I will read it, just to be certain by "might actually help here" - what is "here"? You mean with Rye language design generally, LLM-s relating to new languages or something else? :)
In general I feel like there's some great applicability here in this specific language. The language docs imply a certain degree of homoiconicity, which I think would be really helpful for DSLs like this...
Instead of DSL-s Rye focus much more on constructing specialized, also limited and isolated contexts (scopes), that have just the functions you need or just the custom functions you need, while the evaluation mechanism doesn't change (is the one you (or LLM) already know(s)).
I haven't thought about contexts + LLM-s yet. I will read the PDF you referenced with interest! Here is a little more info about contexts: https://ryelang.org/meet_rye/specifics/context/
I've experienced this to some degree already in using LLMs to write Zig code (ironically, for my own pet programming language). Because Zig is still evolving so quickly, often the code the LLM produces is wrong because it's based on examples targeting incompatible prior versions of the language. Alternatively, if you ask an LLM to try to write code for a more esoteric language (e.g., Faust), the results are generally pretty terrible.
[1] There are a lot of models that achieve this. E.g. Goedel-Prover-V2-32B [2] is a model based off of Qwen3-32B and fine tuned on Lean proofs. It works extremely well. I personally tried further fine tuning this model on Agda and although my dataset was pretty sloppy and small, it was pretty successful. If you actually sit down and generate a large dataset with variety it's pretty reachable to fine tune it for any similar prog lang.
Let’s see.
I’ve vibe-coded some apps with TypeScript and react, not knowing react at all, because I thought it’s the most exemplified framework online.
But I came to a point where my app was too buggy and diverged, and being unable to debug it, I refactored it to Vue, since I personally know it better.
My point is that just because there’s more training data, the quietly is not necessarily excellent; I ended up with a mixture of conflicting idioms seasoned react developers would have frowned upon.
Picking a less exemplified language and supplementing with more of your knowledge of the language might yield better results. E.g. while the AI can’t write better Rust on its own, I don’t mind contributing with Rust code myself more often.
One of the many pitfalls with using an llm to write code. It's very easy to find yourself with a codebase you know nothing about that you can't progress any further because it keeps breaking.
I could learn about react and understand the large-scale incongruences / mismatching choices the LLM made for me.
But I already have one reactive framework in my wetware that I can have an educated opinion on.
So why would they be able to "read" the docs and use that knowledge except up to pattern matching level. That's why I also assume, that tons of examples with results would do better than lang docs, but I haven't tested it yet.
So while they cant learn arithmetic they should be able to learn programming languages given that they are way closer to what it was designed and trained for.
https://github.com/paralleldrive/sudolang-llm-support
https://ben.terhech.de/posts/2025-01-31-llms-vs-programming-... — take-away: output languages like Python and TypeScript fare better, as I’d expect.
Maybe the blog post implies: why make a language the LLMs have zero examples of, and thus can’t synthesize?
I’d still make a language for the heck of it, because programming as a recreational human activity is great.
LLMs are autoencoders for sequences. If an LLM can write the code, the entropy value of that code is low. We know that already, most human communication is low entropy, but the LLMs being good at it implies there is a more efficient structure we could be using. All the embeddings are artifacts of structure, but the entire ANN model obfuscates structures it encodes.
Clearly there are better programming languages, closer fit to our actual intents, than the existing ones. The LLM will never show them to us, we need to go make/find them ourselves.
It's like the absolute minimal threshold of demands for sloppy code to work without immediately falling apart.
dustingetz•6mo ago
“The value of notation lies in how it enables us to work with new abstractions. With more powerful notation, we can work with ideas that would have been too complex or unwieldy without it. Equipped with better notation, we might think of solutions or hypotheses that would have been previously unthinkable. Without Arabic numerals, we don’t have long division. Without chess notation, the best strategies and openings may not have been played. Without a notation for juggling patterns called Siteswap, many new juggling patterns wouldn’t have been invented. I think notation should be judged by its ability to contribute to and represent previously unthinkable, un-expressible thoughts.”
This is pretty much the whole point of programming languages imo
middayc•6mo ago