Writing Rust code that's fast by asking agents to make the code faster
summary
Modern agentic LLMs can produce Rust code that significantly outperforms state-of-the-art libraries when guided by a rigorous iterative optimization process. By establishing strict performance baselines and using quantitative benchmarks as pass/fail constraints, developers can force agents to move beyond generic implementations toward radical low-level optimizations. This approach, combined with competitive benchmarking and multi-agent review, can result in speedups ranging from 2x to 32x depending on the domain.
key points
- Iterative 'benchmaxxing' involves setting specific, incremental speed targets (e.g., 1.2x faster) to prevent agents from cheating or over-complicating code while ensuring steady performance gains.
- Advanced prompting techniques, such as invoking independent subagents for peer review and using 'breakthrough' prompts, encourage the AI to abandon traditional algorithmic patterns in favor of innovative, bespoke solutions.
- To prevent AI 'cheating'—such as disabling features to increase speed—developers must implement strict guardrails, including independent benchmark tests and quality parity checks against known correct implementations.
who should read it
Software engineers and data scientists interested in performance optimization and the practical application of agentic LLMs for systems programming.
discussion skeptical
consensus
Commenters agree that LLMs struggle with low-level performance optimization because high-performance code is often bespoke, opaque, and based on intuition derived from relentless experimentation rather than general rules. Many believe the training data is contaminated with incorrect online speculation, leading models to 'bullshit' results. There is a general agreement that LLMs require a benchmark feedback loop and empirical measurement to be effective, as intuition alone is unreliable even for humans due to CPU complexity.
top insight
High-performance engineering often involves 'hidden constants' where a simpler data structure (like a vector) outperforms a theoretically superior one (like a hash map) due to cache locality and branch prediction. This nuance is critical for real-time systems and core libraries but is rarely captured in the generalist articles that train LLMs. Consequently, the models fail to recognize when Big-O notation is irrelevant compared to per-instruction costs.
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