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Comment by elboru | Hacker News Reader

ChatGPT is great with well known tools and libraries. I’ve been able to build HTML/JS apps in minutes. Then I tried building a MAUI app, it started pretty good, but then when I tried to add charts it failed (it’s important to mention this was my first MAUI app), I tried different approaches, in the end I went back to HTML/JS.

That led me to a realization, this could mean that people will likely discard less known libraries or technologies more easily than before. Popular technologies will become even more popular since we’ll be 10x more productive with them compared to less known ones since we won’t have LLMs super powers for those.

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I agree and have had the same thought. This is gas on the fire to winner-take-all dynamics. It’s already hard for new languages and systems to catch up fast enough to technically weaker existing options bc they have mature tooling. But when the robot is fluent in Java and can’t understand $newlang it’s going to be even harder.

It's way too early to tell. I'm betting that projects will be able to fine tune LLMs against their code bases and documentation, making it easier to adopt new open source projects because mini-GPTs will help new users get set up with their exact use case instead of having them dig through a bunch of semi-irrelevant examples.

When I ask it about Swift, I'm often surprised by how good it is.

When I ask it about Objective-C it bullshits and fabricates APIs.

When I ask it about python or bash scripts it is astonishingly good

A couple of weeks ago I made an absurdly efficient arithmetic expression language compiler in python, complete with a RD/pratt parser, control flow graph IR , optimizer (dead code elimination, algebraic, etc) and a simple x86 codegen. Chatgpt wrote most of it.

Then I asked chatgpt to port it piece by piece to C.

Took a while to get a hang of gpt4's token length limitations, as well as fixes here and there... 1.5 hours.

But damn... Most programmers are just not capable of doing this at all.

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