> While working on Cutlet, though, I allowed Claude to generate every single line of code. I didn’t even read any of the code. Instead, I built guardrails to make sure it worked correctly (more on that later).
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.
The constraints enforced in the language still matter. A language which offers certain correctness guarantees may still be the most efficient way to build a particular piece of software even when it's a machine writing the code.
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.
Uh not really. I am already having Claude read and then one-shot proprietary ERP code written in vintage closed source language OOP oriented BASIC with sparse documentation.... just needed to feed it in the millions of lines of code i have and it works.
I'm sure claude does great at that, but it would be objectively better, for a large variety of reasons, if claude didn't have to keep syntax examples in it's context.
> 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.
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?
Neither RAG nor loading the docs into the context window would produce any effective results. Not even including the grammar files and just few examples in the training set would help. To get any usable results you still need many many usage examples.
My own 100% hallucinated language experiment is very very weird and still has thousands of lines of generated examples that work fine. When doing complex stuff you could see the agent bounce against the tests here and there, but never produced non-working code in the end. The only examples available were those it had generated itself as it made up the language.
It was capable of making things like a JSON parser/encoder, a TODO webapp or a command line kanban tracker for itself in one shot.
And yet it works well enough, regardless. I have a little project which defines a new DSL. The only documentation or examples which exist for this little language, anywhere in the world, are on my laptop. There is certainly nothing in any AI's training data about it. And yet: codex has no trouble reading my repo, understanding how my DSL works, and generating code written in this novel language.
In addition, I think token efficiency will continue to be a problem. So you could imagine very terse programming languages that are roughly readable for a human, but optimized to be read by LLMs.
That's an interesting idea. But IMO the real 'token saver' isn't in the language keywords but it's in the naming of things like variables, classes, etc.
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.
I'm not an expert in LLMs, but I don't think character length matters. Text is deterministically tokenized into byte sequences before being fed as context to the LLM, so in theory `mySuperLongVariableName` uses the same number of tokens as `a`. Happy to be corrected here.
Its not verbose to some of us. It is explicit in what it does, meaning I don't have to wonder if there's syntatic sugar hiding intent. Drastically more minimal than equivalent code in other languages.
I think there's a huge range here - ChatGPT to me seems extra verbose on the web version, but when running with Codex it seems extra terse.
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...
I would be very interested in this research... I'm trying to write a language that is simple and concise like Python, but fast and statically typed. My gutfeeling is that more concise than Python (J, K, or some code golfing language) is bad for readability, but so is the verbosity of Rust, Zig, Java.
Those constraints can be enforced by a library too. Even humans sometimes make a whole new language for something that can be a function library. If you want strong correctness guarantees, check the structure of the library calls.
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.
> every AI coding bot will learn your new language as a matter of course after its next update includes the contents of your website.
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.
They don't understand anything, but they sure can repeat a pattern.
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.
It's wild to me the disconnect between people who actually use these tools every day and people who don't.
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.
The reason it works so well is that everyone’s “personal unique language” really isn’t all that different from what’s been proposed before, and any semantic differences are probably not novel. If you make your language C + transactional memory, the LLM probably has enough information about both to reason about your code without having to be trained on a billion lines.
Probably if you’re trying to be esoteric and arcane then yeah, you might have trouble, but that’s not normally how languages evolve.
Like everything generated by LLMs though, it is built on the shoulders of giants - what will happen to software if no one is creating new programming languages anymore? Does that matter?
I think the only hope is that AGI arises and picks up where humanity left off. Otherwise I think this is the long dark teatime of human engineering of all sorts.
So you’re hoping for a blackbox uninspectable by humans? That to me sounds like a nightmare, a nightmare worse than all the cruft and stupid rules humanity accrued over time. Let’s hope the future tech is inspectable and understandable by humans.
In the 90s people hoped Unified Modeling Language diagrams would generate software automatically. That mostly didn’t happen. But large language models might actually be the realization of that old dream. Instead of formal diagrams, we describe the system in natural language and the model produces the code. It reminds me of the old debates around visual web tools vs hand-written HTML. There seems to be a recurring pattern: every step up the abstraction ladder creates tension between people who prefer the new layer and those who want to stay closer to the underlying mechanics.
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 agree with the sentiment but want to point out that the biggest drive behind UML was the enrichment of Rational Software and its founders. I doubt anyone ever succeeded in implementing anything useful with Rational Rose. But the Rational guys did have a phenomenal exit and that's probably the biggest success story of UML.
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.
