Developer Uses AI Agents to Iteratively Optimize Rust Code Performance

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A developer documented an experiment using AI agents to progressively improve the performance of Rust code. The approach involved repeatedly prompting agents to identify and apply optimizations to an existing codebase. The process demonstrated an agentic, iterative workflow where each cycle aimed to produce measurably faster code. The findings were shared in a technical blog post, highlighting practical use cases for AI-assisted low-level performance tuning. The article has gained early attention on Hacker News, suggesting interest in AI-driven code optimization workflows.

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