We dive into Andrej Karpathy’s AutoResearch, where autonomous AI agents iteratively rewrite neural-network code, run five-minute training experiments, and optimize a standardized metric (validation bits per byte). With human input limited to high-level directives in a single program.md, these agents drive rapid, fair comparisons and continuous improvement. We explore how this autonomous research approach could accelerate breakthroughs across fields, its potential applications, and the trade-offs of letting machines mostly run the lab.

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