I'm of the mind that it will be better to construct more strict/structured languages for AI use than to reuse existing ones.

My reasoning is 1) AIs can comprehend specs easily, especially if simple, 2) it is only valuable to "meet developers where they are" if really needing the developers' history/experience which I'd argue LLMs don't need as much (or only need because lang is so flexible/loose), and 3) human languages were developed to provide extreme human subjectivity which is way too much wiggle-room/flexibility (and is why people have to keep writing projects like these to reduce it).

We should be writing languages that are super-strict by default (e.g. down to the literal ordering/alphabetizing of constructs, exact spacing expectations) and only having opt-in loose modes for humans and tooling to format. I admit I am toying w/ such a lang myself, but in general we can ask more of AI code generations than we can of ourselves.

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bityard 19 hours ago | parent [-]

I think the hard part about that is you first have to train the model on a BUTT TON of that new language, because that's the only way they "learn" anything. They already know a lot of Python, so telling them to write restricted and sandboxed Python ("you can only call _these_ functions") is a lot easier.

But I'd be interested to see what you come up with.

	▲	kodablah 14 hours ago \| parent \| next [-]
		> that's the only way they "learn" anything I think skills and other things have shown that a good bit of learning can be done on-demand, assuming good programming fundamentals and no surprise behavior. But agreed, having a large corpus at training time is important. I have seen, given a solid lang spec to a never-before-seen lang, modern models can do a great job of writing code in it. I've done no research on ability to leverage large stdlib/ecosystem this way though. > But I'd be interested to see what you come up with. Under active dev at https://github.com/cretz/duralade, super POC level atm (work continues in a branch)
	▲	Terretta 15 hours ago \| parent \| prev [-]
		> you first have to train the model on a BUTT TON of that new language Tokenization joke?