I have a hypothesis that an LLM can act as a pseudocode to code translator, where the pseudocode can tolerate a mixture of code-like and natural language specification. The benefit being that it formalizes the human as the specifier (which must be done anyway) and the llm as the code writer. This also might enable lower resource “non-frontier” models to be more useful. Additionally, it allows tolerance to syntax mistakes or in the worst case, natural language if needed.
In other words, I think llms don’t need new languages, we do.
Thats again programming languages. Real issue with LLMs now is it doesn't matter if it can generate code quickly. Some one still has to read, verify and test it.
Perhaps we need a need a terse programming language. Which can be read quickly and verified. You could call that specification.
The programming language can look more like code in parts where the specification needs to be very detailed. I think people can get intuition about where the LLM is unlikely to be successful. It can have low detail for boilerplate or code that is simple to describe.
You should be able to alter and recompile the specification, unlike the wandering prompt which makes changes faster than normal version control practices keep up with.
Perhaps there's a world where reading the specification rather than the compiled code is sufficient in order to keep cognitive load at reasonable levels.
At very least, you can read compiled code until you can establish your own validation set and create statistical expectations about your domain. Principally, these models will always be statistical in nature. So we probably need to start operating more inside that kind of framework if we really want to be professional about it.
- LLMs can act as pseudocode to code translators (they are excellent at this)
- LLMs still create bugs and make errors, and a reasonable hypothesis is at a rate in direct proportion to the "complexity" or "buggedness" of the underlying language.
In other words, give an AI a footgun and it will happily use it unawares. That doesn't mean however it can't rapidly turn your pseudocode into code.
None of this means that LLMs can magically correct your pseudocode at all times if your logic is vastly wrong for your goal, but I do believe they'll benefit immensely from new languages that reduce the kind of bugs they make.
This is the moment we can create these languages. Because LLMs can optimize for things that humans can't, so it seems possible to design new languages to reduce bugs in ways that work for LLMs, but are less effective for people (due to syntax, ergonomics, verbosity, anything else).
This is crucially important. Why? Because 99% of all code written in the next two decades will be written by AI. And we will also produce 100x more code than has ever been written before (because the cost of doing it, has dropped essentially to zero). This means that, short of some revolutions in language technology, the number of bugs and vulnerabilities we can expect will also 100x.
That's why ideas like this are needed.
I believe in this too and am working on something also targeting LLMs specifically, and have been working on it since Mid to Late November last year. A business model will make such a language sustainable.
That is, in the same way that event sourcing materializes a state from a series of change events, this language needs to materialize a codebase from a series of "modification instructions". Different models may materialize a different codebase using the same series of instructions (like compilers), or say different "environmental factors" (e.g. the database or cloud provider that's available). It's as if the codebase itself is no longer the important artifact, the sequence of prompts is. You would also use this sequence of prompts to generate a testing suite completely independent of the codebase.
More terse the better.
We didn't end up with Lean and Rust, for a lack of understanding in how to create strong specifications. Pascal-like languages fell out of favour, despite having higher readability.
Jonathan Edwards (Subtext lang) has a lot of great research on this.
It sounds like your plan is for it to write fewer bugs in NewLang, but, well, that seems a bit hard to achieve in the abstract. From bugs I've fixed in generated code, early LLM, it was just bad code. Multiple variables for the same thing, especially. Recently they've gotten better at that, but it still happens.
For a concrete example, any app dealing with points in time. Which sometimes have a date attached but sometimes do not. And also, what are timezones. The complexity is there because it depends on what you're trying to do. An alarm clock is different than a calendar is different than a pomodoro timer. How are you going to reduce the bugged-ed-ness of that without making one of those use cases more complicated than need be, given access to various primitives.
This is something that could be distilled from some industries like aviation, where specification of software (requirements, architecture documents, etc.) is even more important that the software itself.
The problem is that natural language is in itself ambiguous, and people don't really grasp the importance of clear specification (how many times I have repeated to put units and tolerances to any limits they specify by requirements).
Another problem is: natural language doesn't have "defaults": if you don't specify something, is open to interpretation. And people _will_ interpret something instead of saying "yep I don't know this".
