To give an example, I had 3 things on my hobby to-do list that fit together: learning some data compression algos, learning Golang and testing out Chatgpt/Copilot coding efficiency. So I downloaded slides and exercises from a course of my old uni, and started implementing the exercises in VSCode with Copilot. Whenever I had a question, I would consult chatgpt. Whenever my Copilot code didn't work, I'd go through it line by line to see what's wrong and paste it to ChatGPT to see if it knows what's wrong.

My experience:

- Copilot did a decent job at suggesting functions when I typed out comments. It got progressively worse as the compression algorithms got less common (eg. Huffman vs. entropy coding) The smaller the functions, the more manageable it was. <- You need to write good tests, you want to step carefully through every line of code it writes.

- ChatGPT got most things right, and a few things really wrong. It was always super confident. <- Super dangerous if you don't double check, basically only usable as a refresher on a topic you are already good on.

- If given a piece of code and asked "How would you suggest to refactor this?", ChatGPT gave mostly very useful ideas. <- This is something that I will keep in my workflow.

- The bigger the project got, the worse the overall codebase looked. It became a mix of styles, pretty inconsistent, more subtle bugs were introduced that took me a while to figure out. (The nice thing about lossless compression is that you know if the code is right by using it)

- My "productivity", how many lines of code "I" wrote, was way beyond anything I could usually reach. I do also feel like I got a very quick start into Golang with this, much faster and broader than reading documentation or doing a getting started. That said, that knowledge now definitely needs to be refined in order to use the appropriate concept at the right time. Comparing my code with more professional Golang code bases I spot a lot of things that need to be improved.