[ivoras]

I did a similar thing but with backend-heavy code, and I agree with this assessment:

> In particular, I asked ChatGPT to write a function by knowing precisely how I would have implemented it. This is crucial since without knowing the expected result and what every line does, I might end up with a wrong implementation.

In my eyes, it makes the whole idea of AI coding moot. If I need to explain every step in detail - and it does not "understand" what it's doing; I can virtually the statistical trial-and-error behind its action - then what's the point? I might as well write it all myself and be a bit more sure the code ends up how I like it.

link: https://www.linkedin.com/feed/update/urn:li:activity:7289241...

[doug_durham]

Because as op pointed out it's faster. It has ready access to the correct usage of different libraries.

[WD-42]

So does the language features of basically any modern editor.

[simonw]

LLMs are way, way faster at typing code than I am. I often dictate to them exactly what I need and it saves me a bunch of time.

[bcrosby95]

So you would say typing speed matters?

[alkonaut]

There are tons of use cases. E.g. if you know an algorithm (take any pseudocode description from a moderately complex algorithm on Wikipedia for example) and you know the programming language, you still may be looking at an hour or two of typing just to get that pseudocode down into code using your own language, variable names, libraries.

But this is the kind of thing a LLM excels at. It gives you 200 lines of impl right away, you have a good understanding of both what it should look like and how it should work.

Slow and error prone to type but, but quick and easy to verify once done, that's the key use case for me.

[guelo]

It’s a better, faster, personalized Stack Overflow. Just like SO you might be led down the wrong path by an answer, but if you’re a programmer and you say you don’t get value out of Stack Overflow I don’t believe you.