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Comment by v3ss0n | Hacker News Reader

With LLM do we actually need new programming languages?

Even though this just showed up on HN (for the 10th time?) the Lobster project started in ~2010. No LLMs on the horizon back then.

And while Lobster is a niche language LLMs don't know as well, they do surprisingly well coding in it, especially in the context of a larger codebase as context. It occasionally slips in Python-isms but nothing that can't be fixed easily.

Not suitable for larger autonomous coding projects, though.

Yes.

In fact, LLMs have shown that we really, really need new programming languages.

1. They have shown that the information density in our existing languages is extremely low: small prompts can generate very large programs.

2. But the only way to get that high information density now (with LLMs) is to give up any hope of predictability. I want both.

Small prompts leading to large programs has absolutely nothing to do with programming languages and everything to do with the design of the word generators used to produce the programs — which ingest millions of programs in training and can spit out almost entire examples based on them.

APL?

> They have shown that the information density in our existing languages is extremely low: small prompts can generate very large programs.

"Write a book about a small person who happens upon a magical ring which turns out to be the repository of an evil entities power. The small person needs to destroy the ring somehow, probably using the same means it was created"

...wait a few minutes...

THE LORD OF THE RINGS

http://lotrproject.com/statistics/books/wordscount

Sadly, probably not. I fear new languages will struggle from here on out. As a language guy, very few things in this new AI world make me more sad than this.

I don't get the feeling this will happen. LLMs are extremely good at learning new languages because that's basically their whole point. If your new language has a standard library, and the LLM can see its source code, I am sure you can give it to any last-generation AI and it will happily spit out perfectly correct new code in it. If you give it access to a reference docs, then it can even ensure it never generates syntactically incorrect code quite well. As long as your error messages are enough to understand what a problem's root cause is, the LLM will iterate and explore until it gets it right.

Not sure if this is a good example, but I used ChatGPT (not even Codex) to fix some Common Lisp code for me, and it absolutely nailed it. Sure, Common Lisp has been around for a long time, but there's not so much Common Lisp code around for LLMs to train on... but OTOH it has a hyperspec which defines the language and much of the standard libraries so I believe the LLM can produce perfect Common Lisp based on mostly that.

I think it would be cool if a language specifically for LLMs came about. It should have something like required preconditions and postconditions so that a deterministic compiler can verify the assumptions the LLM is claiming. Something like a theorem prover, but targeted specifically for programming and efficient compilation/runtime. And it doesn't need all the niceties human programmers tend to prefer (implicit conversions comes to mind).

I'm working on this now.

It's a Profile Guided Optimization language - with memory safety like Rust.

It's extremely easy to optimize assuming you either 1) profile it in production (obviously has costs) or 2) can generate realistic workloads to test against.

It's like Rust, in that it makes expressing common illegal states just outright impossible. Though it goes much further than Rust.

And it's easier to read than Swift or Go.

There's a lot of magic that happens with defaults that languages like Zig or Rust don't want, because they want every cost signal to be as visible as possible, so you can understand the cost of a line and a function.

LLMs with tests can - I hope - do this without that noise.

We shall see.

Do you have a repo?

Yes.

I'm almost ready to launch v0.1 - but the documentation is especially a mess right now, so I don't want to share yet.

I'll update this comment in a week or so [=

Appreciate it!

If you're that confident in the LLM's output, just train it to output some kind of intermediate language, or even machine code.

And if you're not that confident, shouldn't you still be optimising for humans, because humans have to check the LLM's output?

At least in programming, humans have to check the product of the LLM's output rather than the output itself.

Possibly. First, I think there's still low hanging fruit in creating a programming language designed to be as easy as possible for agents to work with that we won't try to unlock until people writing code is a curiosity. Second, agents don't care about verbosity of code, so we can do verbosity/correctness/tooling tradeoffs that wouldn't have made sense when humans were the sole consumers of the code.

Yes, very much so. Programming languages are tools for thought, if you want your LLMs to think better, then they'll need better tools for thinking than the ones we have today. That they are mostly thinking in and writing Python is incidental to when they were born and a limitation of current AI technology, not its final evolution.

A more concise language is more efficient for an LLM to produce and easier for a human to verify.

I don't think LLMs have solved the problem of wanting code that's concise and also performant.

But a less concise language is (theoretically, if you're doing useful stuff with the verbosity) easier for machines to verify.

Yes, languages that talk to llms that is.

imho no

Novel programming languages have still educational value for those building them and, yes, we still need programming languages. I dont see any reason we would not need them. Even if Ai is going to write the code for you; how is it going to write it with no programming language? With raw binary? Absolutly not.

Eventually it wont need to write any code at all. The end goal for AI is "The Final Software" - no more software needs to be written, you just tell the AI what you actually want done and it does it, no need for it to generate a program.

But how do you know AI can generate programs without writing code? It can't today -- in fact the best thinking models work by writing code as part of the process. Natural intelligence requires it as well, as all our jobs are about expressing problem domains formally. So why would we expect an artificial intelligence should be able to reason about the kinds of programming problems we want them to without a formal language?

Maybe they will not be called programming languages anymore because that name was mostly incidental to the fact they were used to write programs. But formal, constructed languages are very much still needed because without them, you will struggle with abstraction, specification, setting constraints and boundaries, etc. if all you use is natural language. We invented these things for a reason!

Also the AI will have to communicate output to you, and you'll have to be sure of what it means, which is hard to do with natural language. Thus you'll still have to know how to read some form of code -- unless you're willing to be fooled by the AI through its imprecise use of natural language.

how did you get that 'no programming language' conclusion? there are so many well established languages that are more than we need already, the market has picked the winners too, and AI has well trained with them, these are facts. If there is a new language needed down the road for AI coders, most likely it will be created by using AI itself. for the moment, human created niche language is too late for the party, move on.

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