The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat. The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc. A bit more context on this project is here in this tweet.
Sure enough, I found a broken crescent bond on the long bond wire crossing over the controller die, right where it should have connected to the package.
，详情可参考heLLoword翻译
但陆子天终究是个广东人，创业这件事早已深入血液。他在学校学音乐，回到家里却总琢磨着编程和各种科技产品。
Data exfiltration via output — instructing the AI to embed confidential information into its responses, which users might then copy into client-facing documents or external emails.
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