[tyingq]

That matches my experience. It's a sort of shortcut to the old process of googling for examples and sifting through the results. And those results, I didn't typically cut and paste from them, or if I did, it was mostly as a sort of a scaffold to build from, including deleting a fair amount of what was there.

Many times it works really well, and it surfaces the kind of example I need. Sometimes it works badly. Usually when it's bad, going to the google/sift method has similar results. Which I guess makes sense, it couldn't find much to train on, so that's why it's answer wasn't great.

One area it works really well for me is 3rd party apis where their documentation is mostly just class/function/etc. ChatGPT generally does a good job of producing an orchestrated example with relevant comments that helps me see the bigger picture.

[shanusmagnus]

Me too. As someone who used to be a dev but hasn't written code professionally in twelve years or so, it was such an amazing accelerant. My iteration loop was to contextualize it (in English and in code), ask how to do a thing, look at its response, tweak it, execute, see what happened, alter it some more.

The fact that it usually had errors didn't bother me at all -- it got much of the way there, and it did so by doing the stuff that is slowest and most boring for me: finding the right libraries / functions / API set up, structuring the code within the broader sweep.

Interesting side note: un-popular languages, but ones that have been around for a long time and have a lot of high-quality and well-documented code / discussion / projects around, are surprisingly fecund. Like, it was surprisingly good at elisp, given how fringe that is.

[bumbledraven]

With GPT-4, you can often just paste the error message in without any further commentary, and it will reply with a modified version of the code that it thinks will fix the error.

[gumballindie]

And then you waste time fixing the error the "fix" gpt introduced. Clever.