buzz

Perception is a system problem. One camera misses depth, occlusion, and hand interactions. Gen DAS Ego uses 6 synced cameras (270° FOV). What you get: cm-level joint tracking ms-level head–hand sync full coverage Built for real use: plug-and-play fit 24/7 swappable battery

Say Hello to William 👋 The first AI agent with a phone. 5 billion hours this year will be spent tapping the same buttons on a phone. Testing games. Navigating App. Doing repetitive workflows. We think that should be 0. RT and comment “William” to get access.

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

HTMLを直接3Dの質感として描画できる新機能「html-in-canvas」 複雑な操作画面をWebブラウザの標準機能でそのまま3D空間へ配置できます。 現在はChromeの試験運用機能ですが、Web3DのUI制作を劇的に効率化する可能性を秘めた期待の技術です。

my curtain is in a mood today