Comment by sureglymop

I always used to try doing that. Really putting in the work, thoroughly reading the docs, books, study enough to have all the background information and context. It works but takes a lot of time and focus.

However, for side projects, there may be many situations where the documentation is actually not that great. Especially when it comes to interacting with and contributing to open source projects. Most of the time my best bet would be to directly go read a lot of source code. It could take weeks before I could understand the system I'm interacting with well enough to create the optimal solution to whatever problem I'd be working on.

With AI now, I usually pack an entire code base into a text file, feed it into the AI and generate the first small prototypes by guiding it. And this really is just a proof of concept, a validation that my idea can be done reasonably well with what is given. After that I would read through the code line by line and learn what I need and then write my own proper version.

I will admit that with AI it still takes a long time, because often it takes 4 or 5 prototypes before it generates exactly what you had in mind without cheating, hard coding things or weird workarounds. If you think it doesn't, you probably have lower standards than me. And that is with continuous guidance and feedback. But it still shortens that "idea validation" phase from multiple weeks to just one for me.

So: is it immensely powerful and useful? Yes. Can it save you time? Sometimes. Is it a silver bullet that replaces a programmer completely? Definitely no.

I think an important takeaway here also is that I am talking strictly about side projects. It's great as the stakes are low. But I would caution to wait a little longer before putting it in production though.