[bluegatty]

So where AI has deterministic inputs and outputs it is extremely good to the point I think that there's a theoretical issue around computational there.

Like - it can do the work for us.

It jives with post training and verifiable rewards.

The reason AI doesn't do well at 'architecture' is 1) are are bad at it and have given it a lot of mush and 2) we don't have good abstractions for it.

The result is - you stick to 'very strong conventions' and if you walk of that path you're risking a lot.

Toolchains are very deterministic, the AI can take it apart and re-assemble like Lego - and each level of the space is also deterministic. It's perfect for AI.

[mpweiher]

> The reason AI doesn't do well at 'architecture' is [...] 2) we don't have good abstractions for it.

Maybe it's time for an architecture-oriented programming language?

https://objective.st

https://dl.acm.org/doi/10.1145/3689492.3690052

[regularfry]

I have found that if you give it a pre-baked architecture to work within it works really well. It's not really what you'd use here, but just saying "this project uses a ports and adapters architecture" can stop it from generating mush by default. I think it's not so much that they're bad at it as that they don't have a clear reason to pick something other than mush. And not just "something" - a specific something, from a fairly short list of architectures suitable for your problem domain.

[bluegatty]

Yes, totally,with examples and references.

But there's something existential there maybe?

NASA says, any time you make a program that has a new 'launch vehicle' (aka architecture) - the whole project is the 'launch vehicle'.

'Oh, you want use a new architecture? Welcome to the cesspit of hallucination!'

Basically, there's a lot more complexity than we might imagine 'hidden in the nothingness' of he unknown.

Pick a 'known off-the-shelf launch vehicle' first ... then you design the landing craft