[gf000]

I don't know, LLMs strive on human text, so I would wager that a language designed for humans would quite closely match an ideal one for LLMs. Probably the only difference is that LLMs are not "lazy", they better tolerate boilerplate, and lower complexity structures likely fit them better. (E.g. they can't really one-shot understand some imported custom operator that is not very common in its training data)

Also, they rely surprisingly closely on "good" code patterns, like comments and naming conventions.

So if anything, a managed language [1] with a decent type system and not a lot of features would be the best, especially if it has a lot of code in its training data. So I would rather vote on Java, or something close.

[1] reasoning about life times, even if aided by the compiler is a global property, and LLMs are not particularly good at that

[hnaccount_rng]

But that is leas fundamental then you make it sound. LLMs work well with human language because that’s all they are trained on. So what else _could_ an ideal language possible look like?

On the other hand: the usefulness of LLMs will always be gated by their interface to the human world. So even if their internal communication might be superseded at some point. Their contact surface can only evolve if their partners/subjects/masters can interface

[dudeinhawaii]

When I think of the effect of a single word on Agent behavior - I wonder if a 'compiler' for the human prompt isn't something that would benefit the engineer.

I've had comical instances where asking an agent to "perform the refactor within somespec.md" results in it ... refactoring the spec as opposed to performing a refactor of the code mentioned in the spec. If I say "Implement the refactor within somespec.md" it's never misunderstood.

With LLMs _so_ strongly aligned on language and having deep semantic links, a hypothetical prompt compiler could ensure that your intent converts into the strongest weighted individual words to ensure maximal direction following and outcome.

Intent classification (task frame) -> Reference Binding (inputs v targets) -> high-leverage word selection .... -> Constraints(?) = <optimal prompt>