59 karma · joined September 19, 2020
Codeflash optimizes any Python code for performance by using AI and verification.
We make all human and AI written code super-intelligent by discovering new algorithms and fixing any performance mistakes.
I wrote up my findings on why this happens and some practical alternatives that can give you significant performance improvements.
*TL;DR:* deepcopy's recursive approach and safety checks create memory overhead that often isn't worth it. The post covers when to use alternatives like shallow copy + manual handling, pickle round-trips, or restructuring your code to avoid copying altogether.
Has anyone else run into this? Curious to hear about other performance gotchas you've discovered in commonly-used Python functions.
There is grey area that the new code might cause licensing issues if it derives from other licensed code. But does that mean that no open source code with open license can accept code from AI codegen models? That seems excessive.
Have you contributed AI generated code to open source projects that you don't own? Have you received any such pushback?
- You have the best mental model about a piece of code and its system the best when you are working on it, not when you read a ticket description of what should happen. This leads to overall better efficiency and less context switching.
- I think that the principles of "leave things better than you first found them" and "with every not-so-minor change, one should think about how would you architect the system the best way possible at this current time" great principles.
- I find that "Refactoring sprints" never really work great. They tend to be inefficient and rarely prioritized tasks by the management. As developers we have responsibility over code and make sure that its in the best state possible as an implicit description of our profession.
Even though it pays to be focused, I think there is merit in the exploration of code-base as it helps figure out new ideas and avenues of improvement.