zlacker

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+(OP)
If you are on the same wave length as someone you don't need to produce a full spec. You can trust that the other person has the same vision as you and will pick reasonable ways to implement things. This is one reason why personalized AI agents are important.

>>crabmu+(OP)
Programming languages can be a thinking tool for a lot of tasks. Very much like a lot of notation, like music sheet and map drawing. A condensed and somewhat formal manner of describing ideas can increase communication speed. It may lack nuance, but in some case, nuance is harmful.

The nice thing about code compared to other notation is that it's useful on its. You describe an algorithm and the machine can then solve the problem ad infinitum. It's one step instead of the two step of writing a spec and having an LLM translate it, then having to verify the output and alter it.

Assembly and high level languages are equivalent in terms of semantics. The latter helps in managing complexity, by reducing harmful possibilities (managing memory, off-by-one errors) and presenting common patterns (iterators/collections, struct and other data structures, ....) so that categories of problems are easily solved. There's no higher level of computing model unlocked. Just faster level of productivity unlocked by following proven patterns.

Spec driven workflow is a mirage, because even the best specs will leave a lot of unspecified details. Which are crucial as most of programming is making the computer not do the various things it can do.

>>skydha+y6
> most of programming is making the computer not do the various things it can do

This is a very stimulating way of putting it!

>>crabmu+(OP)
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+8d
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+rL
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

>>gf000+rL
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>