I don’t agree with the idea that programming languages don’t have an impact of an LLM to write code. If anything, I imagine that, all else being equal, a language where the compiler enforces multiple levels of correctness would help the AI get to a goal faster.
A good example of this is Rust. Rust is by default memory safe when compared to say, C, at the expense of you having to be deliberate in managing memory. With LLMs this equation changes significantly because that harder/more verbose code is being written by the LLM, so it won't slow you down nearly as much. Even better, the LLM can interact with the compiler if something is not exactly as it should.
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.
That is why Typescript is the main one used by most people vibe coding, The LLMs do like to work around the type engine in it sometimes, but strong typing and linting can help a ton in it.
> 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.
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.
I agree with your questioning of it being capable of preventing bugs, but your second point is quite likely false -- we have developed a bunch of very useful abstractions in "research" languages 50 years ago, only to re-discover them today (no null, algebraic data types, pattern matching, etc).
Saves tokens. The main reason though is to manage performance for what techniques get used for specific use cases. In their case it seems to be about expressiveness in Bash.
I have been building a game via a separate game logic library and Unity (which includes that independent library).. let's just say that over the last couple weeks I have 100% lost the need to do the coding myself. I keep iterating and have it improve and there are hundreds of unit tests.. I have a Unity MCP and it does 95% of the Unity work for me. Of course the real game will need custom designing and all that; but in terms of getting a complete prototype setup.... I am literally no longer the coder. I just did in a week what it would have taken me months and months and months to do. Granted Unity is still somewhat new to, but still.. even if you are an expert- it can immediately look at all your game objects and detect issues etc.
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.
In principle (and we hope in practice) the person is still responsible for the consequences of running the code and so it remains important they can read and understand what has been generated.
I've been working on a large codebase that was already significant before LLM-assisted programming, leveraging code I’d written over a decade ago. Since integrating Claude and Codex, the system has evolved and grown massively. Realistically, there’s a lot in there now that I simply couldn't have built in a standard human lifetime without them.
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.
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.
This is the take, very well said. I've been trying to use analogies with cars and cabinet making, but building a house is just right for the scale and complexity of the efforts enabled, and the ownership idea threads into it well.
This takes all the satisfaction out of spending a few well thought out weekends to build your own language. So many fun options: compiled or interpreted; virtual machine, or not; single pass, double pass, or (Leeloo Dallas) Multipass? No cool BNF grammars to show off either…
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.
One topic of llms not doing well with UI and visuals.
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.
> More addictive than that is the unpredictability and randomness inherent to these tools. If you throw a problem at Claude, you can never tell what it will come up with. It could one-shot a difficult problem you’ve been stuck on for weeks, or it could make a huge mess. Just like a slot machine, you can never tell what might happen. That creates a strong urge to try using it for everything all the time.
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.
Why not let Claude do our dating? I'm surprised someone hasn't thought of this: AI dating, let the AI find and qualify a date for you, and match with the person who meets you, for you!
I am kind of doing that now. I put Kimi K2.5 into a Ralph Loop to make a Screeps.com AI. So far its been awful at it. If you want to track its progress, I have its dashboard at https://balsa.info
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.
You can make an LLM sound very natural if you simply ask for it and provide enough text in the tone you’d like it to reproduce. Otherwise, it’s obvious that an LLM with no additional context will try to stick to the tone the company aligned it to produce
> The @ meta operator also works with comparisons.
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 "+"?
Not to discount your experience, but I dont understand what's interesting about this. You could always build a programming language yourself, given enough time. Programming languages' constructs are well represented in the training dataset. I want someone to build something uniquely novel that's not actually in the dataset and i'll be impressed by CC.
I have been trying this as well, and you can quickly come very far.
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 think we're going to see a lot more of this. I've done a similar thing, hosting a toy language on haskell, and it was remarkably easy to get something useful and usable, in basically a weekend. If you keep the surface area small enough you can now make a fully fledged, compiled language for basically every single purpose you'd like, and coevolve the language, the code, and the compiler
Yeah it's a rewarding project. Getting a language that kinda works is surprisingly accessible. Though we must be mindful that this is still the "draw some circles" pane. Producing the rest of the rest of the famous owl is, as always, the hard bit.
I'd say these times will be filled with a lot of tailored-to-you "self"-made software, but the question is, are we increasing amount of information in the world? I heard that claude and chatgpt are getting good at mathematical proofs which give really something to our knowledge, but all other things are neutral to entropy, if not decreasing. Strange time to live in, strange valuations and devaluations...
