LLM 迭代优化写 Rust 提速 Python
推荐指数 54.0 NO. 019 · 2026.09.23
发布2026/09/22Score85Comments46
为什么值得看
作者让 agent 反复要求 LLM 优化代码，最终用 Rust + PyO3 替代 Python 核心逻辑实现大幅加速。这验证了 agentic 优化在跨语言重写上的可行性，但暴露了通用 agent 缺乏真正性能优化能力的短板。
编辑判断
这个实验的真正价值不在结果，而在方法论：把"优化"拆解成"写 Rust"这个可验证的子任务，规避了 LLM 对"更好代码"的滥用。之前让 Sonnet 直接优化 Python，它只会堆无用功能；给定明确语言边界后，agent 才能有效迭代。
对做 AI 基础设施的团队有启发：如果你的瓶颈在 Python 性能，不要指望 agent 自动发现，而是预设技术路线（如 Rust/Cython/Numba）让 agent 执行。这比端到端"帮我优化"更可控。
一个待验证的延伸：这种"预设架构 + agent 填充实现"的模式，是否比完全自主的 agent 更适合工程落地？
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意见分歧 42 条评论
核心争论：LLM能否自主优化性能，还是仅能在人类设定测量框架后迭代试错？
If it can be measured, then LLMs can optimize it. Once I had repo commands that could dump `sample` results and a cpu profiler/trace and then a benchmark tool that let me A/A + ABBA/BAAB-test the current modified git workspace against HEAD or any commit, the LLMs could just do their t
> If it can be measured, then LLMs can optimize it Then they can start attempting to optimize it. They can also spin round and round making the numbers worse because they don't actually know what to do.
Yeah, but that's just the scientific process of hypothesis -> evidence -> conclusion. You need a measurement that can falsify hypotheses and reject branches that won't work. Also, if all you have left in your project are performance issues that are hard to identify without flailing around (even with