Impressive. As a practical matter, one wonders what th point would be in creating a new programming languages if the programmer no longer has to write or read code.
Programming languages are after all the interface that a human uses to give instructions to a computer. If you’re not writing or reading it, the language, by definition doesn’t matter.
There may actually be more value in creating specialized languages now, not less. Most new languages historically go nowhere because convincing human programmers to spend the time it would take to learn them is difficult, but every AI coding bot will learn your new language as a matter of course after its next update includes the contents of your website.
If there are millions of lines on github in your language.
Otherwise the 'teaching AI to write your language' part will occupy so much context and make it far less efficient that just using typescript.
The vast majority of tokens are not used for documentation or reference material but rather are for reasoning/thinking. Unless you somehow design a programming language that is just so drastically different than anything that currently exists, you can safely bet that LLMs will pick them up with relative ease.
You can do it today if you are willing to pay (API or on top of your subscription) [0]
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[0] https://code.claude.com/docs/en/model-config#extended-contex...
That's assuming that your new, very unknown language gets slurped up in the next training session which seems unlikely. Couldn't you use RAG or have an LLM read the docs for your language?
There are languages that are already pretty sparse with keywords. e.g in Go you can write 'func main() string', no need to define that it's public, or static etc. So combining a less verbose language with 'codegolfing' the variables might be enough.
Code readability is another, correlating one, but this is more subjective. To me go scores pretty low here - code flow would be readable were it not for the huge amount of noise you get from error "handling" (it is mostly just syntactic ceremony, often failing to properly handle the error case, and people are desensitized to these blocks so code review are more likely to miss these).
For function signatures, they made it terser - in my subjective opinion - at the expense of readability. There were two very mainstream schools of thought with relation to type signature syntax, `type ident` and `ident : type`. Go opted for a third one that is unfamiliar to both bases, while not even having the benefits of the second syntax (e.g. easy type syntax, subjective but that : helps the eye "pattern match" these expressions).
Claude seems more consistently _concise_ to me, both in web and cli versions. But who knows, after 12 months of stuff it could be me who is hallucinating...
Programming languages function in large parts as inductive biases for humans. They expose certain domain symmetries and guide the programmer towards certain patterns. They do the same for LLMs, but with current AI tech, unless you're standing up your own RL pipeline, you're not going to be able to get it to grok your new language as well as an existing one. Your chances are better asking it to understand a library.
How will it "learn" anything if the only available training data is on a single website?
LLMs struggle with following instructions when their training set is massive. The idea that they will be able to produce working software from just a language spec and a few examples is delusional. It's a fundamental misunderstanding of how these tools work. They don't understand anything. They generate patterns based on probabilities and fine tuning. Without massive amounts of data to skew the output towards a potentially correct result they're not much more useful than a lookup table.
I'm using Claude Code to work on something involving a declarative UI DSL that wraps a very imperative API. Its first pass at adding a new component required imperative management of that component's state. Without that implementation in context, I told Claude the imperative pattern "sucks" and asked for an improvement just to see how far that would get me.
A human developer familiar with the codebase would easily understand the problem and add some basic state management to the DSL's support for that component. I won't pretend Claude understood, but it matched the pattern and generated the result I wanted.
This does suggest to me that a language spec and a handful of samples is enough to get it to produce useful results.
I have done exactly the above with great success. I work with a weird proprietary esolang sometimes that I like, and the only documentation - or code - that exists for it is on my computer. I load that documentation in, and it works just fine and writes pretty decent code in my esolang.
"But that can't possibly work [based on my misunderstanding of how LLMs work]!" you say.
Well, it does, so clearly you misunderstand how they work.
Probably if you’re trying to be esoteric and arcane then yeah, you might have trouble, but that’s not normally how languages evolve.
The impact that lack of training data has on the quality of the results is easily observable. Try getting them to maintain a Python codebase vs. e.g. an Elixir one. Not just generate short snippets of code, but actually assist in maintaining it. You'll constantly run into basic issues like invalid syntax, missing references, use of nonexistent APIs, etc., not to mention more functional problems like dead, useless, or unnecessarily complicated code. I run into these things with mainstream languages (Go, Python, Clojure), so I don't see how an esolang could possibly fair any better.
