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If it's a one off and needs no or minimal maintenance work afterwords, sure.

If it's intended to be actively maintained, then you probably should understand how things work, unless you want to wipe everything and start from scratch when the LLM creates such a mess that it can't be sorted out.

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It's interesting user43928 that you only created your account here 19 days ago and that every one of your comments is pro AI. You don't comment on anything else. Also interesting that you promote Fable by name here (it was only released 2 days ago).

(Don't worry, I know I'm rowing against the tide with this comment. The AI people have decided to destroy the commons for a few more millions on top of the billions they have already been given. It's a shame.)

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What's crazy is the prompt must be something like "pro-AI but still believable and measured", since its "fixed my iOS app albeit with back and forth". Interesting, they know the HN crowd for sure.
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Is that surprising, considering I'm using AI a lot?

I have not hand written a single line of code in months on my side projects.

Obviously I am also interested in discussing the latest model. Your claim that I promote anything or otherwise don't engage here in good faith is both misplaced and against the site rules.

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What was the back and forth?
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Since it couldn't reproduce the issue in the iOS simulator, I had to run it on the device, reproduce the issue, and provide the logs.

This went on for many rounds, during which I tried to steer it toward what I thought was the source of the bug, while the model mostly kept adding instrumentation and logs.

In the end the cause was not what I suspected, but reasonably close to it via another mechanism.

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If you look into large fully-vibecoded projects getting styling changes to work is a nightmare. The problem with agents is using them on large projects without manual review for consistency, guidelines and taste. Doesn't really matter the type of project.

Agents can't look at a large system holistically, guidelines on .md files only go so far.

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This line of thinking is like suggesting people who would like to become structural engineers should learn to Google plans and copy them since in the future, all plans will be out there more or less, or something that insane.
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I suggest people who need some structural engineering done may use an AI tool to do it, in the hypothetical scenario that it was within the AI's capabilities.

That's hardly insane. Not everyone is interested in learning something they want done.

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How do you know if this something is done?

If you do the thing yourself, you know your knowledge limits, you know where the thing lacks. With LLMs, you don't. Maybe it works, maybe it doesn't. You have no idea.

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That is a good question.

In structural engineering, there probably is no risk tolerance.

In the OP's network or port scan? Perhaps you can get away with verifying a few of the results to get an idea about whether it worked as expected.

I use AI mostly on mobile app side projects, and there QA testing on phone and tablet tells me whether a feature works or not.

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CSS keeps improving and models still train on legacy. So yes, knowing what’s possible and how is very much needed if you want to do something scalable and maintainable. Random blog or landing page, not so much.
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