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I'd say it's more about learning how to organize your work more efficiently.

If you think about a product like marble: it's something that most be chiseled out of time.

Some people can chisel better products: the AI is just a better chisel.

Sometime still has to guide the chisel and judge the art/product.

In our cases, the market judges products.

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I think my point is that its sometimes (!) easier to use the manual chisel rather than go for the automatic chisel and then fix its mistakes.
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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.

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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.

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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?

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You are advocating for the kind of compartmentalization that got us here in the first place.
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Your assumption is that LLMs will ever leave this uncanny valley.

Maybe unforeseen breakthroughs and different architectures are achieved. Given LLM fundamental shortcomings grounded in mathematics and information theory, I highly doubt they will and we will always need to deal with these issues in some capacity.

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I think that bigger context windows help, but I feel that for AI to cross this chasm, it needs to be able to encode more abstract context knowledge. I think the holy grail here is online learning.
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