9 ms·

Typed languages are better suited for vibecoding

274 points by hosolmaz 1y ago

gompertz 1y ago
Curious, has it been proven that typed languages are easier for LLMs to work with as they dont have to infer types?
- benreesman 1y ago
  I'm not aware of any rigorous study on it, but my personal anecdote is that I don't even bother with Claude Code or similar unless the language is Haskell, the deployment is Nix, the config is Dhall, and I did property tests. Once you set it up like that you just pour money in until its too much money or its stuck, and thats how far LLMs can go now. I used to yell at Claude Code when it tried to con me with mocks to get the TODO scratched off, now I laugh at the little bastard when it tries to pull a fast one on -Werror. Nice try Claude Code, but around here we come to work or we call in sick, so what's it going to be?
  - herrington_d 1y ago
    There are researches backing some sort of "typed language is better for LLM". Like https://arxiv.org/abs/2504.09246 https://arxiv.org/abs/2504.09246, Type-Constrained Code Generation with Language Models, where LLM's output is constrainted by type checkers. Also https://arxiv.org/abs/2406.03283 https://arxiv.org/abs/2406.03283, Enhancing Repository-Level Code Generation with Integrated Contextual Information, uses staic analyzers to produce prompts with more context info. Yet, the argument does directly translate to the conclusion that typed language is rigorously better for LLM without external tools. However, typed language and its static analysis information do seem to help LLM.
    - vidarh 1y ago
      Dynamically typed languages are far from "untyped". Though they may well require more effort to analyze from scratch without making assumptions, there is nothing inherently preventing type-constrained code generation of the kind the first paper proposes even without static typing. A system doing type-constrained code-generation can certainly implement its own static type system by tracking a type for variables it uses and ensuring those constraints are maintained without actually emitting the type checks and annotations. Similarly, static analyzers can be - and have been - applied to dynamically typed languages, though if these projects have been written using typical patterns of dynamic languages the types can get very complex, so this tends to work best with code-bases written for it.
  - cultofmetatron 1y ago
    this is just the kind of sass I needed today. cheers!
- treve 1y ago
  Do they infer anything? Correct me if I'm wrong but having the types right there in the source for training data just means more context.
NischalM 1y ago
I have found this to be true as well. Although I exclusively used python and R at work and tried CC several times for small side projects, it always seemed to have problems and ended up in a loop trying to fix its own errors. CC seems much better at vibe coding with typescript. I went from no knowledge of node.js development to deploying reasonable web app on vercel in a few days. Asking CC to run tsc after changes helps it fix any errors because of the faster feedback from the type system compared to python. Granted this was only for a personal side project and may not be true for production systems that might be much larger, I was pleasantly surprised how easy it was in typescript compared to python
- koakuma-chan 1y ago
  > I was pleasantly surprised how easy it was in typescript compared to python It's time for people to wake up and stop using Python, and forcing me to use Python
- cttet 1y ago
  It may be a Claude specific thing. I tried to ask Claude to various tasks in machine learning, like implement gradient boosting without specifying the language, thinking it will use Python since it is the most common option and have utilities like Numpy to make it much easier. But Claude mostly choose Javascript for the language and somehow managed to do it in JS.
linkage 1y ago
This claim needs to be backed up by evals. I could just as well argue the opposite, that LLMs are best at coding Python because there are two orders of magnitude more Python in their training sets than C++ or Rust. In any case, you can easily get most of the benefits of typed languages by adding a rule that requires the LLM to always output Python code with type annotations and validate its output by running ruff and ty.
- yibers 1y ago
  I agree that the training sets for LLMs have much more training data for Python than for Rust. But C++ has existed before Python I believe. So I doubt there is 2 orders of magnitude of Python code more than C++.
  - hibikir 1y ago
    You miss how many fewer programmers were there in the early years, how much of that code was ever public, and even if it was, how useful it was, as C++ has changed drastically since, say, what we used to write in 2001.
  - vidarh 1y ago
    It's not just a question of whether there is more actual code in a given language, but how much is available in the public and private training data. I've done work on reviewing and fine-tuning training data with a couple of providers, and the amount of Python code I got to see at least out-distanced C++ code by far more than 2 orders of magnitude. It could be a heavily biased sample, but I have no problems believing it also could be representative.
  - a96 1y ago
    Python is pretty old, so I had a quick look. https://en.wikipedia.org/wiki/C%2B%2B#History https://en.wikipedia.org/wiki/C%2B%2B#History In 1985, the first edition of The C++ Programming Language was released, which became the definitive reference for the language, as there was not yet an official standard.[31] The first commercial implementation of C++ was released in October of the same year.[28] In 1998, C++98 was released, standardizing the language, and a minor update (C++03) was released in 2003. https://en.wikipedia.org/wiki/History_of_Python https://en.wikipedia.org/wiki/History_of_Python The programming language Python was conceived in the late 1980s,[1] and its implementation was started in December 1989[2] by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system.[3] Python reached version 1.0 in January 1994. Of course it's hard to say how much that is reflected in code available and is any of the old code still valid input for modern use. It does broadly look like c++ is older, in general.
fluxkernel 1y ago
All existing programming languages are designed for human beings. Is it the right time to design something that is specifically for vibe coding? For example, ease of read/understanding is probably much more important than all the syntactic sugars to reduce typing. Creating ten ways to accomplish the same task is not useful for LLMs.
- largbae 1y ago
  I've been wondering if Java would have a resurgence due to strong typing even into the error types, and widespread runtime availability. But so far, seems no.
- jongjong 1y ago
  Ease of understanding; JavaScript. That was literally its design goal; JS might have a whole lot of bad parts but it's flexible and easy to understand.
- reflectiveattn 1y ago
  This is the complete opposite of how LLMs are trained. LLMs are most effectively prompted (for instruct/chat finetunes anyway, i.e. chatbots) through the same kind of language patterns (natural or formal/programming) that they learn from. Trying to write formal prompts to them is exactly as misguided as speaking to your friends and family in C.
herrington_d 1y ago
The logic above can support exactly the opposite conclusion: LLM can do dynamic typed language better since it does not need to solve type errors and save several context tokens. Practically, it was reported that LLM-backed coding agents just worked around type errors by using `any` in a gradually typed language like TypeScript. I also personally observed such usage multiple times. I also tried using LLM agents with stronger languages like Rust. When complex type errors occured, the agents struggled to fix them and eventually just used `todo!()` The experience above can be caused by insufficient training data. But it illustrates the importance of eval instead of ideological speculation.
- MattGaiser 1y ago
  Or just bad training data. I've seen "any" casually used everywhere.
- mithras 1y ago
  In my experience you can get around it by having a linter rule disallowing it and using a local claude file instructing it to fix the linting issues every time it does something.
  - vidarh 1y ago
    You can equally get around a significant portion of the purported issues with dynamically typed languages by having Claude run tests, and try to run the actual code. I have no problem believing they will handle some languages better than others, but I don't think we'll know whether typing makes a significant difference vs. other factors without actual tests.
  - herrington_d 1y ago
    it does not always work in my experience due to complex type definitions. Also extra tool calls and time are needed to fix linting.
  - theshrike79 1y ago
    I always instructions to have the LLM run `task build` before claiming a task is done. Build runs linters and tests and actually builds the project, kinda-sorta confirming that nothing major broke.
lukev 1y ago
As has been said, actual evals are needed here. Anecdotally, the worst and most common failure mode of an agent is when an agent starts spinning its wheels and unproductively trying to fix some error and failing, iterating wildly, eventually landing on a bullshit (if any) “solution”. In my experience, in Typescript, these “spin out” situations are almost always type-related and often involve a lot of really horrible “any” casts.
- mewpmewp2 1y ago
  This is why I have very specific ruleset and linting for my LLMs, not allowing any at all and other quality checks.
  - Mtinie 1y ago
    Is this a shareable ruleset? I would completely understand if not but I’m interested in learning new ways to interact with my tools.
    - diggan 1y ago
      I tend to have three layers of "rulesets", one general one I reuse across almost any coding task (https://gist.github.com/victorb/1fe62fe7b80a64fc5b446f82d3137398 https://gist.github.com/victorb/1fe62fe7b80a64fc5b446f82d313...), then language specific ones, and finally project specific ones. Concat them before injecting into the system prompt.
      - energy123 1y ago
        Second this method. The one thing I would really recommend adding to your constraints is to Don't Repeat Yourself - always check if something already exists. LLMs like to duplicate functionality, even if it's included in their context. Can I ask why you have asked it to avoid abstractions? My experience has been that the old rules, such as avoid premature abstraction or premature optimization, don't apply as cleanly because of how ephemeral and easy to write the actual code is. I now ask the LLM to anticipate the space of future features and design modular abstractions that maximize extensibility.
chrisjharris 1y ago
I've been wondering about this for some time. My initial assumption was that would be that LLMs will ultimately be the death of typed languages, because type systems are there to help programmers not make obvious mistakes, and near-perfect LLMs would almost never make obvious mistakes. So in a world of near-perfect LLMs, a type system is just adding pointless overhead. In this current world of quite imperfect LLMs, I agree with the OP, though. I also wonder whether, even if LLMs improve, we will be able to use type systems not exactly for their original purpose but more as a way of establishing that the generated code is really doing what we want it to, something similar to formal verification.
- ImprobableTruth 1y ago
  Even near-perfect LLMs would benefit from the compiler optimizations that types allow. However perfect LLMs would just replace compilers and programming languages above assembly completely.
  - jongjong 1y ago
    It's interesting to think about what is 'optimal' when discussing LLMs; considering that the cost is per-token. So assembly would be far from optimal as it is not exactly a succinct language... A lot of common operations are repetitive and require many operations; a more abstract, higher level language might actually be inherently more succinct. It's not just that humans aren't good at thinking in assembly language or binary, but the operations are much more granular and so it requires a lot of operations to do express something as simple as a for-loop or a function call. I think the perfect AI might actually come up with a language closer to Python or JavaScript.
Mistletoe 1y ago
I don't know what vibecoding is, and at this point I'm too afraid to ask.
- shric 1y ago
  It’s fine to not know what it is, but what is the rationale for commenting that you don’t know? Why not just look it up? Or don’t, as you’re too afraid to ask.
