I reviewed the selected contributor’s work and I do think they contributed more in terms of volume. However, the goal of this project is to speed up computation especially on larger inputs. That said, I have made sure all code I contributed are with high quality and scalability to meet the goal. My own proposal focused specifically on optimizing performance for large inputs. I believe my approach is more aligned with handling larger-scale scenarios efficiently.
Personally, I used AI tools during development as well, but I also made sure to thoroughly refine and optimize the output to ensure correctness and performance. I almost can say for sure that his code will not bring efficiency for large inputs. He/she just used one thread for each data on with branching and IO. It is so obvious that it will NOT bring any speed up when input is large. This is similar to the output I got from AI by simply giving it the task without further optimizing.
There is also one more interesting thing I observed. I checked the past programs of this org. The mentor is a past contributor for 2 years then he became a mentor. Before that there were contributors from other countries. After he became the mentor, all contributors they accepted are from India. Just some fun observation. I am sure it is not related to the selection process. Haha
After all this, I have concluded a few suggestions for the future applicants.
- Contribute more code with the help of AI without caring too much about the quality of it
- Check the past history of your mentor