Motivator❤️💙

New episode of The Information Bottleneck is out 🥳 In this one, we talked with @srush_nlp, a researcher at Cursor and professor at Cornell, about the hottest topic - coding agents! We talked about how Cursor trains Composer, the challenges, and where all this is going. One point Sasha made stuck with me. Coding agents work really well, but only when you can specify a clear hill-climbing signal. A problem where the agent knows it's getting better. Karpathy tried this on nanochat a few weeks back, letting an agent run overnight to optimize the validation loss autonomously. The follow-ups were mixed. Sometimes the "improvement" was worse than classical methods. Most real problems don't come pre-packaged with a reward signal at all. You don't know if a new architecture is better until you've already run the experiment. You don't know if a refactor is cleaner until someone reads it. You don't know if a product decision worked until months later. A lot of what we call hard problems are hard precisely because the signal is missing, noisy, or expensive to get. I think the next big challenge isn't getting agents to solve problems. It's finding problems you can actually formalize as hill climbing, or building a cheap proxy that correlates with what you care about.