- bashtoni 1y ago
  I wouldn't worry too much, no-one seems to be able to agree what it means anyway. Depending on who you speak to it can be anything from coding only by describing the general idea of what you want, to just being another term for LLM assisted programming.
- OutOfHere 1y ago
  The strict original definition of vibe coding is it is LLM writing code with the programmer never caring about the code, only caring about the code's runtime output. It is easily the worst way to use LLMs for code, and I think even coining the term was a highly irresponsible and society-damaging move by Karpathy, making me lose much respect for him. This coined definition was taken literally by managers to fire workers. In truth, for LLM generated code to be maintainable and scalable, it first needs to be speced-out super well by the engineer in collaboration with the LLM, and then the generated code must also be reviewed line-by-line by the engineer. There is no room for vibe coding in making things that last and don't immediately get hacked.
- fulafel 1y ago
  Here's the tweet where Karpathy coined the term. tldr; fast throwaway code from a LLM, where the human is just looking at the results and not trying to make maintainable code. > There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like "decrease the padding on the sidebar by half" because I'm too lazy to find it. I "Accept All" always, I don't read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I'd have to really read through it for a while. Sometimes the LLMs can't fix a bug so I just work around it or ask for random changes until it goes away. It's not too bad for throwaway weekend projects, but still quite amusing. I'm building a project or webapp, but it's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.
J_Shelby_J 1y ago
Writing rust and the LLM almost never gets function signatures and returns types wrong. That just leaves the business logic to sort out. I can only imagine that IDEs will eventually pair directly with the compiler for instant feedback to fix generations. But rust also has traits, lifetimes, async, and other type flavors that multiples complexity and causes issues. It also an in progress language… im about to add a “don’t use once cell.. it’s part of std now “ to my system prompt. So it’s not all sunshine, and I’m deeply curious how a pure vibe coded rust app would turn out.
- 762236 1y ago
  Gemini has been doing a fantastic job for me for Rust
deleted 1y ago
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SteveJS 1y ago
I think this is true -- especially for new code. I did this not knowing any rust: https://github.com/KnowSeams/KnowSeams https://github.com/KnowSeams/KnowSeams and rust felt like a very easy to use a scripting language.
- xwolfi 1y ago
  That seems a little bit dangerous, why not do it in a language you know ? Plus, this is not launching rockets on the moon, it's a sentence splitter with a fancy state machine (probably very useful in your niche, not a critique) - the difficulty was for you to put the effort to build a complicated state machine, the rest was frankly... not very LLM-needing and now you can't maintain your own stuff without Nvidia burning uranium. Did the LLM help at all in designing the core, the state machine itself ?
  - SteveJS 1y ago
    Nah it was a hobby project because I was laid off for a bit. Rust's RegEx was perfect because it doesn't allow anything that isn't a DFA. Yes-ish, the LLM facilitated designing the state machine, because it was part of the dev-loop I was trying out. The speed is primarily what enabled finding all of the edge cases I cared about. Given it can split 'all' of a local project gutenberg mirror in < 10 seconds on my local dev box I could do things I wouldn't otherwise attempt. The whole thing is there in the ~100 "completed tasks" directory.
- NitpickLawyer 1y ago
  Really cool stuff, I appreciate you sharing this. Although, to be fair this is far from vibecoding. Your setup, at a glance, says a lot about how you use the tools, and it's clear you care about the end result a lot. You have a PRD file, your tasks are logged, each task defines both why's and how's, your first tasks are about env setup, quality of dev flow, exploration and so on. (as a nice tidbit, the model(s) seem to have caught on to this, and I see some "WHY:" as inline comments throughout the code, with references to the PRD. This feels nice) It's a really cool example of "HOW" one should approach LLM-assisted coding, and shows that methods and means matter more than your knowledge in langx or langy. You seem to have used systems meant to help you in both speed of dev and ease of testing that what you got is what you need. Kudos! I might start using your repo as a good example of good LLM-assisted dev flows.
exclipy 1y ago
The closest we got to vibe coding pre-LLMs was using a language with a very good strong type system in a good IDE and hitting Ctrl-Space to autocomplete your way to a working program. I wonder if LLMs can use the type information more like a human with an IDE. eg. It generates "(blah blah...); foo." and at that point it is constrained to only generate tokens corresponding to public members of foo's type. Just like how current gen LLMs can reliably generate JSON that satisfies a schema, the next gen will be guaranteed to natively generate syntactically and type- correct code.
- esafak 1y ago
  LLMs can use LSPs. https://en.wikipedia.org/wiki/Language_Server_Protocol https://en.wikipedia.org/wiki/Language_Server_Protocol
- deleted 1y ago
  [deleted]
- treyd 1y ago
  You already can use LLM engines that force generation according to an arbitrary CFG definition. I am not aware of any systems that apply that to generating actual programming language code.
- koolba 1y ago
  > I wonder if LLMs can use the type information more like a human with an IDE. Just throw more GPUs at the problem and generate N responses in parallel and discard the ones that fail to match the required type signature. It’s like running a linter or type check step, but specific to that one line.
  - xwolfi 1y ago
    We have infinite uranium anyway !
nu11ptr 1y ago
Everything said is true without AI as well, at least for me. I don't hate Python, and I like it for very small scripts, but for large programs the lack of static type makes it much to brittle IMO. Static typing gives the confidence that not every single line needs testing, which reduces friction during the lifecycle of the code.
rvz 1y ago
Such extraordinary claims, require extraordinary evidence. Not "vibes" > It seems that typed, compiled, etc. languages are better suited for vibecoding, because of the safety guarantees. There are no "safety guarantees" with typed, compiled languages such as C, C++, and the like. Even with Go, Rust and others, if you don't know the language well enough, you won't find the "logic bugs" and race conditions in your own code that the LLM creates; even with the claims of "safety guarantees". Additionally, the author is slightly confusing the meaning of "safety guarantees" which refers to memory safety. What they really mean is "reasoning with the language's types" which is easier to do with Rust, Go, etc and harder with Python (without types) and Javascript. Again we will see more of LLM written code like this example: [0] [0] https://sketch.dev/blog/our-first-outage-from-llm-written-code https://sketch.dev/blog/our-first-outage-from-llm-written-co...
lvl155 1y ago
I can say with 100% certainty that they all stink at Rust. It’s laughably bad. Python, on the other hand, is surprisingly good.
- energy123 1y ago
  I scraped every comment on HN that discussed using Rust with LLMs and about half gave positive feedback, half negative feedback. Can you explain more why you've arrived at this opinion?
- OutOfHere 1y ago
  With Python, it scales better if the Python is well-typed, not so much otherwise. It's the best at Go imho since it has enforced types and a garbage collector.
timuckun 1y ago
It's been my experience that strongly opinionated frameworks are better for vibe coding regardless of the type system. For example if you are using rails vibe coding is great because there is an MCP, there are published prompts, and there is basically only one way to do things in rails. You know how files are to be named, where they go, what format they should take etc. Try the same thing in go and you end up with a very different result despite the fact that go has stronger typing. Both Claude and Gemini have struggled with one shotting simple apps in go but succeed with rails.
- topato 1y ago
  This is pretty anecdotal, but it feels like most of the published rails source code you find online (and by extension, an LLM has found) is from large, stable, and well-documented code.
  - rafamvc 1y ago
    Claude code with rails is amazing. Should out to Obie for the Claude on rails. Works phenomenally well.
- EGreg 1y ago
  Basically it's like this: the more constraints you have, the more freedom you have to "vibe" code and if someone actually built AI for writing tests, catching bugs and iterating 24/7 then you'd have something even cooler
  - delta_p_delta_x 1y ago
    > if someone actually built AI for writing tests, catching bugs and iterating 24/7 This is called a nightly CI/CD pipeline. Run a build and run all tests and run all coverage at midnight, failed/regressed tests and reduced coverage automatically are assigned to new tickets for managers to review and assign.
    - progbits 1y ago
brikym 1y ago
You could just leave it at "Typed languages are better."
adamnemecek 1y ago
They are also better suited for being ported to other languages, also unsurprisingly
itsafarqueue 1y ago
This generalises to “Agents respond well to red/green feedback loops”.
warrenmiller 1y ago
it aint great at c# i can tell you. this from grok yesterday: foreach (string enumName in Enum.GetNames(typeof(Pair))) { if (input.Contains($"${enumName}"))
woodruffw 1y ago
> I am managing projects in languages I am not fluent in—TypeScript, Rust and Go—and seem to be doing pretty well. This framing reminds me of the classic problem in media literacy: people know when a journalistic source is poor when they’re a subject matter expert, but tend to assume that the same source is at least passably good when less familiar with the subject. I’ve had the same experience as the author when doing web development with LLMs: it seems to be doing a pretty good job, at least compared to the mess I would make. But I’m not actually qualified to make that determination, and I think a nontrivial amount of AI value is derived from engineers thinking that they are qualified as such.
- giantrobot 1y ago
  Gell-Mann Amnesia [0] [0] https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect
  - woodruffw 1y ago
    Thank you! I couldn’t remember the term.
    - giantrobot 1y ago
      > I couldn’t remember the term. That's lethologica! Or maybe in this specific case lethonomia. [0] [0] https://en.m.wikipedia.org/wiki/Tip_of_the_tongue https://en.m.wikipedia.org/wiki/Tip_of_the_tongue
- bravesoul2 1y ago
  Why I only use it on stuff I can properly judge.
- deleted 1y ago
  [deleted]
- muglug 1y ago
  Yup — this doesn't match my experience using Rust with Claude. I've spent 2.5 years writing Rust professionally, and I'm pretty good at it. Claude will hallucinate things about Rust code because it’s a statistical model, not a static analysis tool. When it’s able to create code that compiles, the code is invariably inefficient and ugly. But if you want it to generate chunks of usable and eloquent Python from scratch, it’s pretty decent. And, FWIW, I’m not fluent in Python.
jbellis 1y ago
I'm really shocked at how slow people are to realize this, because it's blindingly obvious. I guess that just shows how much the early adopter crowed is dominated by python and javascript. (BTW the answer is Go, not Rust, because the other thing that makes a language well suited for AI development is fast compile times.)
- woodruffw 1y ago
  My experience with agent-assisted programming in Rust is that the agent typically runs `cargo check` instead of `cargo build` for this exact reason -- it's much faster and catches the relevant compilation errors. (I don't have an opinion on one being better than the other for LLM-driven development; I've heard that Go benefits from having a lot more public data available, which makes sense to me and seems like a very strong advantage.)
poink 1y ago
Typed languages are also better suited to IDE assistance and static analysis I'm a relatively old school lisp fan, but it's hard to do this job for a long time without eventually realizing helping your tools is more valuable than helping yourself
anupshinde 1y ago
