zlacker

>>salkah+(OP)
I think It's not because AI working on "misaligned" goals. The user never specify the goal clearly enough for AI system to work.

However, I think producing detailed enough specification requires same or even larger amount of work than writing code. We write rough specification and clarify these during the process of coding. I think there are minimal effort required to produce these specification, AI will not help you speed up these effort.

>>smy200+m7
That makes me wonder about the "higher and higher-level language" escalator. When you're writing in assembly, is it more work to write the code than the spec? And the reverse is true if you can code up your system in Ruby? If so, does that imply anything about the "spec driven" workflow people are using with AIs? Are we right on the cusp where writing natural language specs and writing high level code are comparably productive?

>>crabmu+p8
I believe that the issue right now is that we're using languages designed for human creation in an AI context. I think we probably want languages that are optimized for AI written but human read code, so the surface texture is a lot different.

My particular hypothesis on this is something that feels a little bit like python and ruby, but has an absolutely insane overkill type system to help guide the AI. I also threw in a little lispiness on my draft: https://github.com/jaggederest/locque/

>>jagged+xl
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

>>gf000+QT
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