The AI age is calling for a language that is append-only, so we can write in a literate programming style and mix prompts with AI output, in a linear way.
Curious how you handled context management as the project grew — did you end up with a single CLAUDE.md or something more structured? I've been thinking about this problem and working on a standard for it.
Does this really test Claude in a useful way? Is building a highly derivative programming language a useful use case? Claude has probably indexed all existing implementations of imperative dynamic languages and is basically spewing slop based on that vibe. Rather than super flexible, super unsafe languages, we need languages with guardrails, restrictions and expressive types, now more than ever. Maybe LLMs could help with that? I'm not sure, it would certainly need guidance from a human expert at every step.
> I’ve also been able to radically reduce my dependency on third-party libraries in my JavaScript and Python projects. I often use LLMs to generate small utility functions that previously required pulling in dependencies from NPM or PyPI.
This is such an interesting statement to me in the context of leftpad.
Also, neither over the wire dependency issues or code injection issues (the two major criticisms) are solved by using an llm to produce the code. Talk about shifting complexity. It would be better if every LSP had a general utility library generator built in.
Admittedly I only skimmed this, but I found it interesting that they came to the conclusion that Claude is really bad at (thing they know how to do, and therefore judge ) and really good at (thing they don't know how to do or judge).
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."
I recently tried using Claude to generate a lexer and parser for a language i was designing. As part of its first attempt, this was the code to parse a float literal:
Admittedly, I do have a very idiosyncratic definition of floating-point literal for my language (I have a variety of syntaxes for NaNs with payloads), but... that is not a usable definition of float literal.
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.
I had a somewhat experience with Claude coding an Occam parser but I just let it do it's thing and once I had presented it with a suitable suite of test source code, it course corrected, refactored and ended up with a reasonable solution. The journey was a bit different to an experienced human developer but the results much the same and perhaps 100X cheaper.
Some of the issues are undoubtedly that I have a decidedly non-standard architecture for my system that the AI refuses to acknowledge--it hallucinated things like integers, which isn't a part of my system, simply because what I have looks almost like a standard example expression grammar so clearly I must have all of the standard example expression grammar things. (This is a pretty common failure mode I've noticed in AI-based systems--when the thing you're looking for is very similar to a very notable, popular thing, AI systems tend to assume you mean the latter as opposed to the former.)
This is not exactly novel. In the 2000s, someone made a fully functioning Perl 6 runtime in a very short amount of time (a month, IIRC) using Haskell. The various Lisps/Schemes have always given you the ability to implement specialized languages even more quickly and ergonomically than Haskell (IMHO).
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).
> While working on Cutlet, though, I allowed Claude to generate every single line of code. I didn’t even read any of the code. Instead, I built guardrails to make sure it worked correctly (more on that later).
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 working on Cutlet, though, I allowed Claude to generate every single line of code. I didn’t even read any of the code. Instead, I built guardrails to make sure it worked correctly (more on that later).
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.
The constraints enforced in the language still matter. A language which offers certain correctness guarantees may still be the most efficient way to build a particular piece of software even when it's a machine writing the code.
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.
> every AI coding bot will learn your new language
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.
Uh not really. I am already having Claude read and then one-shot proprietary ERP code written in vintage closed source language OOP oriented BASIC with sparse documentation.... just needed to feed it in the millions of lines of code i have and it works.
I'm sure claude does great at that, but it would be objectively better, for a large variety of reasons, if claude didn't have to keep syntax examples in it's context.
"i haven't been able to find much" != "there isn't much on the entire internet fed into them"
> 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.
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?
Agreed - unpopular languages and packages have pretty shaky outcomes with code generation, even ones that have been around since before 2023.
Neither RAG nor loading the docs into the context window would produce any effective results. Not even including the grammar files and just few examples in the training set would help. To get any usable results you still need many many usage examples.
My own 100% hallucinated language experiment is very very weird and still has thousands of lines of generated examples that work fine. When doing complex stuff you could see the agent bounce against the tests here and there, but never produced non-working code in the end. The only examples available were those it had generated itself as it made up the language. It was capable of making things like a JSON parser/encoder, a TODO webapp or a command line kanban tracker for itself in one shot.
And yet it works well enough, regardless. I have a little project which defines a new DSL. The only documentation or examples which exist for this little language, anywhere in the world, are on my laptop. There is certainly nothing in any AI's training data about it. And yet: codex has no trouble reading my repo, understanding how my DSL works, and generating code written in this novel language.