I am comfortable with both Python and Go. I prefer Go for performance; however, the earlier issue was verbosity. It is easier to write things using a Python dict than to create a struct in Go or use the weird `map[string]interface{}` and then deal with the resulting typecast code. After I started using GitHub Copilot (before the Agents), that pain went away. It would auto-create the field names, just by looking at the intent or a couple of fields. It was just a matter of TAB, TAB, TAB... and of course I had to read and verify - the typing headache was done with. I could refactor the code easily. The autocomplete is very productive. Type conversion was just a TAB. The loops are just a TAB. With Agents, things have become even better - but also riskier, because I can't keep up with the code review now - it's overwhelming.
jjcm 1y ago
I've noticed a fairly similar pattern. I particularly like vibecoding with golang. Go is extremely verbose, which makes it almost like an opposite perl - writing go is a bad experience, but reading go is delightful. The verbosity of golang makes it so you're able to always jump in and understand context, often from just a single file. Pre-llms, this was an up front cost when writing golang, which made the cost/benefit tradeoff often not worth it. With LLMs, the cost of writing verbose code not only goes down, it forces the LLM to be strict with what it's writing and keeps it on track. The cost/benefit tradeoff has increased greatly in go's favor as a result.
- WD-42 1y ago
  No shade on Go but you kinda just said that the language has always looked like AI generated code and this works in its favor now because you don’t actually have to write it anymore. Funny, but not sure I’d consider that in Go’s favor.
OutOfHere 1y ago
The argument against Python is weak because Python can be written with types. Moreover, the types can be checked for correctness by various type checkers. The issue is those who don't use type checkers religiously with Python - they give Python a bad name.
rgoldfinger 1y ago
Totally agree. With ai coding, ensuring correctness is critical. Having types and compile-time checks helps a lot.
Reubend 1y ago
While I agree with the main thesis here, I find this extremely worrying: > I am amazed every time how my 3-5k line diffs created in a few hours don’t end up breaking anything, and instead even increase stability. In my personal opinion, there's no way you're going to get a high quality code base while adding 3,000 - 5,000 lines of code from LLMs on a regular basis. Those are huge diffs.
- pablitokun 1y ago
  Yeah imagine one of your colleagues doing this to your code base...
  - a96 1y ago
    Imagine a reviewer that doesn't block that patch immediately. Of course, there might be some exceptions like if the codebase for some reason has some massive fixed tables or imports upstream files that may get updated occasionally. Those end up as massive patches or sets.
- jongjong 1y ago
  Yes. From experience, for a relatively complex system, 1k+ line PRs from mid-level devs without tests are almost guaranteed to have bugs; often nasty ones which can take many hours to identify and fix. I remember when I started coding (decades ago), it would take me days to debug certain issues. Part of the problem was that it was difficult to find information online at the time, but another part of the problem was that my code was over-engineered. I could churn out thousands of lines of code quickly but I was only trying to produce code which appeared to work, not code which actually worked in all situations. I would be shocked when some of my code turned out to break once in a while but now I understand that this is a natural consequence of over-complicating the solution and churning out those lines as fast as I could without thinking enough. Good code is succinct; it looks like it was discovered in its minimal form. Bad code looks like it was invented and the author tried to make it extra fancy. After 20 years coding, I can tell bad code within seconds. Good code is just easy to read; first of all, you already know what each function is going to do before you even started reading it, just by its name. Then when you read it, there's nothing unexpected, it's not doing anything unnecessary.
Myrmornis 1y ago
Python has static typing unless you don't add any types. The vast majority of reputable Python codebases nowadays use static typing rigorously. If you don't, you should. To enforce it when coding with an agent you can either tell the agent to run the type checker after every edit (e.g. via a hook in Claude Code), or if you're using an agent that has access to the LSP diagnostics then tell it to look at them and demand that they are clean after every edit (easy with Cursor, and achieveable in Claude Code I believe via MCP).
- heavyset_go 1y ago
  Why isn't the agent smart enough to recognize typed Python code existing in a project or detect that an explicit py.typed file exists?
  - Myrmornis 1y ago
    In the case of Claude Code the hook feature is ideal for this so I could imagine the designers deciding that it is more appropriate to put the user in control. That said I think I do agree with you that -- given Python's fairly unique position of having good static typing but not requiring it -- the agents should default to running the type checker if they see it configured in pyproject.toml.
- physicsguy 1y ago
  > The vast majority of reputable Python codebases nowadays use static typing rigorously As judged by who? And in what field? I mean, if I look at the big Python libraries I use regularly none of them have types - Django, DRF, NumPy, SciPy, Scikit-learn. That’s not to say there aren’t externally provided stubs but the library authors themselves are often not the ones writing them
  - Myrmornis 1y ago
    Yes fair enough my wording wasn't great. And we can add cpython to your list... But the provided type stubs for those libraries make the resulting user experience the same as if they had types. Does Django ORM have a good typing experience via stubs btw? I know that one's always been a challenge. Overall though my point was that the article, and most comments here, were completely misrepresenting the situation regarding Python. It's a statically typed language for those that want it to be. There's no need to attempt to run any code that hasn't passed a type checker. And it's an expressive type system; much more so than Go which has been mentioned in comments. However the fact that the standard library documentation doesn't have types is embarrassing IMO.
jongjong 1y ago
My experience suggests the opposite of what this article claims. Claude Code is ridiculously good with vanilla JavaScript, provided that your code is well written. I tried it with a TypeScript code base and it wasn't anywhere near as good. With JS, Claude has very high success rate. Only issue I had with it was that one time it forgot to update one part of the code which was in a different file but as soon as I told it, it updated it perfectly. With TypeScript my experience was that it struggles to find things. Writing tests was a major pain because it kept trying to grep the build output because it had to mock out one of the functions in the test and it just couldn't figure it out. Also typed code it produces is more complex to solve the same problem with more different files and it struggles to get the right context. Also TS is more verbose (this is objectively true and measurable); requires more tokens so it literally costs more.
randomifcpfan 1y ago
Here’s a study that found that for small problems Gemini is almost equally good at Python and Rust. Looking at the scores of all the languages tested, it seems that the popularity of the language is the most important factor: https://jackpal.github.io/2025/03/29/Gemini_2.5_Pro_Advent_of_Code.html https://jackpal.github.io/2025/03/29/Gemini_2.5_Pro_Advent_o...
- whytevuhuni 1y ago
  But isn't it the case that Python is vastly more popular than Rust? If Gemini is equally good at them in spite of that, doesn't that mean it'd be better at Rust than at Python if it had equal training in both?
  - randomifcpfan 1y ago
    The study points out, “Python and Rust are the two most popular languages used by Advent of Code participants. This may explain why Rust fares so well.”
    - whytevuhuni 1y ago
      Ah, that makes a lot more sense. Thanks!
- reflectiveattn 1y ago
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blurbleblurble 1y ago
I've had really good experiences with claude code + rust
rbalicki 1y ago
Folks here may be interested in checking out Isograph. In [this conference talk](https://www.youtube.com/watch?v=sf8ac2NtwPY https://www.youtube.com/watch?v=sf8ac2NtwPY), I vibe code an Isograph app, and make non-trivial refactors to it using Cursor. This is only feasible because the interface between components is very simple, and all the hard stuff (generating a query for exactly the needed data, wiring things up, etc.) is done deterministically, by a compiler. It's not quite the same principal OP articulates, which is that a compiler provides safety and that certainty lets you move fast when vibe coding. Instead, what I'm claiming is that you can move fast by allowing the LLM to focus on fewer things. (Though, incidentally, the compiler does give you that safety net as well.)
MutedEstate45 1y ago
The real win isn't static vs dynamic typing. It's immediate, structured feedback for LLM iteration. cargo check gives the LLM a perfectly formatted error it can fix in the next iteration. Python's runtime errors are often contextless ('NoneType has no attribute X') and only surface after execution. Even with mypy --strict, you need discipline to check it constantly. The compiler makes good feedback unavoidable.
- seunosewa 1y ago
  Printing stack traces generates a lot of useful context but it's not done enough.
throwawaymaths 1y ago
i find claude is very good with elixir, which is a dynamically typed language. i suspect strong conventions and value immutability help.
leric 1y ago
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levocardia 1y ago
Interesting...my experience has been that LLMs are generally better at more common languages (not surprising: more data exists in those languages!). So, my admittedly amateur vibe coding experiences have been best in Python and pretty vanilla web development setups. When I push LLMs to, say, fit advanced statistical models in R, they fall apart pretty badly. Yet they can crush a PyTorch or SciKitLearn task no problem.
- reflectiveattn 1y ago
  This. This is the most important thing to consider: the available corpus the model was trained on. Remember that LLMs are inferring code. They don't "know" anything at all about its axiomatic workings. They just know what "looks right" and what "looks wrong". Agentic and RL are about to make this philosophy obsolete on grand scale, but signs still don't look good for being any to improve how much they can "hold in their head" to infer what token to spit out next from the vector embedding, tho.
misja111 1y ago
My experience with Python and Scala so far is different. With Python the LLM's do a pretty good job. The code always compiles, sometimes there are some logical or architectural errors but that's it. With Scala, I have to give the LLM a super simple job, e.g. creating some mock data for a unit test, and even then it frequently screws up; every now and then it gives me code that doesn't even compile. So much for Scala's strong type system ..
- jmfldn 1y ago
  Less training data for Scala is the issue probably. Imagine how much Python is out there.
- smrtinsert 1y ago
  I've been asking it to spit out python all day long and it just flies with it. Ask all the LLMs most of them will tell you Python is the top if not preferred language.
solatic 1y ago
It's not so much typing that is valuable for vibecoding, but being able to give the agent hooks into tooling that provides negative feedback for errors. The easiest is typing, sure, because it's built into the compiler. But you can also add in static analysis linters and automated testing, including - notably - testing for performance. Of course, you have to tell the agent to set up static analysis linters first, and tell the agent to write tests. But then it'll obey them. The reason why large enterprises could hire armies of juniors in the past, safely, was because they set up all manner of guardrails that juniors could bounce off of. Why would you "hire" a "junior" agent without the same guardrails in place?
- darksaints 1y ago
  Exactly this. The ability of LLMs to write code is going to strongly depend on the availability and quantity of training data. But agentic coding is more than just LLMs, it is also the various abilities that give feedback to the LLM to refine the resulting code...and that is something that strongly typed and statically typed languages do so much better than their weak/dynamic counterparts.
