Yeah, the places to use it are (1) boilerplate code that is so predictable a machine can do it, and (2) with a big pinch of salt for advice when a web search didn’t give you what you need. In the second case, expect at best a half-right answer that’s enough to get you thinking. You can’t use it for anything sophisticated or critical. But you now have a bit more time to think that stuff through because the LLM cranked out some of the more tedious code.

(1) boilerplate code that is so predictable a machine can do it

The thing I hate most about it is that we should be putting effort into removing the need for boilerplate. Generating it with a non-deterministic 3rd party black box is insane.

Hard disagree. There is a certain level of boilerplate that is necessary for an app to do everything it needs. Django, for example, requires you to specify model files, admin files, view files, form files, etc. that all look quite similar but are dependent on your specific use case. You can easily have an AI write these boilerplate for you because they are strongly related to one another, but they can’t easily be distilled down to something simpler because there are key decisions that need specified.

Why does it have to be AI instead of a purpose built, deterministic tool?

Because it’s not worth inventing a whole tool for a one-time use. Maybe you’re the kind of person who has to spin up 20 similar Django projects a year and it would be valuable to you.

But for the average person, it’s far more efficient to just have an LLM kick out the first 90% of the boilerplate and code up the last 10% themself.

“Not worth inventing”? Do you have any idea how insanely expensive LLMs are to run? All for a problem whose solution is basically static text with a few replacements?