In addition, I think token efficiency will continue to be a problem. So you could imagine very terse programming languages that are roughly readable for a human, but optimized to be read by LLMs.
That's an interesting idea. But IMO the real 'token saver' isn't in the language keywords but it's in the naming of things like variables, classes, etc.
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.
I'm not an expert in LLMs, but I don't think character length matters. Text is deterministically tokenized into byte sequences before being fed as context to the LLM, so in theory `mySuperLongVariableName` uses the same number of tokens as `a`. Happy to be corrected here.
Go is one of the most verbose mainstream programming languages, so that's a pretty terrible example.
Maybe not a perfect example but it’s more lightweight than Java at least haha
If by lightweight you mean verbosity, then absolutely no.
In go every third line is a noisy if err check.
To you maybe, but Go is running a large amount of internet infrastructure today.
How does that relate to Go being a verbose language?
Its not verbose to some of us. It is explicit in what it does, meaning I don't have to wonder if there's syntatic sugar hiding intent. Drastically more minimal than equivalent code in other languages.
Well LLMs are made to be extremely verbose so it's a good match!
I think there's a huge range here - ChatGPT to me seems extra verbose on the web version, but when running with Codex it seems extra terse.
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...
I think I remember seeing research right here on HN that terse languages don't actually help all that much
I would be very interested in this research... I'm trying to write a language that is simple and concise like Python, but fast and statically typed. My gutfeeling is that more concise than Python (J, K, or some code golfing language) is bad for readability, but so is the verbosity of Rust, Zig, Java.
Those constraints can be enforced by a library too. Even humans sometimes make a whole new language for something that can be a function library. If you want strong correctness guarantees, check the structure of the library calls.
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.
> every AI coding bot will learn your new language as a matter of course after its next update includes the contents of your website.
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.
They don't understand anything, but they sure can repeat a pattern.
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.
It's wild to me the disconnect between people who actually use these tools every day and people who don't.
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.
The reason it works so well is that everyone’s “personal unique language” really isn’t all that different from what’s been proposed before, and any semantic differences are probably not novel. If you make your language C + transactional memory, the LLM probably has enough information about both to reason about your code without having to be trained on a billion lines.
Probably if you’re trying to be esoteric and arcane then yeah, you might have trouble, but that’s not normally how languages evolve.
No, mine's a esoteric declarative data description/transform language. It's pretty damn weird.
Like everything generated by LLMs though, it is built on the shoulders of giants - what will happen to software if no one is creating new programming languages anymore? Does that matter?
I think the only hope is that AGI arises and picks up where humanity left off. Otherwise I think this is the long dark teatime of human engineering of all sorts.
So you’re hoping for a blackbox uninspectable by humans? That to me sounds like a nightmare, a nightmare worse than all the cruft and stupid rules humanity accrued over time. Let’s hope the future tech is inspectable and understandable by humans.
In the 90s people hoped Unified Modeling Language diagrams would generate software automatically. That mostly didn’t happen. But large language models might actually be the realization of that old dream. Instead of formal diagrams, we describe the system in natural language and the model produces the code. It reminds me of the old debates around visual web tools vs hand-written HTML. There seems to be a recurring pattern: every step up the abstraction ladder creates tension between people who prefer the new layer and those who want to stay closer to the underlying mechanics.
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 agree with the sentiment but want to point out that the biggest drive behind UML was the enrichment of Rational Software and its founders. I doubt anyone ever succeeded in implementing anything useful with Rational Rose. But the Rational guys did have a phenomenal exit and that's probably the biggest success story of UML.
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.
I don’t agree with the idea that programming languages don’t have an impact of an LLM to write code. If anything, I imagine that, all else being equal, a language where the compiler enforces multiple levels of correctness would help the AI get to a goal faster.
A good example of this is Rust. Rust is by default memory safe when compared to say, C, at the expense of you having to be deliberate in managing memory. With LLMs this equation changes significantly because that harder/more verbose code is being written by the LLM, so it won't slow you down nearly as much. Even better, the LLM can interact with the compiler if something is not exactly as it should.
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.
That is why Typescript is the main one used by most people vibe coding, The LLMs do like to work around the type engine in it sometimes, but strong typing and linting can help a ton in it.
> 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.
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.
How does a programming language prevent the vast majority of bugs? I feel like we would all be using that language!
See Rust with Use-after-Free, fearless concurrency, etc.
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).
I agree with your questioning of it being capable of preventing bugs, but your second point is quite likely false -- we have developed a bunch of very useful abstractions in "research" languages 50 years ago, only to re-discover them today (no null, algebraic data types, pattern matching, etc).