sshine 1y ago
I've been vibe-coding for weeks in Rust, and it works great. I've been vibe-coding for a few days in Haskell, and I don't like the result. Maybe I am just accustomed to being ok with verbose Rust, while Haskell comes with a great potential for elegance that the LLM does not explore. Regardless, the argument that types guide the LLM in a very reliable way holds in both cases.
- xwiz 1y ago
  My experience with Haskell has been the same. The GHC provides stellar feedback, so the LLM is almost always able to bang the code into working order, but wow is that code bloated.
isodev 1y ago
Typed but maybe with the exception of the likes of Swift where Claude reveals just how complex and ambiguous the language can be. The lack of documentation and overly complex proposal documents also appear to overload the LLM context and confuse them.
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teiferer 1y ago
Nit upfront: Python is typed, just not statically typed. What dynamically typed languages lack in compile-time safety, the programmer must make up using (automated) testing. With adequate tests, a python program doesn't break more than a Rust or Go program. It's just that people often regard testing as an annoying chore which is the first thing they skip when vibe coding (or "going fast and breaking things" which is then literally what happens).
- tonyhart7 1y ago
  "a python program doesn't break more than a Rust or Go program" but it is tho, You literally can just give LLM to check LSP to analyze early it for you without write test to begin, Their LSP and Compiler is just that smart
Surac 1y ago
I have made the exact opposite with Claude and low level C. Claude is very good in writing classic c functions you need on a daily basis. I often wonder how much defensive coding it puts into the functions. I for myself let any code I write at least be read one time by Claude now
theusus 1y ago
I programmed my services in Python without any convention and I suffered a lot. Now, I do it in typed languages with a strong compulsory conventions and things are far more manageable.
tibastral2 1y ago
Try PURE functionnal programming, it's even better ;)
- jrvieira 1y ago
  try not vibe coding. it's even better
antirez 1y ago
LLMs also write good C, if well directed. My feeling is that this is not really about C or something inherent to Python (where I get not stellar results), but to the large low quality Python code bases that are out there. Basically my hypothesis is that, within the training set, there are languages with better examples and languages with worse examples. I found that to write better Python, prompt engineering goes a great length: especially stressing of not using not really needed dependencies, to write simple, avoid trivial asserts that are not really useful, and so forth.
zk108 1y ago
*Typed languages are better Fixed it
pyrale 1y ago
I'm not sure I agree with the author's conclusion. While python was never a great language for large codebases and it thrived because people with little development knowledge could get going pretty easily, a large part of its current appeal is the profusion of great specialized libraries which you would have to code yourself in other languages. I suspect vibe coding will not be a good fit for writing these libraries, because they require knowledge and precision which the typical vibe coding use probably doesn't show, or the willingness to spend time on the topic which is also typically not what drives people to vibe coding. So my conclusion would be that vibe coding drives the industry to solidify around already well-established ecosystem, since less of the people producing code will have the time, knowledge and/or will to build that ecosystem in newer languages. Whether that drive is strong enough to be noticable is another question.
- jaynetics 1y ago
  Then again, LLMs are well-suited to translate stuff, a relatively grunt work kind of task, so porting libs to your ecosystem of choice is a lot more feasible now. Perhaps there is a future where individuals can translate large numbers of libraries, and instead of manually porting future improvements of the original versions to the copies, just rerun the translation as needed.
  - hugs 1y ago
    Yup, I recently started doing more development in Nim. I love the language, but the user community is (currently) small, which means the ecosystem of libraries available isn't as big. But LLMs are a massive equalizer here and has made it a lot easier for me to get things done with Nim.
lonelyasacloud 1y ago
So if for whatever reason it is better for vibe coding, then legacy code aside, why would anyone not use a technology that makes it a bit easier for them to understand what the AI is actually churning out on their behalf?
OldfieldFund 1y ago
Why not just tell the LLM in rules to strongly type and write guardrails and then: [tool.ruff] line-length = 88 select = ["E", "F", "W", "I", "N", "UP", "B", "C4"] # A good strict baseline ignore = [] [tool.mypy] python_version = "3.12" warn_return_any = true warn_unused_configs = true disallow_untyped_defs = true disallow_any_unimported = true no_implicit_optional = true check_untyped_defs = true strict = true
- lysecret 1y ago
  Yep this is pretty much what I do. I also added a /check command to explcicitly runn all those checks + pyright and ruff format.
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binarymax 1y ago
I’ve not had good success with vibing rust. It requires lots and lots of handholding and editing. Perhaps it’s because the model is always trying to do things from scratch. It does a poor job of finding crates and understanding the docs and implementation.
jelder 1y ago
Preference for typed languages may be the one and only good thing to come out of this bubble.
__mharrison__ 1y ago
Curious if anyone has done experiments where they compare output of Python vs annotated Python. Do type hints help LLMs?
hugs 1y ago
Nim might hit the sweet-spot here: typed, compiled, and Python-like. I wrote this [1] comment a few weeks ago: """ ... Claude Code is surprisingly good at writing Nim. I just created a QuickJS + MicroPython wrapper in Nim with it last week, and it worked great! Don't let "but the Rust/Go/Python/JavaScript/TypeScript community is bigger!" be the default argument. I see the same logic applied to LLM training data: more code means more training data, so you should only use popular languages. That reasoning suggests less mainstream languages are doomed in the AI era. But the reality is, if a non-mainstream language is well-documented and mature (Nim's been around for nearly 20 years!), go for it. Modern AI code gen can help fill in the gaps. """ [1]: https://news.ycombinator.com/item?id=44400913 https://news.ycombinator.com/item?id=44400913
infecto 1y ago
I can see this making sense purely from a tool chain perspective. If we’re are entering the age of treating code like cattle then it would make sense overly verbose and strict languages may benefit from it.
stared 1y ago
I have the same impressions. Typing helps a lot, and (I think) in a few ways - one is being a safe guard, second a constraint (so say, AI is less likely to create a clunky variable which can be a string, list, or a few other things), third - to prompt into writing solid code in general. I add one more step - add strong linting (ESLint with all recommended rules switched on, Ruff for Python) and asking to run it after each edit. Usually I also prompt to type things well, and avoid optional types unless strictly necessary (LLMs love to shrink responsibility that way). For example, see my recent vibe-coding instructions, https://github.com/QuesmaOrg/demo-webr-ggplot/blob/main/CLAUDE.md https://github.com/QuesmaOrg/demo-webr-ggplot/blob/main/CLAU....
jcmontx 1y ago
I reached the same conclusion. But unfortunately LLMs are not that good at C#.
- seunosewa 1y ago
  What did the LLMs get wrong most frequently when working with C#?
  - jcmontx 1y ago
    They write code that don't build in the first or second shot. With Claude Code I gave it instructions to "fix tests" and it became so frustrated with them it started to "rm" the files lol
    - seunosewa 1y ago
      Providing agents with relevant documentation including code samples and API references should help a lot in your scenario. Relevant documentation helps a lot when you're working with obscure languages or libraries.
jcadam 1y ago
I've found most LLMs I've tried generate better code in typed, procedural languages than they do in something like Clojure. From the perspective of a primarily backend dev who knows just enough React/ts to be dangerous, Claude is generating pretty decent frontend code, letting me spend more time on the Rust backend of my current side project.
- iLemming 1y ago
  > generate better code in typed, procedural languages Better in what sense? I've been using Anthropic models to write in different Lisps - Fennel, Clojure, Emacs Lisp, and they do perform a decent job. I can't always blindly copy-and-paste generated code, but I wouldn't do that with any PL.
dragonwriter 1y ago
> I am managing projects in languages I am not fluent in—TypeScript, Rust and Go—and seem to be doing pretty well. > It seems that typed, compiled, etc. languages are better suited for vibecoding, because of the safety guarantees. This is unsurprising in hindsight, but it was counterintuitive because by default I “vibed” projects into existence in Python since forever [...] > For example, I refactored large chunks of our TypeScript frontend code at TextCortex. Claude Code runs tsc after finishing each task and ensures that the code compiles before committing. This let me move much faster compared to how I would have done it in Python, which does not provide compile-time guarantees. While Python doesn't have a required compulation step, it has both a standard type system and typecheckers for it (mypy, etc.) that are ubiquitous in the community and could be run at the same point in the process. I would say it's not just Rust, TypeScript, and Go that the author has a weak foundation in.
helle253 1y ago
My experience with LLMs in Rails has been... pretty bad. It isn't good at tracking 'context' (not in the technical token sense) and constantly gets lost in the sauce and doing weird stuff. Given Rail's maturity, i would have expected otherwise - there is tons of Ruby/Rails code to train on, but... yeah. OTOH, doing some side-project stuff in TS, and the difference is a little mindblowing. I can see the hype behind vibecoding WAY more.
dominicrose 1y ago
I can vibecode in Rust but I don't like the result. There are too many lines of code and they are too long and contain too many symbols and extra stuff. Just compare SeaORM with Ruby + sequel where you just inherit the Sequel::Model class and Sequel reads the table schema without you having to tell it to. It gives you objects with one method for each column and each value has the correct type. I was happy with Ruby's performance 15 years ago and now it's about 7-20x with a modern ruby version and CPU, one a single thread. AI is still helpful to learn but it doesn't need to do the coding when using Ruby. I think the same criteria apply with or without AI for choosing a language. Is it a single-person project? Does it really require highly optimized machine code? etc.
- joker99 1y ago
  Sounds like you really need to have a look at loco.rs! Given the right constraints, I’m fairly productive with CC
waffletower 1y ago
I have not found this to be the case at all. Type mismatches have been very common in Java, C++ and Objective-C inference output. I think there is complexity in what contributes to LLM suitability to programming tasks, and the nature and history of APIs relevant to the ask are a big part of that. Seems that the OP really loves their types, like many here, and this article is just more evangelism.
smrtinsert 1y ago
> Because of this, I predict a decrease in Python adoption in companies, specifically for production deployments, even though I like it so much. Most definitely not going to happen. Python is the language of the AI age and lot of ML/AI libraries do their reference or first release in Python.
irvingprime 1y ago
Vibecoding is bad coding. Always. Even if I take the headline as correct, so what? It's still crap code that will collapse into an unmaintainable mess sooner rather than later.
reflectiveattn 1y ago
The language using the fewest punctuation tokens is going to be the safest from most categories of hallucination, and give each context window the greatest usable space for vector manipulation headed into self-attention before the model suffers from "vector-clouded judgment" due to overcrowded latent space.

