LLMs and the Limits of Automated Code Optimization

David Andersen experimented with using Large Language Models (LLMs) to optimize code that finds the difference between the smallest and largest numbers whose digits sum to 30 in a list of a million random integers. Initial Python and Rust code ran slowly. While the LLM improved parts, such as the digit summing function, it missed a crucial optimization: checking if a number is relevant *before* the expensive digit sum calculation. Manual intervention, involving a faster random number generator, parallelization, and preprocessing, sped up the Rust code by a factor of 55. This highlights LLMs' limitations in code optimization, particularly for complex problems demanding deep algorithmic understanding and parallelization strategies. Human ingenuity remains crucial.