- LLMs are absolutely abysmal at PyTorch. They can basic MLP workflows, but that's it more or less. 0% efficiency gained.
- LLMs are great at short autocompletes, especially when the code is predictable. The typing itself is very efficient. Using vim-like shortcuts is now the slower way to write code.
- LLMs are great at writing snippets for tech I am not using that often. Formatting dates, authorizing GDrive, writing advanced regex, etc. I could do it manually, but I would have to check docs, now I can have it done in seconds.
- LLMs are great at writing boilerplate code, e.g. setting up argparse, printing the results in tables, etc. I think I am saving hours per month on these.
- Nowadays I often let LLMs build custom HTML visualization/annotation tools. This is something I would never do before due to time constraints, and the utility is crazy good. It allows my team to better understand the data we are working with.
- LLMs are absolutely abysmal at PyTorch. They can basic MLP workflows, but that's it more or less. 0% efficiency gained.
- LLMs are great at short autocompletes, especially when the code is predictable. The typing itself is very efficient. Using vim-like shortcuts is now the slower way to write code.
- LLMs are great at writing snippets for tech I am not using that often. Formatting dates, authorizing GDrive, writing advanced regex, etc. I could do it manually, but I would have to check docs, now I can have it done in seconds.
- LLMs are great at writing boilerplate code, e.g. setting up argparse, printing the results in tables, etc. I think I am saving hours per month on these.
- Nowadays I often let LLMs build custom HTML visualization/annotation tools. This is something I would never do before due to time constraints, and the utility is crazy good. It allows my team to better understand the data we are working with.