274 points by hosolmaz 1y ago

gompertz 1y ago

Curious, has it been proven that typed languages are easier for LLMs to work with as they dont have to infer types?

benreesman 1y ago
I'm not aware of any rigorous study on it, but my personal anecdote is that I don't even bother with Claude Code or similar unless the language is Haskell, the deployment is Nix, the config is Dhall, and I did property tests. Once you set it up like that you just pour money in until its too much money or its stuck, and thats how far LLMs can go now. I used to yell at Claude Code when it tried to con me with mocks to get the TODO scratched off, now I laugh at the little bastard when it tries to pull a fast one on -Werror. Nice try Claude Code, but around here we come to work or we call in sick, so what's it going to be?
- herrington_d 1y ago
  There are researches backing some sort of "typed language is better for LLM". Like https://arxiv.org/abs/2504.09246 https://arxiv.org/abs/2504.09246, Type-Constrained Code Generation with Language Models, where LLM's output is constrainted by type checkers. Also https://arxiv.org/abs/2406.03283 https://arxiv.org/abs/2406.03283, Enhancing Repository-Level Code Generation with Integrated Contextual Information, uses staic analyzers to produce prompts with more context info. Yet, the argument does directly translate to the conclusion that typed language is rigorously better for LLM without external tools. However, typed language and its static analysis information do seem to help LLM.
  - vidarh 1y ago
    Dynamically typed languages are far from "untyped". Though they may well require more effort to analyze from scratch without making assumptions, there is nothing inherently preventing type-constrained code generation of the kind the first paper proposes even without static typing. A system doing type-constrained code-generation can certainly implement its own static type system by tracking a type for variables it uses and ensuring those constraints are maintained without actually emitting the type checks and annotations. Similarly, static analyzers can be - and have been - applied to dynamically typed languages, though if these projects have been written using typical patterns of dynamic languages the types can get very complex, so this tends to work best with code-bases written for it.
- cultofmetatron 1y ago
  this is just the kind of sass I needed today. cheers!
treve 1y ago
Do they infer anything? Correct me if I'm wrong but having the types right there in the source for training data just means more context.

NischalM 1y ago

I have found this to be true as well. Although I exclusively used python and R at work and tried CC several times for small side projects, it always seemed to have problems and ended up in a loop trying to fix its own errors. CC seems much better at vibe coding with typescript. I went from no knowledge of node.js development to deploying reasonable web app on vercel in a few days. Asking CC to run tsc after changes helps it fix any errors because of the faster feedback from the type system compared to python. Granted this was only for a personal side project and may not be true for production systems that might be much larger, I was pleasantly surprised how easy it was in typescript compared to python

koakuma-chan 1y ago
> I was pleasantly surprised how easy it was in typescript compared to python It's time for people to wake up and stop using Python, and forcing me to use Python
cttet 1y ago
It may be a Claude specific thing. I tried to ask Claude to various tasks in machine learning, like implement gradient boosting without specifying the language, thinking it will use Python since it is the most common option and have utilities like Numpy to make it much easier. But Claude mostly choose Javascript for the language and somehow managed to do it in JS.

linkage 1y ago

This claim needs to be backed up by evals. I could just as well argue the opposite, that LLMs are best at coding Python because there are two orders of magnitude more Python in their training sets than C++ or Rust. In any case, you can easily get most of the benefits of typed languages by adding a rule that requires the LLM to always output Python code with type annotations and validate its output by running ruff and ty.

yibers 1y ago
I agree that the training sets for LLMs have much more training data for Python than for Rust. But C++ has existed before Python I believe. So I doubt there is 2 orders of magnitude of Python code more than C++.
- hibikir 1y ago
  You miss how many fewer programmers were there in the early years, how much of that code was ever public, and even if it was, how useful it was, as C++ has changed drastically since, say, what we used to write in 2001.
- vidarh 1y ago
  It's not just a question of whether there is more actual code in a given language, but how much is available in the public and private training data. I've done work on reviewing and fine-tuning training data with a couple of providers, and the amount of Python code I got to see at least out-distanced C++ code by far more than 2 orders of magnitude. It could be a heavily biased sample, but I have no problems believing it also could be representative.
- a96 1y ago
  Python is pretty old, so I had a quick look. https://en.wikipedia.org/wiki/C%2B%2B#History https://en.wikipedia.org/wiki/C%2B%2B#History In 1985, the first edition of The C++ Programming Language was released, which became the definitive reference for the language, as there was not yet an official standard.[31] The first commercial implementation of C++ was released in October of the same year.[28] In 1998, C++98 was released, standardizing the language, and a minor update (C++03) was released in 2003. https://en.wikipedia.org/wiki/History_of_Python https://en.wikipedia.org/wiki/History_of_Python The programming language Python was conceived in the late 1980s,[1] and its implementation was started in December 1989[2] by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system.[3] Python reached version 1.0 in January 1994. Of course it's hard to say how much that is reflected in code available and is any of the old code still valid input for modern use. It does broadly look like c++ is older, in general.

fluxkernel 1y ago

All existing programming languages are designed for human beings. Is it the right time to design something that is specifically for vibe coding? For example, ease of read/understanding is probably much more important than all the syntactic sugars to reduce typing. Creating ten ways to accomplish the same task is not useful for LLMs.

largbae 1y ago
I've been wondering if Java would have a resurgence due to strong typing even into the error types, and widespread runtime availability. But so far, seems no.
jongjong 1y ago
Ease of understanding; JavaScript. That was literally its design goal; JS might have a whole lot of bad parts but it's flexible and easy to understand.
reflectiveattn 1y ago
This is the complete opposite of how LLMs are trained. LLMs are most effectively prompted (for instruct/chat finetunes anyway, i.e. chatbots) through the same kind of language patterns (natural or formal/programming) that they learn from. Trying to write formal prompts to them is exactly as misguided as speaking to your friends and family in C.

herrington_d 1y ago

The logic above can support exactly the opposite conclusion: LLM can do dynamic typed language better since it does not need to solve type errors and save several context tokens. Practically, it was reported that LLM-backed coding agents just worked around type errors by using `any` in a gradually typed language like TypeScript. I also personally observed such usage multiple times. I also tried using LLM agents with stronger languages like Rust. When complex type errors occured, the agents struggled to fix them and eventually just used `todo!()` The experience above can be caused by insufficient training data. But it illustrates the importance of eval instead of ideological speculation.

MattGaiser 1y ago
Or just bad training data. I've seen "any" casually used everywhere.
mithras 1y ago
In my experience you can get around it by having a linter rule disallowing it and using a local claude file instructing it to fix the linting issues every time it does something.
- vidarh 1y ago
  You can equally get around a significant portion of the purported issues with dynamically typed languages by having Claude run tests, and try to run the actual code. I have no problem believing they will handle some languages better than others, but I don't think we'll know whether typing makes a significant difference vs. other factors without actual tests.
- herrington_d 1y ago
  it does not always work in my experience due to complex type definitions. Also extra tool calls and time are needed to fix linting.
- theshrike79 1y ago
  I always instructions to have the LLM run `task build` before claiming a task is done. Build runs linters and tests and actually builds the project, kinda-sorta confirming that nothing major broke.

lukev 1y ago

As has been said, actual evals are needed here. Anecdotally, the worst and most common failure mode of an agent is when an agent starts spinning its wheels and unproductively trying to fix some error and failing, iterating wildly, eventually landing on a bullshit (if any) “solution”. In my experience, in Typescript, these “spin out” situations are almost always type-related and often involve a lot of really horrible “any” casts.

mewpmewp2 1y ago
This is why I have very specific ruleset and linting for my LLMs, not allowing any at all and other quality checks.
- Mtinie 1y ago
  Is this a shareable ruleset? I would completely understand if not but I’m interested in learning new ways to interact with my tools.
  - diggan 1y ago
    I tend to have three layers of "rulesets", one general one I reuse across almost any coding task (https://gist.github.com/victorb/1fe62fe7b80a64fc5b446f82d3137398 https://gist.github.com/victorb/1fe62fe7b80a64fc5b446f82d313...), then language specific ones, and finally project specific ones. Concat them before injecting into the system prompt.
    - energy123 1y ago
      Second this method. The one thing I would really recommend adding to your constraints is to Don't Repeat Yourself - always check if something already exists. LLMs like to duplicate functionality, even if it's included in their context. Can I ask why you have asked it to avoid abstractions? My experience has been that the old rules, such as avoid premature abstraction or premature optimization, don't apply as cleanly because of how ephemeral and easy to write the actual code is. I now ask the LLM to anticipate the space of future features and design modular abstractions that maximize extensibility.

chrisjharris 1y ago

I've been wondering about this for some time. My initial assumption was that would be that LLMs will ultimately be the death of typed languages, because type systems are there to help programmers not make obvious mistakes, and near-perfect LLMs would almost never make obvious mistakes. So in a world of near-perfect LLMs, a type system is just adding pointless overhead. In this current world of quite imperfect LLMs, I agree with the OP, though. I also wonder whether, even if LLMs improve, we will be able to use type systems not exactly for their original purpose but more as a way of establishing that the generated code is really doing what we want it to, something similar to formal verification.

ImprobableTruth 1y ago
Even near-perfect LLMs would benefit from the compiler optimizations that types allow. However perfect LLMs would just replace compilers and programming languages above assembly completely.
- jongjong 1y ago
  It's interesting to think about what is 'optimal' when discussing LLMs; considering that the cost is per-token. So assembly would be far from optimal as it is not exactly a succinct language... A lot of common operations are repetitive and require many operations; a more abstract, higher level language might actually be inherently more succinct. It's not just that humans aren't good at thinking in assembly language or binary, but the operations are much more granular and so it requires a lot of operations to do express something as simple as a for-loop or a function call. I think the perfect AI might actually come up with a language closer to Python or JavaScript.