But then again, the definitions of "just fine" and "decent" are subjective, and these tools are inherently unreliable, which is where I suspect the large disconnect in our experiences comes from.
On a different but related note, it's almost the same as pairing django or rails with an LLM. The framework allows you to trust that things like authentication and a passable code organization are being correctly handled.
I'm working on a language as well (hoping to debut by end of month), but the premise of the language is that it's designed like so:
1) It maximizes local reasoning and minimizes global complexity
2) It makes the vast majority of bugs / illegal states impossible to represent
3) It makes writing correct, concurrent code as maximally expressive as possible (where LLMs excel)
4) It maximizes optionality for performance increases (it's always just flipping option switches - mostly at the class and function input level, occassionaly at the instruction level)
The idea is that it should be as easy as possible for an LLM to write it (especially convert other languages to), and as easy as possible for you to understand it, while being almost as fast as absolutely perfect C code, and by virtue of the design of the language - at the human review phase you have minimal concerns of hidden gotcha bugs.
My language is a step ahead of Rust, but not as strict as Ada, while being easier to read than Swift (especially where concurrency is involved).
Roughly: machine code --> assembly --> C --> high-level languages --> frameworks --> visual tools --> LLM-assisted coding. Most of those transitions were controversial at the time, but in retrospect they mostly expanded the toolbox rather than replacing the lower layers.
One workflow I’ve found useful with LLMs is to treat them more like a code generator after the design phase. I first define the constraints, objects, actors, and flows of the system, then use structured prompts to generate or refine pieces of the implementation.
I'm being slightly facetious of course, I still use sequence diagrams and find them useful. The rest of its legacy though, not so much.
By what definition? It still matters if I write my app in Rust vs say Python because the Rust version still have better performance characteristics.
So yeah for some things we are already at the point of "I am not longer the coder, I am the architect".. and it's scary.
That is the part of the post that stuck with me, because I've also picked up impossible challenges and tried to get Claude to dig me out of a mess without giving up from very vague instructions[1].
The effect feels like the Loss-Disguised-As-Win feeling of the video-games I used to work on at Zynga.
Sure it made a mistake, but it is right there, you could go again.
Pull the lever, doesn't matter if the kids have Karate at 8 AM.
That said, the core value of the software wouldn't exist without a human at the helm. It requires someone to expend the energy to guide it, explore the problem space, and weave hundreds of micro-plans into a coherent, usable system. It's a symbiotic relationship, but the ownership is clear. It’s like building a house: I could build one with a butter knife given enough time, but I'd rather use power tools. The tools don't own the house.
At this point, LLMs aren't going to autonomously architect a 400+ table schema, network 100+ services together, and build the UI/UX/CLI to interface with it all. Maybe we'll get there one day, but right now, building software at this scale still requires us to drive. I believe the author owns the language.
Going into the vault!
I have yet to see a study showing something like a 2x or better boost in programmer productivity through LLMs. Usually it's something like 10-30%, depending on what metrics you use (which I don't doubt). Maybe it's 50% with frontier models, but seeing these comments on HN where people act like they're 10x more productive with these tools is strange.
Not according to the US Copyright Office. It is 100% LLM output, so it is not copyrighted, thus it's free for anyone to do anything with it and no claimed ownership or license can stop them.
It's possible to use AI output in human created content, and it can be copyrightable, and substantiative, transformative human-creative alteration of AI output is also copyrightable.
100% machine generated code is not copyrightable.
[1] https://newsroom.loc.gov/news/copyright-office-releases-part...
It’s missing all the heart, the soul, of deciding and trading off options to get something to work just for you. It’s like you bought a rat bike from your local junkyard and are trying to pass it off as your own handmade cafe racer.
Also you decide how much in control you are. Want to provide a hand made grammar? go ahead, want the agent to come up with it just from chatting and pointing it to other languages, ok too. Want to program just the first arithmetic operator yourself and then save the tedium of typing all the others so you can go to the next step? fine...
So you can have a huge toy language in mere days and experiment with stuff you'd have to build for months by hand to be able to play with.
