this post was submitted on 04 Oct 2024
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I feel like a broken record saying this. But AI frequently does solve coding problems for me that would've taken hours. It can't solve everything, and can't handle large amounts, but it can be genuinely useful.
Same, but it has to be presented well. If you want it to work for you like a Junior Coding Assistant you need to talk to it like such; outline what you need, refine the prompt for caveats, and provide unique information for specialized use cases. I find it especially helpful for one off programming in languages I'm not familiar with or getting me past the mental block of a blank page.
Also, there's a lot of stuff being thrown at LLMs that really shouldn't be. It's not the be all end all of AI tech.
In my experience the main risks in coding are poor communication about what the thing is supposed to do and why and then translating this into a clear specification that everyone understands and can push forward on. Rarely is it about chugging away at a problem, which is mostly about typing speed and familiarity with dev tooling.
What kinds of things has it saved time on? It has only caused headaches for those around me. At best they get something that is 90% what they asked for but they then need to spend just as much time finding the 10%.
The most praise I've seen is for writing a bunch of tests, but to me this is actually the main way you defend a specification, that most important step I mentioned above. It's where you get to say, "this captures what this stupid thing is supposed to do and what the expected edge cases look like". That's where things should be most bespoke!
Diagnosing networking issues, short bash/python scripts of any and all purposes, gdb debugging, finding and learning how to use appropriate libraries, are most of my use cases. It's not a one-and-done either, I often have to ask it to explain, or fix a broken aspect, or Google the documentation and try again, etc.