Saves tokens. The main reason though is to manage performance for what techniques get used for specific use cases. In their case it seems to be about expressiveness in Bash.
I have been building a game via a separate game logic library and Unity (which includes that independent library).. let's just say that over the last couple weeks I have 100% lost the need to do the coding myself. I keep iterating and have it improve and there are hundreds of unit tests.. I have a Unity MCP and it does 95% of the Unity work for me. Of course the real game will need custom designing and all that; but in terms of getting a complete prototype setup.... I am literally no longer the coder. I just did in a week what it would have taken me months and months and months to do. Granted Unity is still somewhat new to, but still.. even if you are an expert- it can immediately look at all your game objects and detect issues etc.
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.
> If you’re not writing or reading it, the language, by definition doesn’t matter.
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.
In principle (and we hope in practice) the person is still responsible for the consequences of running the code and so it remains important they can read and understand what has been generated.
I've been wondering if a diffusion model could just generate software as binary that could be fed directly into memory.
Yeah, what could go wrong.
I've been working on a large codebase that was already significant before LLM-assisted programming, leveraging code I’d written over a decade ago. Since integrating Claude and Codex, the system has evolved and grown massively. Realistically, there’s a lot in there now that I simply couldn't have built in a standard human lifetime without them.
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.
> I believe the author owns the language.
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.
This is the take, very well said. I've been trying to use analogies with cars and cabinet making, but building a house is just right for the scale and complexity of the efforts enabled, and the ownership idea threads into it well.
Going into the vault!
This takes all the satisfaction out of spending a few well thought out weekends to build your own language. So many fun options: compiled or interpreted; virtual machine, or not; single pass, double pass, or (Leeloo Dallas) Multipass? No cool BNF grammars to show off either…
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.
One topic of llms not doing well with UI and visuals.
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.
Claude Code built a programming language using you
> More addictive than that is the unpredictability and randomness inherent to these tools. If you throw a problem at Claude, you can never tell what it will come up with. It could one-shot a difficult problem you’ve been stuck on for weeks, or it could make a huge mess. Just like a slot machine, you can never tell what might happen. That creates a strong urge to try using it for everything all the time.
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.
[1] - https://github.com/t3rmin4t0r/magic-partitioning
Next you can let Claude play your video games for you as well. Gads we are a voyeuristic society aren’t we.
Why not let Claude do our dating? I'm surprised someone hasn't thought of this: AI dating, let the AI find and qualify a date for you, and match with the person who meets you, for you!
I suspect this is going to be an iteration of the Simpsons meme soon, but...
Black Mirror did it first https://en.wikipedia.org/wiki/Hang_the_DJ
Here's Claude playing Detroit: Become Human https://www.youtube.com/watch?v=Mcr7G1Cuzwk
I am kind of doing that now. I put Kimi K2.5 into a Ralph Loop to make a Screeps.com AI. So far its been awful at it. If you want to track its progress, I have its dashboard at https://balsa.info
Honestly some of the most fun I had playing Ultima Online was writing scripts to play it for me.
AI written code with a human writted blog post, that's a big step up.
That said, it's a lot of words to say not a lot of things. Still a cool post, though!
> with a human writted blog post
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.
We're definitely not at that point.
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.
You can make an LLM sound very natural if you simply ask for it and provide enough text in the tone you’d like it to reproduce. Otherwise, it’s obvious that an LLM with no additional context will try to stick to the tone the company aligned it to produce
”I named it Cutlet after my cat. It’s completely legal to do that.”
I’ve never seen LLM being able to produce these kind of absurdist jokes. Or any jokes, really.
comedy is a completely different thing than natural tone. I agree that they’re incapable of coming up with decent jokes
Agree. I've been yearning for more insightful posts and there's just not alot of them out there these days
> The @ meta operator also works with comparisons.
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 "+"?
Not to discount your experience, but I dont understand what's interesting about this. You could always build a programming language yourself, given enough time. Programming languages' constructs are well represented in the training dataset. I want someone to build something uniquely novel that's not actually in the dataset and i'll be impressed by CC.
I have been trying this as well, and you can quickly come very far.
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.
Using LLMs to invent new programming languages is a mystery to me. Who or what is going to use this? Presumably not the author.
AI generate some feedback, then just move onto the next project, and repeat.
