Magic, the AI startup founded by Eric Steinberger and Sebastian De Ro, published a research update claiming its language-model pretraining is over 10x more compute-efficient than recipes behind leading open-weight base models. It says matching DeepSeek V4 Pro’s pretraining took 50x less compute, costing about $0.5M on Nvidia GB200 (and scaling to about $4M). The result is a new cost-and-benchmark narrative based on its own held-out bits-per-byte loss measurements and a next stated focus on long-horizon reinforcement learning and deployment safety policies, while noting the figures are unverified and no model has shipped.
Danijar Hafner left Google DeepMind to start a new stealth-mode robotics startup focused on AI agents that can plan ahead for unfamiliar situations. He is building on Dreamer 4, which learned diamond mining from an offline dataset of recorded gameplay videos without directly interacting with the game. The work shifts from game-based world models to physical humanoid robots designed to handle new indoor layouts and respond to unexpected events in real homes.
DuckFightClub is launching a livestreamed competition where eight teams train reinforcement-learning policies to control MicroDucks in a simulator. It starts today with 55 followers listed on the page. The event shifts participants from building robots to training the ducks’ brains, with IRL fights when the real MicroDucks ship.
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