misrasaurabh1

I really don't want to share all my personal life information with meta like this.

I like how the proof is so concise. I made progress on some unsolved combinatorics problems but the proof was 45 pages long to extend the frontier by one step.

I've been running into performance bottlenecks in the wild where `copy.deepcopy()` was the bottleneck. After digging into it, I discovered that deepcopy can actually be slower than even serializing and deserializing with pickle or json in many cases!

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.

I tried contributing some AI Generated optimizations to core Scientific Python packages like NetworkX and Scikit-image. But they were rejected since they were created from AI models.

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?

What we discovered that LLMs aren't really the answer wrt performance, the whole system around verifying correct and performance is the key.

Hi, I am the creator of Codeflash. The idea is to do everything that a professional performance engineer would do and to automate it. So codeflash, analyzes your code, creates correctness tests, creates performance benchmarking tests, executes these tests to figure out the behavior and performance of code. Along side it also profiles and determines the line coverage of your original code. Then it uses all this info to generate multiple optimizations and applies them one by one to see if the new code is indeed an optimization, i.e. is correct and is more performant. If this is the case then it opens a Pull Request for dev's review with all the info.