Back in the day, JetBrains tried revision-controlling AST trees or psi-nodes in their parlance. That project was cancelled, as it became a research challenge. That was 10 years ago or so. At this point, things may work out well, time will tell.
Or, maybe my lanng just had LLM-easy syntax - which would be good - but I think this is more just par for the course for LLMs, bud.
I think you're right within limits but the issue is semantics and obscure features. If the language differs from existing languages in only trivial ways, then LLMs can pick it up quickly. But then the value of such a language is trivial. If you deviate in bigger ways, it's harder to properly use just based on pre-existing code.
Here's a simple case study: Kotlin is semantically Java with a more concise syntax, but part of what makes it more concise is the Kotlin standard library adds a lot of utility methods to Java. Many utility methods are only needed rarely. LLMs can write competent Kotlin because they read the user guide and saw millions of examples in their training set, but if they were trying to learn exclusively from small examples in their context window, they wouldn't know about those obscure utilities and would never use them. Much of the benefit would be lost.
Given this, I see a few ways forward:
1. Just give up on designing new programming languages. Languages are user interfaces but the user is now an LLM with near infinite patience, so who cares if they aren't ideal. If the LLM has to brute force a utility method every single time instead of using a standard library... ok. Whatever. This would parallel what happened with CPU ISAs. There are very few of them today, they don't matter much and they're designed in ways that only machines can handle all the details, because everyone codes to higher level languages and compilers write all the assembly.
2. Define new languages as a delta on top of some well known initial language, ensuring that the language definition always fits inside a prompt as a skill. In this world we don't bother with new syntaxes anymore unless that syntax change encodes significant new semantics, because it's not worth wasting tokens showing the LLM what to do. Everything is just an extension to Python, in this world. The line between new languages and new libraries becomes increasingly blurred as runtimes get more powerful and flexible.
3. New languages have to come with their own fine tuned and hosted coding LLM. Maybe that's even a way to monetize new language creation.
4. The big model firms offer a service where you can pay to get your data into the training set. Then you use the giant prompt+delta mechanism to get an LLM to generate a textbook of sample code, pay to get it into the training set, wait six months for another foundation model run and then your language becomes usable.
Of these I think (2) is currently the most practical.
There's likely challenges here, but it's not the ones you're seeing so far.
Many of our traditional functional languages, ML family in particular, let you write hyper concise expressions (pure math if you’re in to that sort of thing), craft DSLs of unlimited specifiable power (‘makeTpsReportWith “new cover page format”’), and also in natural language (function names like `emptied cart should have zero items`).
I think if we did that and leveraged the type systems of those languages and the systematic improvements we see from ADTs and pattern matching in those languages, combined with a specification first approach like TDD, that we’d have a great starting point to have an LLM generate the rest of the system perfectly.
… yes, that is just writing Haskell/OCaml/F# with extra steps.
… yes, that level of specification is also the point with those languages where your exploratory type-diddling suddenly goes ‘presto’ and you magically have a fully functioning system.
I guess I’m old-fashioned, but sometimes I wonder if compilers are good for what they’re good for.
This is literally what software developers are actually paid to do. They are not paid to write code. This is reinventing software development.
But seriously, llms can transmit ideas to each other through English that we do understand, we are screwed if it’s another language lol
p.s. a combination of the above fares very well during my agentic coding adventures.
And then we can look at multiple LLM-generated implementations to inform how the prompt might need to be updated further until it's a one-shot.
Now you have perfect intention behind code, and you can refine the intention if it's wrong.
Languages don't exist in isolation, they exist on a continuum. Your brand new language isn't brand new, it's built off the semantics and syntax of many languages that have come before it. Most language designers operate under what is known as a "weirdness budget", which is about keeping your language to within some delta of other languages modulo a small number of new concepts. This is to maintain comprehensibility, otherwise you get projects like Hoon / Nock where true is false and up is down that no one can figure out.
Under a small weirdness budget, an LLM should be able to understand your new language despite not being trained on it. if you just explain what's different about it. I've had great success with this so far even on early LLM models. One thing you can do is give it the EBNF grammar and it can just generate strings from that. But that method is prone to hallucinations.
The code was always a secondary effect of making software. The pain is in fully specifying behavior.