Mine is an Io and Rebol inspired language that uses SQlite and Luajit as a runtime.
1.to 10 .map[n | n * n].each[n | n.say!]
It has not had any issues at all writing objc3 code
I've been trying a new approach I call CLI first. I realized CLI tools are designed to be used both by humans (command line) and machines (scripting), and are perfect for llms as they are text only interface.
Essentially instead of trying to get llm to generate a fully functioning UI app. You focus on building a local CLI tool first.
CLI tool is cheaper, simpler, but still has a real human UX that pure APIs don't.
You can get the llm to actually walk through the flows, and journeys like a real user end to end, and it will actually see the awkwardness or gaps in design.
Your commands structure will very roughly map to your resources or pages.
Once you are satisfied with the capability of the cli tool. (Which may actually be enough, or just local ui)
You can get it to build the remote storage, then the apis, finally the frontend.
All the while you can still tell it to use the cli to test through the flows and journeys, against real tasks that you have, and iterate on it.
I did recently for pulling some of my personal financial data and reporting it. And now I'm doing this for another TTS automation I've wanted for a while.
That said, it's a lot of words to say not a lot of things. Still a cool post, though!
I believe we're at a point where it's not possible to accurately decide whether text is completely written by human, by computer, or something in between.
If this blog post is unedited LLM output, the blog owner needs to sell whatever model, setup and/or prompt he used for a million dollars, since it's clearly far beyond the state-of-the-art in terms of natural-sounding tone.
I’ve never seen LLM being able to produce these kind of absurdist jokes. Or any jokes, really.
By all means, go read the post and then try to do so.
Black Mirror did it first https://en.wikipedia.org/wiki/Hang_the_DJ
I really liked that part - the house always wins.
However, I fear that agents will always work better on programming languages they have been heavily trained on, so for an agent-based development inventing a new domain specific language (e.g. for use internally in a company) might not be as efficient as using a generic programming language that models are already trained on and then just live with the extra boilerplate necessary.
I haven't read any farther than this, yet, but this made me stutter in my reading. Isn't a comparison just a function that takes two arguments and returns a third? How is that different from "+"?
fn read_float_literal(&mut self) -> &'a str {
let start = self.pos;
while let Some(ch) = self.peek_char() {
if ch.is_ascii_alphanumeric() || ch == '.' || ch == '+' || ch == '-' {
self.advance_char();
} else {
break;
}
}
&self.source[start..self.pos]
}
At the end of the day, I threw out all of the code the AI generated and wrote it myself, because the AI struggled to produce code that was functional to spec, much less code that would allow me to easily extend it to other kinds of future operators that I knew I would need in the future.
This is such an interesting statement to me in the context of leftpad.
This latest fever for LLMs simply confirms that people would rather do _anything_ other than program in a (not necessarily purely) functional language that has meta-programming facilities. I personally blame functional fixedness (psychological concept). In my experience, when someone learns to program in a particular paradigm or language, they are rarely able or willing to migrate to a different one (I know many people who refused to code in anything that did not look and feel like Java, until forced to by their growling bellies). The AI/LLM companies are basically (and perhaps unintentionally) treating that mental inertia as a business opportunity (which, in one way or another, it was for many decades and still is -- and will probably continue to be well into a post-AGI future).
I mean, they may be right but there is also a big opportunity for this being Gell-Mann amnesia : "The phenomenon of a person trusting newspapers for topics which that person is not knowledgeable about, despite recognizing the newspaper as being extremely inaccurate on certain topics which that person is knowledgeable about."
Who the hell is going to use it then? You certainly won't, because you're dependent on AI.
The "more on that later" was unit tests (also generated by Claude Code) and sample inputs and outputs (which is basically just unit tests by a different name).
This is... horrifically bad. It's stupidly easy to make unit tests pass with broken code, and even more stupidly easy when the test is also broken.
These "guardrails" are made of silly putty.
EDIT: Would downvoters care to share an explanation? Preferably one they thought of?
While I agree "AI is bad", well-written posts like this one can provide real insight into the process of using them, and reveal more about _why_ AI is bad.