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.
:first-child]:h-full [&:first-child]:w-full [&:first-child]:mb-0 [&:first-child]:rounded-[inherit] h-full w-full。Snipaste - 截图 + 贴图对此有专业解读
，这一点在传奇私服新开网｜热血传奇SF发布站｜传奇私服网站中也有详细论述
Diesel has increased by 8.59p to 150.97p.
营收同比增长 158.9%，达到 7903.8 万美元；。超级权重对此有专业解读
丁宁：当高价值的业务场景需要打通的时候自然会打通。就像云计算最先开始建IaaS，后来SaaS通过场景对下层资源进行封装一样。产品形态不会说凭空臆想出来，一定是在发展中高ROI需求场景启发的。