21–30 of 61 posts
Obvisouly you have never built software. English is a terrible programming language, you cannot have ambiguity in defining your computation.
nobody except for maybe nasa would make software in this scenario.
(I'll admit that I've built a few "applications" exploring interaction descriptions with our Design team that do exactly this - but they were design explorations that, in effect, used the LLM to simulate a back-end. Glorious, but not shippable.)
I can't even imagine how many joules would be used per function call! As an experiment, it's kind of cool. I'm kind of at a loss to what useful software you'd build with it though. Surely once you've run the AI function once it would be much simpler to cache the resulting code than repeatedly re-generate it? Can anyone think of any uses for this?
Obvisouly you have never built software. English is a terrible programming language, you cannot have ambiguity in defining your computation.
I can't even imagine how many joules would be used per function call! As an experiment, it's kind of cool. I'm kind of at a loss to what useful software you'd build with it though. Surely once you've run the AI function once it would be much simpler to cache the resulting code than repeatedly re-generate it? Can anyone think of any uses for this?
You just tell the AI: use as little energy as possible, by whatever means necessary!
I’m not just making this stuff up of course, got the idea yesterday after reading Karpathy’s tweet about Nanoclaws contribution model (don’t submit PRa with features, submit PRs that tell an llm how to modify the program). Now I can’t concentrate on my day job. Can’t stop thinking about my little elixir beam project.
I can't even imagine how many joules would be used per function call! As an experiment, it's kind of cool. I'm kind of at a loss to what useful software you'd build with it though. Surely once you've run the AI function once it would be much simpler to cache the resulting code than repeatedly re-generate it? Can anyone think of any uses for this?
Haven’t we been seeing libraries that implement this pattern going on two years now? Take the docstring and monkey patch the function with llm generated code, with optional caching against an AST hash key. The reason it hasn’t take off is that it’s a supremely bad and unmaintable idea. It also just doesn’t work very well because the LLM doesn’t have access to the rest of the codebase without an agentic loop to ground…
> You write a Python function with a natural language specification instead of implementation code. You attach post-conditions – plain Python assertions that define what correct output looks like.
Vs
> You write a Python function with ~~a natural language specification instead of~~ implementation code.
In many cases.