Mistletoe 1y ago

I don't know what vibecoding is, and at this point I'm too afraid to ask.

shric 1y ago
It’s fine to not know what it is, but what is the rationale for commenting that you don’t know? Why not just look it up? Or don’t, as you’re too afraid to ask.
bashtoni 1y ago
I wouldn't worry too much, no-one seems to be able to agree what it means anyway. Depending on who you speak to it can be anything from coding only by describing the general idea of what you want, to just being another term for LLM assisted programming.
OutOfHere 1y ago
The strict original definition of vibe coding is it is LLM writing code with the programmer never caring about the code, only caring about the code's runtime output. It is easily the worst way to use LLMs for code, and I think even coining the term was a highly irresponsible and society-damaging move by Karpathy, making me lose much respect for him. This coined definition was taken literally by managers to fire workers. In truth, for LLM generated code to be maintainable and scalable, it first needs to be speced-out super well by the engineer in collaboration with the LLM, and then the generated code must also be reviewed line-by-line by the engineer. There is no room for vibe coding in making things that last and don't immediately get hacked.
fulafel 1y ago
Here's the tweet where Karpathy coined the term. tldr; fast throwaway code from a LLM, where the human is just looking at the results and not trying to make maintainable code. > There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like "decrease the padding on the sidebar by half" because I'm too lazy to find it. I "Accept All" always, I don't read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I'd have to really read through it for a while. Sometimes the LLMs can't fix a bug so I just work around it or ask for random changes until it goes away. It's not too bad for throwaway weekend projects, but still quite amusing. I'm building a project or webapp, but it's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.

J_Shelby_J 1y ago

Writing rust and the LLM almost never gets function signatures and returns types wrong. That just leaves the business logic to sort out. I can only imagine that IDEs will eventually pair directly with the compiler for instant feedback to fix generations. But rust also has traits, lifetimes, async, and other type flavors that multiples complexity and causes issues. It also an in progress language… im about to add a “don’t use once cell.. it’s part of std now “ to my system prompt. So it’s not all sunshine, and I’m deeply curious how a pure vibe coded rust app would turn out.

762236 1y ago
Gemini has been doing a fantastic job for me for Rust

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SteveJS 1y ago

I think this is true -- especially for new code. I did this not knowing any rust: https://github.com/KnowSeams/KnowSeams https://github.com/KnowSeams/KnowSeams and rust felt like a very easy to use a scripting language.

xwolfi 1y ago
That seems a little bit dangerous, why not do it in a language you know ? Plus, this is not launching rockets on the moon, it's a sentence splitter with a fancy state machine (probably very useful in your niche, not a critique) - the difficulty was for you to put the effort to build a complicated state machine, the rest was frankly... not very LLM-needing and now you can't maintain your own stuff without Nvidia burning uranium. Did the LLM help at all in designing the core, the state machine itself ?
- SteveJS 1y ago
  Nah it was a hobby project because I was laid off for a bit. Rust's RegEx was perfect because it doesn't allow anything that isn't a DFA. Yes-ish, the LLM facilitated designing the state machine, because it was part of the dev-loop I was trying out. The speed is primarily what enabled finding all of the edge cases I cared about. Given it can split 'all' of a local project gutenberg mirror in < 10 seconds on my local dev box I could do things I wouldn't otherwise attempt. The whole thing is there in the ~100 "completed tasks" directory.
NitpickLawyer 1y ago
Really cool stuff, I appreciate you sharing this. Although, to be fair this is far from vibecoding. Your setup, at a glance, says a lot about how you use the tools, and it's clear you care about the end result a lot. You have a PRD file, your tasks are logged, each task defines both why's and how's, your first tasks are about env setup, quality of dev flow, exploration and so on. (as a nice tidbit, the model(s) seem to have caught on to this, and I see some "WHY:" as inline comments throughout the code, with references to the PRD. This feels nice) It's a really cool example of "HOW" one should approach LLM-assisted coding, and shows that methods and means matter more than your knowledge in langx or langy. You seem to have used systems meant to help you in both speed of dev and ease of testing that what you got is what you need. Kudos! I might start using your repo as a good example of good LLM-assisted dev flows.

exclipy 1y ago

The closest we got to vibe coding pre-LLMs was using a language with a very good strong type system in a good IDE and hitting Ctrl-Space to autocomplete your way to a working program. I wonder if LLMs can use the type information more like a human with an IDE. eg. It generates "(blah blah...); foo." and at that point it is constrained to only generate tokens corresponding to public members of foo's type. Just like how current gen LLMs can reliably generate JSON that satisfies a schema, the next gen will be guaranteed to natively generate syntactically and type- correct code.

esafak 1y ago
LLMs can use LSPs. https://en.wikipedia.org/wiki/Language_Server_Protocol https://en.wikipedia.org/wiki/Language_Server_Protocol
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treyd 1y ago
You already can use LLM engines that force generation according to an arbitrary CFG definition. I am not aware of any systems that apply that to generating actual programming language code.
koolba 1y ago
> I wonder if LLMs can use the type information more like a human with an IDE. Just throw more GPUs at the problem and generate N responses in parallel and discard the ones that fail to match the required type signature. It’s like running a linter or type check step, but specific to that one line.
- xwolfi 1y ago
  We have infinite uranium anyway !

nu11ptr 1y ago

Everything said is true without AI as well, at least for me. I don't hate Python, and I like it for very small scripts, but for large programs the lack of static type makes it much to brittle IMO. Static typing gives the confidence that not every single line needs testing, which reduces friction during the lifecycle of the code.

rvz 1y ago

Such extraordinary claims, require extraordinary evidence. Not "vibes" > It seems that typed, compiled, etc. languages are better suited for vibecoding, because of the safety guarantees. There are no "safety guarantees" with typed, compiled languages such as C, C++, and the like. Even with Go, Rust and others, if you don't know the language well enough, you won't find the "logic bugs" and race conditions in your own code that the LLM creates; even with the claims of "safety guarantees". Additionally, the author is slightly confusing the meaning of "safety guarantees" which refers to memory safety. What they really mean is "reasoning with the language's types" which is easier to do with Rust, Go, etc and harder with Python (without types) and Javascript. Again we will see more of LLM written code like this example: [0] [0] https://sketch.dev/blog/our-first-outage-from-llm-written-code https://sketch.dev/blog/our-first-outage-from-llm-written-co...

lvl155 1y ago

I can say with 100% certainty that they all stink at Rust. It’s laughably bad. Python, on the other hand, is surprisingly good.

energy123 1y ago
I scraped every comment on HN that discussed using Rust with LLMs and about half gave positive feedback, half negative feedback. Can you explain more why you've arrived at this opinion?
OutOfHere 1y ago
With Python, it scales better if the Python is well-typed, not so much otherwise. It's the best at Go imho since it has enforced types and a garbage collector.

timuckun 1y ago

It's been my experience that strongly opinionated frameworks are better for vibe coding regardless of the type system. For example if you are using rails vibe coding is great because there is an MCP, there are published prompts, and there is basically only one way to do things in rails. You know how files are to be named, where they go, what format they should take etc. Try the same thing in go and you end up with a very different result despite the fact that go has stronger typing. Both Claude and Gemini have struggled with one shotting simple apps in go but succeed with rails.

topato 1y ago
This is pretty anecdotal, but it feels like most of the published rails source code you find online (and by extension, an LLM has found) is from large, stable, and well-documented code.
- rafamvc 1y ago
  Claude code with rails is amazing. Should out to Obie for the Claude on rails. Works phenomenally well.
EGreg 1y ago
Basically it's like this: the more constraints you have, the more freedom you have to "vibe" code and if someone actually built AI for writing tests, catching bugs and iterating 24/7 then you'd have something even cooler
- delta_p_delta_x 1y ago
  > if someone actually built AI for writing tests, catching bugs and iterating 24/7 This is called a nightly CI/CD pipeline. Run a build and run all tests and run all coverage at midnight, failed/regressed tests and reduced coverage automatically are assigned to new tickets for managers to review and assign.
  - progbits 1y ago

brikym 1y ago

You could just leave it at "Typed languages are better."

adamnemecek 1y ago

They are also better suited for being ported to other languages, also unsurprisingly

itsafarqueue 1y ago

This generalises to “Agents respond well to red/green feedback loops”.

warrenmiller 1y ago

it aint great at c# i can tell you. this from grok yesterday: foreach (string enumName in Enum.GetNames(typeof(Pair))) { if (input.Contains($"${enumName}"))

woodruffw 1y ago

> I am managing projects in languages I am not fluent in—TypeScript, Rust and Go—and seem to be doing pretty well. This framing reminds me of the classic problem in media literacy: people know when a journalistic source is poor when they’re a subject matter expert, but tend to assume that the same source is at least passably good when less familiar with the subject. I’ve had the same experience as the author when doing web development with LLMs: it seems to be doing a pretty good job, at least compared to the mess I would make. But I’m not actually qualified to make that determination, and I think a nontrivial amount of AI value is derived from engineers thinking that they are qualified as such.

giantrobot 1y ago
Gell-Mann Amnesia [0] [0] https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect https://en.m.wikipedia.org/wiki/Gell-Mann_amnesia_effect
- woodruffw 1y ago
  Thank you! I couldn’t remember the term.
  - giantrobot 1y ago
    > I couldn’t remember the term. That's lethologica! Or maybe in this specific case lethonomia. [0] [0] https://en.m.wikipedia.org/wiki/Tip_of_the_tongue https://en.m.wikipedia.org/wiki/Tip_of_the_tongue
bravesoul2 1y ago
Why I only use it on stuff I can properly judge.
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muglug 1y ago
Yup — this doesn't match my experience using Rust with Claude. I've spent 2.5 years writing Rust professionally, and I'm pretty good at it. Claude will hallucinate things about Rust code because it’s a statistical model, not a static analysis tool. When it’s able to create code that compiles, the code is invariably inefficient and ugly. But if you want it to generate chunks of usable and eloquent Python from scratch, it’s pretty decent. And, FWIW, I’m not fluent in Python.

jbellis 1y ago

I'm really shocked at how slow people are to realize this, because it's blindingly obvious. I guess that just shows how much the early adopter crowed is dominated by python and javascript. (BTW the answer is Go, not Rust, because the other thing that makes a language well suited for AI development is fast compile times.)

woodruffw 1y ago
My experience with agent-assisted programming in Rust is that the agent typically runs `cargo check` instead of `cargo build` for this exact reason -- it's much faster and catches the relevant compilation errors. (I don't have an opinion on one being better than the other for LLM-driven development; I've heard that Go benefits from having a lot more public data available, which makes sense to me and seems like a very strong advantage.)

poink 1y ago

Typed languages are also better suited to IDE assistance and static analysis I'm a relatively old school lisp fan, but it's hard to do this job for a long time without eventually realizing helping your tools is more valuable than helping yourself

anupshinde 1y ago

I am comfortable with both Python and Go. I prefer Go for performance; however, the earlier issue was verbosity. It is easier to write things using a Python dict than to create a struct in Go or use the weird `map[string]interface{}` and then deal with the resulting typecast code. After I started using GitHub Copilot (before the Agents), that pain went away. It would auto-create the field names, just by looking at the intent or a couple of fields. It was just a matter of TAB, TAB, TAB... and of course I had to read and verify - the typing headache was done with. I could refactor the code easily. The autocomplete is very productive. Type conversion was just a TAB. The loops are just a TAB. With Agents, things have become even better - but also riskier, because I can't keep up with the code review now - it's overwhelming.

jjcm 1y ago

I've noticed a fairly similar pattern. I particularly like vibecoding with golang. Go is extremely verbose, which makes it almost like an opposite perl - writing go is a bad experience, but reading go is delightful. The verbosity of golang makes it so you're able to always jump in and understand context, often from just a single file. Pre-llms, this was an up front cost when writing golang, which made the cost/benefit tradeoff often not worth it. With LLMs, the cost of writing verbose code not only goes down, it forces the LLM to be strict with what it's writing and keeps it on track. The cost/benefit tradeoff has increased greatly in go's favor as a result.

