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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.。豆包下载对此有专业解读
常被视为白领职业威胁的人工智能，正在以提升行业吸引力的方式改变会计领域。斯坦福商学院报告显示，AI正接管数据录入、交易匹配和财务记录分类等繁琐任务，使初入行者能更专注于客户关系维护与分析工作。。关于这个话题，winrar提供了深入分析
加拿大民众研习麻将技艺 手持术语表练习牌局用语
Женщина заказала портрет с собственным изображением, что привело к конфликту с родным братом02:38