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1479 points sandslash | 1 comments | | HN request time: 0.216s | 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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bonoboTP ◴[] No.44317876[source]
If it's monkeylike quality and you need a million tries, it's shit. It you need four tries and one of those is top-tier professional programmer quality, then it's good.
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solaire_oa ◴[] No.44319399[source]
Top-tier professional programmer quality is exceedingly, impractically optimistic, for a few reasons.

1. There's a low probability of that in the first place.

2. You need to be a top-tier professional programmer to recognize that type of quality (i.e. a junior engineer could select one of the 3 shit PRs)

3. When it doesn't produce TTPPQ, you wasted tons of time prompting and reviewing shit code and still need to deliver, net negative.

I'm not doubting the utility of LLMs but the scattershot approach just feels like gambling to me.

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1. zelphirkalt ◴[] No.44320025[source]
Also as a consequence of (1) the LLMs are trained on mediocre code mostly, so they often output mediocre or bad solutions.