I think we're going to see a lot more of this. I've done a similar thing, hosting a toy language on haskell, and it was remarkably easy to get something useful and usable, in basically a weekend. If you keep the surface area small enough you can now make a fully fledged, compiled language for basically every single purpose you'd like, and coevolve the language, the code, and the compiler
Yeah it's a rewarding project. Getting a language that kinda works is surprisingly accessible. Though we must be mindful that this is still the "draw some circles" pane. Producing the rest of the rest of the famous owl is, as always, the hard bit.
We did this in 4th year comp-sci.
I'd say these times will be filled with a lot of tailored-to-you "self"-made software, but the question is, are we increasing amount of information in the world? I heard that claude and chatgpt are getting good at mathematical proofs which give really something to our knowledge, but all other things are neutral to entropy, if not decreasing. Strange time to live in, strange valuations and devaluations...
The AI age is calling for a language that is append-only, so we can write in a literate programming style and mix prompts with AI output, in a linear way.
That’s git commits.
That's arguably not very ergonomic, which is probably the biggest requirement for a programming language.
Why care about ergonomics if you're not going to write the code?
or css
A REPL + immutability?
It’s been a while friend
Congratulations on getting to the front page ;)
Curious how you handled context management as the project grew — did you end up with a single CLAUDE.md or something more structured? I've been thinking about this problem and working on a standard for it.
Does this really test Claude in a useful way? Is building a highly derivative programming language a useful use case? Claude has probably indexed all existing implementations of imperative dynamic languages and is basically spewing slop based on that vibe. Rather than super flexible, super unsafe languages, we need languages with guardrails, restrictions and expressive types, now more than ever. Maybe LLMs could help with that? I'm not sure, it would certainly need guidance from a human expert at every step.
> I’ve also been able to radically reduce my dependency on third-party libraries in my JavaScript and Python projects. I often use LLMs to generate small utility functions that previously required pulling in dependencies from NPM or PyPI.
This is such an interesting statement to me in the context of leftpad.
I'm imagining the amount of energy required to power the datacenter so that we can produce isEven() utility methods.
Also, neither over the wire dependency issues or code injection issues (the two major criticisms) are solved by using an llm to produce the code. Talk about shifting complexity. It would be better if every LSP had a general utility library generator built in.
we need a caching layer
Admittedly I only skimmed this, but I found it interesting that they came to the conclusion that Claude is really bad at (thing they know how to do, and therefore judge ) and really good at (thing they don't know how to do or judge).
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."
I had the exact same thoughts reading it.
I rolled a fair dice using ChatGPT.
I recently tried using Claude to generate a lexer and parser for a language i was designing. As part of its first attempt, this was the code to parse a float literal:
Admittedly, I do have a very idiosyncratic definition of floating-point literal for my language (I have a variety of syntaxes for NaNs with payloads), but... that is not a usable definition of float literal.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.
I had a somewhat experience with Claude coding an Occam parser but I just let it do it's thing and once I had presented it with a suitable suite of test source code, it course corrected, refactored and ended up with a reasonable solution. The journey was a bit different to an experienced human developer but the results much the same and perhaps 100X cheaper.
Some of the issues are undoubtedly that I have a decidedly non-standard architecture for my system that the AI refuses to acknowledge--it hallucinated things like integers, which isn't a part of my system, simply because what I have looks almost like a standard example expression grammar so clearly I must have all of the standard example expression grammar things. (This is a pretty common failure mode I've noticed in AI-based systems--when the thing you're looking for is very similar to a very notable, popular thing, AI systems tend to assume you mean the latter as opposed to the former.)
"Just one more prompt..." I can relate. who else has been affected by this?
Now anyone can be a Larry Wall, and I'm not sure that's a good thing.
This is not exactly novel. In the 2000s, someone made a fully functioning Perl 6 runtime in a very short amount of time (a month, IIRC) using Haskell. The various Lisps/Schemes have always given you the ability to implement specialized languages even more quickly and ergonomically than Haskell (IMHO).
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).
That was step #1.
Step #2 is: get real people to use it!
Nope. You didn't write it. You plagiarized it. AI is bad
Wait. You built a new language, that there's thus no training data for.
Who the hell is going to use it then? You certainly won't, because you're dependent on AI.
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."
https://news.ycombinator.com/newsguidelines.html
it's a valid question and one that everyone should be asking, unless ofcourse it's for fun which is what I believe this is.
It isn’t shallow.
Who’s going to use it?
With clear examples in their context they don't need training data.
> While working on Cutlet, though, I allowed Claude to generate every single line of code. I didn’t even read any of the code. Instead, I built guardrails to make sure it worked correctly (more on that later).
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?