[DeathArrow]

Yes, it works for new code and simple cases. If you have large code bases, it doesn't have the context and you have to baby it, telling which files and functions it should look into before attempting to write something. That takes a lot of time.

Yes, it can do simple tasks, like you said, writing a call to get the environment variables.

But imagine you work on a basket calculation service, where you have base item prices, where you have to apply some discounts based on some complicated rules, you have to add various kinds of taxes for various countries in the world and you have to use a different number of decimals for each country. Each of your classes calls 5 to 6 other classes, all with a lot of business logic behind. Besides that, you also make lots of API calls to other services.

What will the AI do for you? Nothing, it will just help you write one liners to parse or split strings. For everything else it lacks context.

[JamesSwift]

Are you suggesting you would inline all that logic if you hand-rolled the method? Probably not, right? You would have a high-level algorithm of easily-understood parts. Why wouldnt the AI be able to 1) write that high-level algorithm and then 2) subsequently write the individual parts?

[scosman]

What's the logic here? "I haven't seen it so it doesn't exist?"

There are hundreds of available examples of it processing large numbers of files, and making correct changes across them. There are benchmarks with open datasets already linked in the thread [1]. It's trivial to find examples of it making much more complex changes than "one liners to parse or split strings".

[1] https://huggingface.co/datasets/princeton-nlp/SWE-bench