This vision of AI programming is DOA.

The first step is "define the problem clearly".

This would be incredibly useful for software development, period. A 10x factor, all by itself. Yet it happens infrequently, or, at best, in significantly limited ways.

The main problem, I think, is that it assumes you already know what you want at the start, and, implicitly, that what you want actually makes some real sense.

I guess maybe the context is cranking out REST endpoints or some other constrained detail of a larger thing. Then, sure.

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thefourthchime 3 days ago | parent | next [-]

I disagree with being detailed, many times I want to AI to think of things, half the time it comes up with something I wouldn't have that I like.

The thing I would add is to retry to prompt, don't tell it to fix a mistake. Rewind and change the prompt to tell It not to do that it did.

	▲	athrowaway3z 3 days ago \| parent [-]
		I agree there is a lot of value to have it do, what it considers, the obvious thing. It is almost by definition what the average programmer would expect to find, so it's valuable as such. But the moment you want to do something original, you need to keep high-level high-quality documentation somewhere.

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dec0dedab0de 3 days ago | parent | prev | next [-]

Figuring out what you want is the hard part about programming. I think that's where AI augmentation will really shine, because it lowers the time between iterations and experiments.

That said, this article is basically describing being a product owner.

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ankrgyl 3 days ago | parent | prev | next [-]

(Author here) I can certainly appreciate having an alternate perspective, but I think it's unfair to say it's DOA. I've personally used this workflow for the last 6 months and shipped a lot of features into our product, including the lowest levels of infra all the way to UI code. I definitely think there is a lot to improve. But it works, at least for me :)

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Graphon1 3 days ago | parent | prev [-]

> is that it assumes you already know what you want at the start, and, implicitly, that what you want actually makes some real sense.

My experience is different. I find that AI-powered coding agents drop the barriers to experimentation drastically, so that ... yes if I don't know what I Want, I can go try things very easily, and learn. Exploration just got soooo much cheaper. Now that may be a different interaction that what is described in this blog post. The exploration may be a precursor to what is happening in this blog post. But once I'm done exploring I can define the problem and ask for solutions.

If it's DOA you'd better tell everyone who is currently doing this, that they're not really doing this.