “living organisms.” Its nature and the extent of the breach remain unclear.

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution