Maybe it is
Isn't that basically how it works?
Just add an additional group of monkeys that aggressively hires and fires the others based on performance
Tada, machine learning
It was the best of times, it was the blurst of times!
asks for password hashing
gets code that looks like password hashing, named like password hashing, but, without any of the hashing
But it has better performance
hash-lite
but, without any of the hashing
Or it's something like unsalted MD4.
Because we are the apes that wrote the code that copilot read.
Tbh, copilot was probably the worst AI coding experience I've had. It actually made me less productive and made me question my competency as a programmer at the same time. Straight up did not have a good time. Use Cody or GPT-4 instead.
It is designed for other purposes than GPT models. Next time try to use copilot as autocompletion, not to generate new code. It's excellent in that.
That's how I was using it; I ended up spending as much time as I was saving going around and cleaning up after it and/or second guessing myself. Basically, because it only operates in the context of the file you're working in, it will suggest garbage half the time if you have to work with resources from other files.
If you have those other files open, it also picks those up. And lately it seems to follow imports too, I feel like
From the docs:
GitHub Copilot analyzes the context in the file you are editing, as well as related files
Tho, I don't know if it allways been that way, maybe they added bigger context later
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But the propaganda from GitHub said it was making devs 80%+/- more productive!
How could this have happened? /s
it works well for me, mostly accurately guesses what I am trying to do, helps a ton with boilerplate code
It was 55% for me. Higher baseline I suppose. <\s>
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it bores me to death… all these coders using AI shit to pretend to save times while you just need to reduce the scope.
