Can LLMs Write Better Code? ➹

Well, almost, but not quite. Max Woolf’s exploration of iterative code improvement using LLMs is a fascinating dive into the potential and pitfalls of AI-assisted programming. While the results show significant performance gains, the journey reveals a common theme: the balance between optimization and overengineering. As the code evolves, it becomes increasingly complex, raising questions about maintainability versus raw speed.

It’s a reminder that while AI can suggest clever solutions, it often lacks the nuanced understanding of what constitutes “better” in a real-world context. The experiments highlight the importance of human oversight in the coding process–after all, a 100x speedup is impressive, but not if it comes with a hefty dose of technical debt.

The fun part is that this isn’t that far off from what you might get from a clever intern, which the cynic in me welcomes as further proof that we are effectively creating artificial smartasses.