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1479 points sandslash | 1 comments | | HN request time: 0s | source
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abdullin ◴[] No.44316210[source]
Tight feedback loops are the key in working productively with software. I see that in codebases up to 700k lines of code (legacy 30yo 4GL ERP systems).

The best part is that AI-driven systems are fine with running even more tight loops than what a sane human would tolerate.

Eg. running full linting, testing and E2E/simulation suite after any minor change. Or generating 4 versions of PR for the same task so that the human could just pick the best one.

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latexr ◴[] No.44317792[source]
> Or generating 4 versions of PR for the same task so that the human could just pick the best one.

That sounds awful. A truly terrible and demotivating way to work and produce anything of real quality. Why are we doing this to ourselves and embracing it?

A few years ago, it would have been seen as a joke to say “the future of software development will be to have a million monkey interns banging on one million keyboards and submit a million PRs, then choose one”. Today, it’s lauded as a brilliant business and cost-saving idea.

We’re beyond doomed. The first major catastrophe caused by sloppy AI code can’t come soon enough. The sooner it happens, the better chance we have to self-correct.

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koakuma-chan ◴[] No.44317997[source]
> That sounds awful. A truly terrible and demotivating way to work and produce anything of real quality

This is the right way to work with generative AI, and it already is an extremely common and established practice when working with image generation.

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1. xphos ◴[] No.44318310[source]
"If the only tool you have is a hammer, you tend to see every problem as a nail."

I think the worlds leaning dangerously into LLMs expecting them to solve every problem under the sun. Sure AI can solve problems but I think that domain 1 they Karpathy shows if it is the body of new knowledge in the world doesn't grow with LLMs and agents maybe generation and selection is the best method for working with domain 2/3 but there is something fundamentally lost in the rapid embrace of these AI tools.

A true challenge question for people is would you give up 10 points of IQ for access to the next gen AI model? I don't ask this in the sense that AI makes people stupid but rather that it frames the value of intelligence is that you have it. Rather than, in how you can look up or generate an answer that may or may not be correct quickly. How we use our tools deeply shapes what we will do in the future. A cautionary tale is US manufacturing of precision tools where we give up on teaching people how to use Lathes, because they could simply run CNC machines instead. Now that industry has an extreme lack of programmers for CNC machines, making it impossible to keep up with other precision instrument producing countries. This of course is a normative statement and has more complex variables but I fear in this dead set charge for AI we will lose sight of what makes programming languages and programming in general valuable