Comment by noir_lord
1 day ago
Pretty much, The one thing I use it for is as a sanity check, pretty much "Look at <SomeFile>, point out issues you see, summarise them tersely" and it'll spot stuff a code review by a human might have spotted (in the mythical land where people actually do code reviews properly and don't just flag a spelling mistake to "show they looked at it").
Beyond that I don't trust it at all and I still write all my code the meat sack way.
Trust is earned not given and it hasn't earned it yet.
If anything, I think this hype cycle is fast exposing just how many people, teams, and companies just don't care about what is correct. They just wanna feel good about themselves and get paid.
I for one welcome the fact that this whole thing has driven me back to books and deeper into the fundamentals. I have never read so much on math, hardware, and history as in the past 3 years or so.
Much the same here - I have gone back to basics and am studying a lot more than I ever did.
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Are you doing those things for your own enjoyment though, or to eventually capitalize on it?
And if it’s just for enjoyment, then doesn’t it make sense other people who want the same would just get a job where they can keep pushing things to an AI, feel good, get paid, then quickly get back to the hobbies they really love?
You are advocating for the kind of compartmentalization that got us here in the first place.
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My advice is to try letting the agent fill in the gaps. You can probably architect better than it can. Write your class outlines, explicitly define the public facing bits and what you want APIs to look like. Write the key integration tests that you know ought to pass.
The real advantage is that agents routinely write code without any silly copy/paste mistakes like accidentally accessing x twice on a coordinate operation instead of x and y. You can add some comments for what the function should do, throw in some real/pseudo code and let the LLM figure it out.