And is doesn't matter how many times you tell them the implementation and, more importantly, the tests needs to 100% follow the spec they'll still write tests to match the buggy code or just ignore bugs completely until you call them out on it and/or watch them like a hawk.
Maybe I'm just holding it wrong, who knows?
It's just part of the software lifecycle. People think their job is to "write code" and that means everything becomes more and more features, more abstractions, more complex, more "five different ways to do one thing".
Many many examples, C++, Java esp circa 2000-2010 and on and on and on. There's no hope for older languages. We need simpler languages.
Of course someone eventually will, so I might as well: Well, except for lisp-likes. I think the main reason programming languages grow and grow, is because people want to use them in "new" (sometimes new-new, sometimes existing) ways, and how you add new language features to a programming language? You change the core of the language in some way.
What if instead you made it really easy to change the core language from the language itself, when you need to, without impacting other parts of the codebase? Usually if you use a language from the lisp-"family" of languages, you'll be able to.
So instead of the programming language everyone is using grows regardless if you need it or not, it can stay simple and relatively small for everyone, while for the people who need it, they can grow their own hairballs "locally" (or be solid engineers and avoid hairballs in the first place, requires tenure/similar though).
Just this week, I decided to start learning Kotlin because I want to build a mobile app.
Everything was going great until I reached lambda functions.
Honestly, I can't wrap my head around either their purpose or their syntax. I find them incredibly confusing. Right now, they feel like something that was invented purely to confuse developers.
I know this might just be one of those topics where you suddenly have an "aha" moment and everything clicks, but so far, that moment hasn't come.
Did anyone else coming from older, more imperative languages struggle this much with lambdas? Any tips or mental models that helped you finally "get" them?
This is where LLMs slip up. I need a higher-level spec language where I don't have to specify to an LLM that I want the jpeg crop to be lossless if possible. It's doubly obvious that I wouldn't want it to be lossy, especially because making it lossy likely makes the resulting files larger. This is not obvious to an LLM, but it's absolutely obvious if our objects are users and user value.
A truly higher-level spec language compiler would recognize when actual functionality disappeared when a feature was removed, and would weigh the value of that functionality within the value framework of the hypothetical user. It would be able to recognize the value of redundant functionality by putting a value on user accessibility - how many ways can the user reach that functionality? How does it advertise itself?
We still haven't even thought about it properly. It's that "software engineering" thing that we were in a continual argument about whether it existed or not.
If what you do can be done by the systematic manipulation of symbols, we have a better system for that now. If the spec they hand to you has to be so specific that you don't have to think while implementing it, we have a machine that can do everything except think that can handle that.
Does this exist in 2026? I feel like, at least in my bubble, expectations on individual developers has never been higher. I feel like the cut has already been made.
However, how do you inject logic INTO the middle of a function?
Say you have a function which can iterate over any list and given a condition do a filter. How do you inject the condition logic into that filter function?
In the C days you would use a function pointer for this. C++ introduced templating so you could do this regardless of type. Lambdas make the whole process more ergonomic, it's just declaring a one-shot function in place with some convenient syntax.
In rust instead of the full blown
fn filter_condition(val: ValType) -> bool { // logic }
I can declare a function in place with |val|{logic} - the lambda is just syntactic sugar to make your life easier.
Consider:
"Eat grandma if you're hungry"
"Eat grandma, if you're hungry"
"Eat grandma. if you're hungry"
Same words and entirely different outcome.
Pseudo code to clarify:
[Action | Directive - Eat] [Subject - Grandma] [Conditional of Subject - if hungry]
RDX is more like CRDT JSON DOM in fact, not just JSON+. If that makes sense.
I don't know the syntax of kotlin, but lambda functions generally are usually useful to pass as parameters to other, generic/higher-order functions. For example if you have a sort function `sort(listofitems, compareitems_fn)` you could write the `compareitems_fn` as a lambda directly in-line with the call to sort()
for i in range(len(arr)):
something(arr[i])
for i in arr:
something(i)
[something(i) for i in arr]
Bugs don't come from syntax errors. If you've got a syntax error, it doesn't compile/fails to run entirely. So we're not talking about the LLM learning the syntax, I'm asking the LLM learning the deeper semantics of lanng.