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
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>