@smallnest: I ported @karpathy's autoresearch to automated software development, and after various optimizations, the results are phenomenal.
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A developer adapted Karpathy's autoresearch framework for automated software engineering, implementing multiple optimizations that yielded remarkable results.
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Cached at: 04/20/26, 09:44 AM
I ported @karpathy’s autoresearch to automated software development, and after extensive optimization, the results are absolutely mind-blowing.
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@yibie: awesome-autoresearch periodic review, 1 new entry (discussions): Karpathy's Autoresearch for Software Engineers: applying the three-file model of autoresearch (...
awesome-autoresearch is a curated list of autoresearch use cases. This new entry applies Karpathy's autoresearch pattern to everyday software engineering scenarios, providing concrete templates to lower the adoption barrier for engineers.
@sitinme: Saw Karpathy open-sourced a very interesting project autoresearch, which gives a real but small-scale LLM training task to an AI Agent, letting it do research, modify code, run experiments, look at results, and then decide whether to keep or discard the changes. The project is based on a single NVIDIA…
Karpathy open-sourced an experimental project, autoresearch, that lets an AI Agent automatically complete the research loop for small-scale LLM training: modify code, run experiments, evaluate results, and iterate. Humans only need to write the research plan and constraints.
@yibie: awesome-autoresearch updated, added 6 entries. Trace2Evolve — applying autoresearch to the self-evolution of customer service agents. Automatically generate difficult cases, score traces, classify failure reasons, only retain improvements when both benchmark and reliability gate pass...
awesome-autoresearch list updated, adding 6 application cases based on Karpathy's autoresearch pattern, covering scenarios such as customer service agent self-evolution, Shell integration, code configuration self-optimization, RAG tuning, and ASO.
@AYi_AInotes: Two Hong Kong students achieved a 5x speedup on Karpathy's automated research framework. They didn't switch to a stronger model, add more compute, or even change much code. They just added another loop on top of the original loop. This might be the most useful paper of the year for ordinary Agent developers, bar none. Here's the breakdown:
Two Hong Kong students achieved a 5x speedup by adding another loop outside the original automated research framework, without needing a better model or more compute. It is considered one of the most useful papers for ordinary Agent developers.
@yaohui12138: Karpathy released a GitHub open-source project that truly amazed me. The project is called andrej-karpathy-skills, with 130k+ stars on GitHub. I'd call it the most useful AI engineering project of 2026. The problem it solves is extremely precise: making Cl…
Karpathy released an open-source project called andrej-karpathy-skills, centered around a 4KB CLAUDE.md file containing 4 behavioral guidelines (Think Before Coding, Simplicity First, Surgical Changes, Goal-Driven Execution). It significantly reduces AI coding error rates (up to 90%), improving code quality and development efficiency.