Amazing write up by David My favorite was the anecdote 'Alibaba does a “worst product” award every year at all hands.' It is such a great illustration of how American and Chinese education and culture differ

General world models could overtake specialized vision-language-action models around 2027. @1x_tech founder @BerntBornich made this specific prediction in his interview with @ti_morse Key highlights: 1. Data diversity is the constraint. More recordings of the same tasks add less than exposure to new environments, objects, and ways tasks fail. In Bernt’s words, robotics is “diversity bound.” 2. Internet video could do most of the training. Bernt expects it to provide roughly 99% of 1X’s training data. First-person human video, simulation, teleoperation, and robot experience would supply other kinds of examples. 3. The model should eventually collect its own training data. Bernt wants deployed robots to attempt unfamiliar tasks, record successes and failures, and improve through retraining without a human operating each attempt. 4. That gives the 50,000 target a second purpose. 1X aims to manufacture and ship 50,000 NEOs in 2027. A fleet that size could expose the model to far more variation, provided the robots meet the safety and quality bar for deployment. Interview:

ICLR 2027 submission: we benchmarked all frontier models and our model performs... Anthropic & OpenAI today: no you don't. ICLR deadline is 9/25, so i guess no excuse not including opus5.5 and gpt6-luna & sol in the paper.

Scott Walter, Robotics Research Diligence Director at RoboStrategy ($BOT), wants a humanoid decathlon. “I'm throwing down the gauntlet for the humanoid decathlon challenge.” One robot. The same 10 human events. The same rules. That means more than a sprint demo: › Hurdles and turns › A pole vault that tests upper-body and grip strength › A baton exchange that demands manipulation › A long-jump sand pit, because sand in the joints is part of the test A specialized robot can win one optimized event. A decathlon asks which joints, control policies, and hands survive across a body of work. @GoingBallistic5 @RoboStrategy

I'm sympathetic to mathematicians who worry that AI will lead to a bunch of "alien proofs" that no human understands. It may be that the future of math is mostly finding alien proofs and doing the work needed to make them intelligible to humans. Which, frankly, is the situation in biology already.

yeah this actually looks really bad for zhipu/glm given the recent situation, so far no response from their official account neither Zibo @ZixuanLi_ or @louszbd respond, probably their first crisis PR.

thoughts on Typesafe/Jev: I'm surprised that a general-purpose classifier (or discriminative model) can be just as interesting to the public as a general-purpose generative model. I've worked on search and discriminative models for years, so maybe I should have called this upfront, tbf I never thought about making them general-purpose beyond search. Typesafe also positions Jev really well as "System 1", a complement to the generative models we already have. Humble, unassuming, and sidestepping the frontier-lab warzone. Jev's success could be disruptive to today's agentic systems in many ways, such as tool-calling and routing patterns. Over the last three years the consensus was to use a small generative model for routing, tool calling, MCP etc. Time to pause and rethink such architecture, because we'll probably see tool calling and routing move back to discriminative models. Whoever ships the next Jev-like open-weight base model will likely win the community.