performance-optimization
Apply systematic performance optimization techniques for Python and Rust code: estimation + profiling, API/bulk design, algorithmic wins, cache-friendly memory layout, fewer allocations, fast paths, caching, and compiler-friendly hot loops. Use for performance code reviews, refactors, and profiling-driven optimizations. Keywords: performance, latency, throughput, cache, allocation, memory layout, PyO3, msgspec, tokio, async, pprof, py-spy, perf.
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