WD-42 1y ago
No shade on Go but you kinda just said that the language has always looked like AI generated code and this works in its favor now because you don’t actually have to write it anymore. Funny, but not sure I’d consider that in Go’s favor.

OutOfHere 1y ago

The argument against Python is weak because Python can be written with types. Moreover, the types can be checked for correctness by various type checkers. The issue is those who don't use type checkers religiously with Python - they give Python a bad name.

rgoldfinger 1y ago

Totally agree. With ai coding, ensuring correctness is critical. Having types and compile-time checks helps a lot.

Reubend 1y ago

While I agree with the main thesis here, I find this extremely worrying: > I am amazed every time how my 3-5k line diffs created in a few hours don’t end up breaking anything, and instead even increase stability. In my personal opinion, there's no way you're going to get a high quality code base while adding 3,000 - 5,000 lines of code from LLMs on a regular basis. Those are huge diffs.

pablitokun 1y ago
Yeah imagine one of your colleagues doing this to your code base...
- a96 1y ago
  Imagine a reviewer that doesn't block that patch immediately. Of course, there might be some exceptions like if the codebase for some reason has some massive fixed tables or imports upstream files that may get updated occasionally. Those end up as massive patches or sets.
jongjong 1y ago
Yes. From experience, for a relatively complex system, 1k+ line PRs from mid-level devs without tests are almost guaranteed to have bugs; often nasty ones which can take many hours to identify and fix. I remember when I started coding (decades ago), it would take me days to debug certain issues. Part of the problem was that it was difficult to find information online at the time, but another part of the problem was that my code was over-engineered. I could churn out thousands of lines of code quickly but I was only trying to produce code which appeared to work, not code which actually worked in all situations. I would be shocked when some of my code turned out to break once in a while but now I understand that this is a natural consequence of over-complicating the solution and churning out those lines as fast as I could without thinking enough. Good code is succinct; it looks like it was discovered in its minimal form. Bad code looks like it was invented and the author tried to make it extra fancy. After 20 years coding, I can tell bad code within seconds. Good code is just easy to read; first of all, you already know what each function is going to do before you even started reading it, just by its name. Then when you read it, there's nothing unexpected, it's not doing anything unnecessary.

Myrmornis 1y ago

Python has static typing unless you don't add any types. The vast majority of reputable Python codebases nowadays use static typing rigorously. If you don't, you should. To enforce it when coding with an agent you can either tell the agent to run the type checker after every edit (e.g. via a hook in Claude Code), or if you're using an agent that has access to the LSP diagnostics then tell it to look at them and demand that they are clean after every edit (easy with Cursor, and achieveable in Claude Code I believe via MCP).

heavyset_go 1y ago
Why isn't the agent smart enough to recognize typed Python code existing in a project or detect that an explicit py.typed file exists?
- Myrmornis 1y ago
  In the case of Claude Code the hook feature is ideal for this so I could imagine the designers deciding that it is more appropriate to put the user in control. That said I think I do agree with you that -- given Python's fairly unique position of having good static typing but not requiring it -- the agents should default to running the type checker if they see it configured in pyproject.toml.
physicsguy 1y ago
> The vast majority of reputable Python codebases nowadays use static typing rigorously As judged by who? And in what field? I mean, if I look at the big Python libraries I use regularly none of them have types - Django, DRF, NumPy, SciPy, Scikit-learn. That’s not to say there aren’t externally provided stubs but the library authors themselves are often not the ones writing them
- Myrmornis 1y ago
  Yes fair enough my wording wasn't great. And we can add cpython to your list... But the provided type stubs for those libraries make the resulting user experience the same as if they had types. Does Django ORM have a good typing experience via stubs btw? I know that one's always been a challenge. Overall though my point was that the article, and most comments here, were completely misrepresenting the situation regarding Python. It's a statically typed language for those that want it to be. There's no need to attempt to run any code that hasn't passed a type checker. And it's an expressive type system; much more so than Go which has been mentioned in comments. However the fact that the standard library documentation doesn't have types is embarrassing IMO.

jongjong 1y ago

My experience suggests the opposite of what this article claims. Claude Code is ridiculously good with vanilla JavaScript, provided that your code is well written. I tried it with a TypeScript code base and it wasn't anywhere near as good. With JS, Claude has very high success rate. Only issue I had with it was that one time it forgot to update one part of the code which was in a different file but as soon as I told it, it updated it perfectly. With TypeScript my experience was that it struggles to find things. Writing tests was a major pain because it kept trying to grep the build output because it had to mock out one of the functions in the test and it just couldn't figure it out. Also typed code it produces is more complex to solve the same problem with more different files and it struggles to get the right context. Also TS is more verbose (this is objectively true and measurable); requires more tokens so it literally costs more.

randomifcpfan 1y ago

Here’s a study that found that for small problems Gemini is almost equally good at Python and Rust. Looking at the scores of all the languages tested, it seems that the popularity of the language is the most important factor: https://jackpal.github.io/2025/03/29/Gemini_2.5_Pro_Advent_of_Code.html https://jackpal.github.io/2025/03/29/Gemini_2.5_Pro_Advent_o...

whytevuhuni 1y ago
But isn't it the case that Python is vastly more popular than Rust? If Gemini is equally good at them in spite of that, doesn't that mean it'd be better at Rust than at Python if it had equal training in both?
- randomifcpfan 1y ago
  The study points out, “Python and Rust are the two most popular languages used by Advent of Code participants. This may explain why Rust fares so well.”
  - whytevuhuni 1y ago
    Ah, that makes a lot more sense. Thanks!
reflectiveattn 1y ago
[dead]

blurbleblurble 1y ago

I've had really good experiences with claude code + rust

rbalicki 1y ago

Folks here may be interested in checking out Isograph. In [this conference talk](https://www.youtube.com/watch?v=sf8ac2NtwPY https://www.youtube.com/watch?v=sf8ac2NtwPY), I vibe code an Isograph app, and make non-trivial refactors to it using Cursor. This is only feasible because the interface between components is very simple, and all the hard stuff (generating a query for exactly the needed data, wiring things up, etc.) is done deterministically, by a compiler. It's not quite the same principal OP articulates, which is that a compiler provides safety and that certainty lets you move fast when vibe coding. Instead, what I'm claiming is that you can move fast by allowing the LLM to focus on fewer things. (Though, incidentally, the compiler does give you that safety net as well.)

MutedEstate45 1y ago

The real win isn't static vs dynamic typing. It's immediate, structured feedback for LLM iteration. cargo check gives the LLM a perfectly formatted error it can fix in the next iteration. Python's runtime errors are often contextless ('NoneType has no attribute X') and only surface after execution. Even with mypy --strict, you need discipline to check it constantly. The compiler makes good feedback unavoidable.

seunosewa 1y ago
Printing stack traces generates a lot of useful context but it's not done enough.

throwawaymaths 1y ago

i find claude is very good with elixir, which is a dynamically typed language. i suspect strong conventions and value immutability help.

leric 1y ago

[dead]

levocardia 1y ago

Interesting...my experience has been that LLMs are generally better at more common languages (not surprising: more data exists in those languages!). So, my admittedly amateur vibe coding experiences have been best in Python and pretty vanilla web development setups. When I push LLMs to, say, fit advanced statistical models in R, they fall apart pretty badly. Yet they can crush a PyTorch or SciKitLearn task no problem.

reflectiveattn 1y ago
This. This is the most important thing to consider: the available corpus the model was trained on. Remember that LLMs are inferring code. They don't "know" anything at all about its axiomatic workings. They just know what "looks right" and what "looks wrong". Agentic and RL are about to make this philosophy obsolete on grand scale, but signs still don't look good for being any to improve how much they can "hold in their head" to infer what token to spit out next from the vector embedding, tho.

misja111 1y ago

My experience with Python and Scala so far is different. With Python the LLM's do a pretty good job. The code always compiles, sometimes there are some logical or architectural errors but that's it. With Scala, I have to give the LLM a super simple job, e.g. creating some mock data for a unit test, and even then it frequently screws up; every now and then it gives me code that doesn't even compile. So much for Scala's strong type system ..

jmfldn 1y ago
Less training data for Scala is the issue probably. Imagine how much Python is out there.
smrtinsert 1y ago
I've been asking it to spit out python all day long and it just flies with it. Ask all the LLMs most of them will tell you Python is the top if not preferred language.

solatic 1y ago

It's not so much typing that is valuable for vibecoding, but being able to give the agent hooks into tooling that provides negative feedback for errors. The easiest is typing, sure, because it's built into the compiler. But you can also add in static analysis linters and automated testing, including - notably - testing for performance. Of course, you have to tell the agent to set up static analysis linters first, and tell the agent to write tests. But then it'll obey them. The reason why large enterprises could hire armies of juniors in the past, safely, was because they set up all manner of guardrails that juniors could bounce off of. Why would you "hire" a "junior" agent without the same guardrails in place?

darksaints 1y ago
Exactly this. The ability of LLMs to write code is going to strongly depend on the availability and quantity of training data. But agentic coding is more than just LLMs, it is also the various abilities that give feedback to the LLM to refine the resulting code...and that is something that strongly typed and statically typed languages do so much better than their weak/dynamic counterparts.

sshine 1y ago

I've been vibe-coding for weeks in Rust, and it works great. I've been vibe-coding for a few days in Haskell, and I don't like the result. Maybe I am just accustomed to being ok with verbose Rust, while Haskell comes with a great potential for elegance that the LLM does not explore. Regardless, the argument that types guide the LLM in a very reliable way holds in both cases.

xwiz 1y ago
My experience with Haskell has been the same. The GHC provides stellar feedback, so the LLM is almost always able to bang the code into working order, but wow is that code bloated.

isodev 1y ago

Typed but maybe with the exception of the likes of Swift where Claude reveals just how complex and ambiguous the language can be. The lack of documentation and overly complex proposal documents also appear to overload the LLM context and confuse them.

