The post discusses the common practices and mistakes in performance optimization, particularly in SIMD linear algebra. It emphasizes the importance of focusing on the 'happy-path' for optimized code while recommending a structured approach for handling cleanup and residuals. The comments hint at the effectiveness of writing efficient core functions first and testing against simpler versions to ensure correctness, suggesting that many issues can be avoided by initially prioritizing performance profiling before deep optimization. Overall, the exchange highlights a methodical approach to performance optimization that balances clarity, maintainability, and efficiency.