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teiferer 1y ago

Nit upfront: Python is typed, just not statically typed. What dynamically typed languages lack in compile-time safety, the programmer must make up using (automated) testing. With adequate tests, a python program doesn't break more than a Rust or Go program. It's just that people often regard testing as an annoying chore which is the first thing they skip when vibe coding (or "going fast and breaking things" which is then literally what happens).

tonyhart7 1y ago
"a python program doesn't break more than a Rust or Go program" but it is tho, You literally can just give LLM to check LSP to analyze early it for you without write test to begin, Their LSP and Compiler is just that smart

Surac 1y ago

I have made the exact opposite with Claude and low level C. Claude is very good in writing classic c functions you need on a daily basis. I often wonder how much defensive coding it puts into the functions. I for myself let any code I write at least be read one time by Claude now

theusus 1y ago

I programmed my services in Python without any convention and I suffered a lot. Now, I do it in typed languages with a strong compulsory conventions and things are far more manageable.

tibastral2 1y ago

Try PURE functionnal programming, it's even better ;)

jrvieira 1y ago
try not vibe coding. it's even better

antirez 1y ago

LLMs also write good C, if well directed. My feeling is that this is not really about C or something inherent to Python (where I get not stellar results), but to the large low quality Python code bases that are out there. Basically my hypothesis is that, within the training set, there are languages with better examples and languages with worse examples. I found that to write better Python, prompt engineering goes a great length: especially stressing of not using not really needed dependencies, to write simple, avoid trivial asserts that are not really useful, and so forth.

zk108 1y ago

*Typed languages are better Fixed it

pyrale 1y ago

I'm not sure I agree with the author's conclusion. While python was never a great language for large codebases and it thrived because people with little development knowledge could get going pretty easily, a large part of its current appeal is the profusion of great specialized libraries which you would have to code yourself in other languages. I suspect vibe coding will not be a good fit for writing these libraries, because they require knowledge and precision which the typical vibe coding use probably doesn't show, or the willingness to spend time on the topic which is also typically not what drives people to vibe coding. So my conclusion would be that vibe coding drives the industry to solidify around already well-established ecosystem, since less of the people producing code will have the time, knowledge and/or will to build that ecosystem in newer languages. Whether that drive is strong enough to be noticable is another question.

jaynetics 1y ago
Then again, LLMs are well-suited to translate stuff, a relatively grunt work kind of task, so porting libs to your ecosystem of choice is a lot more feasible now. Perhaps there is a future where individuals can translate large numbers of libraries, and instead of manually porting future improvements of the original versions to the copies, just rerun the translation as needed.
- hugs 1y ago
  Yup, I recently started doing more development in Nim. I love the language, but the user community is (currently) small, which means the ecosystem of libraries available isn't as big. But LLMs are a massive equalizer here and has made it a lot easier for me to get things done with Nim.

lonelyasacloud 1y ago

So if for whatever reason it is better for vibe coding, then legacy code aside, why would anyone not use a technology that makes it a bit easier for them to understand what the AI is actually churning out on their behalf?

OldfieldFund 1y ago

Why not just tell the LLM in rules to strongly type and write guardrails and then: [tool.ruff] line-length = 88 select = ["E", "F", "W", "I", "N", "UP", "B", "C4"] # A good strict baseline ignore = [] [tool.mypy] python_version = "3.12" warn_return_any = true warn_unused_configs = true disallow_untyped_defs = true disallow_any_unimported = true no_implicit_optional = true check_untyped_defs = true strict = true

lysecret 1y ago
Yep this is pretty much what I do. I also added a /check command to explcicitly runn all those checks + pyright and ruff format.

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binarymax 1y ago

I’ve not had good success with vibing rust. It requires lots and lots of handholding and editing. Perhaps it’s because the model is always trying to do things from scratch. It does a poor job of finding crates and understanding the docs and implementation.

jelder 1y ago

Preference for typed languages may be the one and only good thing to come out of this bubble.

__mharrison__ 1y ago

Curious if anyone has done experiments where they compare output of Python vs annotated Python. Do type hints help LLMs?

hugs 1y ago

Nim might hit the sweet-spot here: typed, compiled, and Python-like. I wrote this [1] comment a few weeks ago: """ ... Claude Code is surprisingly good at writing Nim. I just created a QuickJS + MicroPython wrapper in Nim with it last week, and it worked great! Don't let "but the Rust/Go/Python/JavaScript/TypeScript community is bigger!" be the default argument. I see the same logic applied to LLM training data: more code means more training data, so you should only use popular languages. That reasoning suggests less mainstream languages are doomed in the AI era. But the reality is, if a non-mainstream language is well-documented and mature (Nim's been around for nearly 20 years!), go for it. Modern AI code gen can help fill in the gaps. """ [1]: https://news.ycombinator.com/item?id=44400913 https://news.ycombinator.com/item?id=44400913

infecto 1y ago

I can see this making sense purely from a tool chain perspective. If we’re are entering the age of treating code like cattle then it would make sense overly verbose and strict languages may benefit from it.

stared 1y ago

I have the same impressions. Typing helps a lot, and (I think) in a few ways - one is being a safe guard, second a constraint (so say, AI is less likely to create a clunky variable which can be a string, list, or a few other things), third - to prompt into writing solid code in general. I add one more step - add strong linting (ESLint with all recommended rules switched on, Ruff for Python) and asking to run it after each edit. Usually I also prompt to type things well, and avoid optional types unless strictly necessary (LLMs love to shrink responsibility that way). For example, see my recent vibe-coding instructions, https://github.com/QuesmaOrg/demo-webr-ggplot/blob/main/CLAUDE.md https://github.com/QuesmaOrg/demo-webr-ggplot/blob/main/CLAU....

jcmontx 1y ago

I reached the same conclusion. But unfortunately LLMs are not that good at C#.

seunosewa 1y ago
What did the LLMs get wrong most frequently when working with C#?
- jcmontx 1y ago
  They write code that don't build in the first or second shot. With Claude Code I gave it instructions to "fix tests" and it became so frustrated with them it started to "rm" the files lol
  - seunosewa 1y ago
    Providing agents with relevant documentation including code samples and API references should help a lot in your scenario. Relevant documentation helps a lot when you're working with obscure languages or libraries.

jcadam 1y ago

I've found most LLMs I've tried generate better code in typed, procedural languages than they do in something like Clojure. From the perspective of a primarily backend dev who knows just enough React/ts to be dangerous, Claude is generating pretty decent frontend code, letting me spend more time on the Rust backend of my current side project.

iLemming 1y ago
> generate better code in typed, procedural languages Better in what sense? I've been using Anthropic models to write in different Lisps - Fennel, Clojure, Emacs Lisp, and they do perform a decent job. I can't always blindly copy-and-paste generated code, but I wouldn't do that with any PL.

dragonwriter 1y ago

> I am managing projects in languages I am not fluent in—TypeScript, Rust and Go—and seem to be doing pretty well. > It seems that typed, compiled, etc. languages are better suited for vibecoding, because of the safety guarantees. This is unsurprising in hindsight, but it was counterintuitive because by default I “vibed” projects into existence in Python since forever [...] > For example, I refactored large chunks of our TypeScript frontend code at TextCortex. Claude Code runs tsc after finishing each task and ensures that the code compiles before committing. This let me move much faster compared to how I would have done it in Python, which does not provide compile-time guarantees. While Python doesn't have a required compulation step, it has both a standard type system and typecheckers for it (mypy, etc.) that are ubiquitous in the community and could be run at the same point in the process. I would say it's not just Rust, TypeScript, and Go that the author has a weak foundation in.

helle253 1y ago

My experience with LLMs in Rails has been... pretty bad. It isn't good at tracking 'context' (not in the technical token sense) and constantly gets lost in the sauce and doing weird stuff. Given Rail's maturity, i would have expected otherwise - there is tons of Ruby/Rails code to train on, but... yeah. OTOH, doing some side-project stuff in TS, and the difference is a little mindblowing. I can see the hype behind vibecoding WAY more.

dominicrose 1y ago

I can vibecode in Rust but I don't like the result. There are too many lines of code and they are too long and contain too many symbols and extra stuff. Just compare SeaORM with Ruby + sequel where you just inherit the Sequel::Model class and Sequel reads the table schema without you having to tell it to. It gives you objects with one method for each column and each value has the correct type. I was happy with Ruby's performance 15 years ago and now it's about 7-20x with a modern ruby version and CPU, one a single thread. AI is still helpful to learn but it doesn't need to do the coding when using Ruby. I think the same criteria apply with or without AI for choosing a language. Is it a single-person project? Does it really require highly optimized machine code? etc.

joker99 1y ago
Sounds like you really need to have a look at loco.rs! Given the right constraints, I’m fairly productive with CC

waffletower 1y ago

I have not found this to be the case at all. Type mismatches have been very common in Java, C++ and Objective-C inference output. I think there is complexity in what contributes to LLM suitability to programming tasks, and the nature and history of APIs relevant to the ask are a big part of that. Seems that the OP really loves their types, like many here, and this article is just more evangelism.

smrtinsert 1y ago

> Because of this, I predict a decrease in Python adoption in companies, specifically for production deployments, even though I like it so much. Most definitely not going to happen. Python is the language of the AI age and lot of ML/AI libraries do their reference or first release in Python.

irvingprime 1y ago

Vibecoding is bad coding. Always. Even if I take the headline as correct, so what? It's still crap code that will collapse into an unmaintainable mess sooner rather than later.

reflectiveattn 1y ago

The language using the fewest punctuation tokens is going to be the safest from most categories of hallucination, and give each context window the greatest usable space for vector manipulation headed into self-attention before the model suffers from "vector-clouded judgment" due to overcrowded latent space.