alt Hacker NewsIf AI writes your code, why use Python?
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Agreed. People should just use JavaScript since it's the one with the largest training set.
>Smaller languages like Zig, Haskell and Gleam don’t have the same quality when AI-generated (for now).
GPT 5.5 writes good haskell.
Also easier to ship a binary like a cli
Honestly the bigger question is why we still write glue code at all. Let the agent orchestrate.
The most common languages in the training corpus will output the most reliably.
"Rust, [...], a build system that fought you"
I started using Rust in 2018 and I've never used a build system that fought me less, ever, before or after.
I stopped reading after that sentence.
I dislike Go but I have to admit, it's a great language for AI generated code. Simple enough, it compiles quickly and it performs meh-well enough for most applications.
One of the reasons I dislike Go is because it's easy for most engineers to write really low grade code with it. But AI agents would probably not write the best code in any language anyway, so not much is lost.
if AI is generating text for you, why type?
A somewhat contrarian/pessimistic view: The hardest thing in any future of LLM generated code is going to be the verification step, and especially types of verification that require humans which are going to be the most expensive.
Therefore the "best" language is going to be whatever makes it easiest for humans to detect bugs, bad design, or that the "wrong thing" has been developed.
I stopped reading as soon as the claude C compiler was mentioned and it was claimed it can compile big projects. We all seem to exist on a different plane of reality
Cute interesting take but I feel like it misses the point. Specifically, this makes sense where performance is necessary. Many projects have been written in suboptimal languages because the writers didn't want to learn lower level languages.
Still, not ALL projects benefit from such an approach and there are times when yes python is the right tool. Not just due to readability of humans but the other qualities that make it really good for small, iterative apps.
My take has never changed. Knowledge is cheaper than ever, but wisdom is as rare as ever. This is a great example of misunderstanding the former for the latter
The LLMs just churns out non-idiomatic slop in any language.
It doesn't matter if the 800-line if statement is able to use pattern matching.
There's been a lot of progress on making coding agents able to solve problems when they can easily evaluate in a closed loop, we desperately need something similar for controlling complexity and using relevant abstractions.
Because LLMs fuck it up near-constantly and I need to review it
… because model tool calls is non-standard, so Python as the only tool call available works wonders
(Joke but also not a joke)
Interesting question.
AI doesn't really write code for me, but I do use them to brainstorm/ask questions. Though, I do not use Python. I have never been a fan of the language. I still think Python is a perfectly serviceable language, but it would solve no (important) problems I have ever had better than any other language.
I can see why Python is appealing to many people, and I applaud Guido for all the work and oversight over the years, but Python lacks a lot of the things I like in a language.
Because I can understand and edit that code by hand if I need to.
I stay for the libraries
people don't write python because of the language. Some do but that's not the main reason. They do to utilise tools only exist in the ecosystem. AI changes nothing.
As always, "it depends."
I'm using coding tools to build a complex media-intensive application. The approach I'm taking is to build a _reference implementation_ in Python, which is in its design specifics, constrained to use patterns which transliterate into the actual deployment targets (iPadOS/MacOS/Web).
Why start with Python?
Because I can read it, reason about it, and run it, trivially, which are Good Things for the reference. I intend to have multiple targets; I'd rather relate them to a source of ground truth I am fluent in.
For what I'm doing, there is also a very rich set of prior art and existing libraries for doing various esoteric things—my spidey sense is that I'm benefiting from that. More examples, more discourse.
I'm out of the prediction business and won't say this is either a good model for every new project, or, one I will need in another N months/years.
But for the moment it sure feels like a sweet spot.
Ask me again though, after the reference goes gold and I actually take up the transliteration though... :)
I had gpt-5.5 translate microgpt.py into a C++ version recently. I had to steer/convince it to use data oriented design to avoid excessive pointer chasing, but the end result was as expected: Now 500 LOC instead of 199, but speedup was 100x. That speedup is definitely worth doubling the line count. And frankly, modern C++ can read very nicely, even compared to Python.
you still need to look at the code oneday so id say c++ still would be a preferred target language even for ai. i know i hear a lot about rust but im still getting the idea its a niche language overall. i know people love it and point out its advantages, but sometimes good enough is good enough (i.e. c++)
If you can use Python, why have AI write your code? :-)
Slop begets slop?
a) Python (and Node) comprise the largest training set for all the models, so you are likely to get way better accuracy, especially with local models
b) Python code is easier to introspect, and set up test harnesses around. And also extend in agentic frameworks
c) LLMs are really good at translation. I can give it python code and it can translate it into C.
Its unbeliebale that there are people possessing these types of thought processes, --- If AI can talk, why speak?
> Go and ... strong type systems
Lol good meme
> The strongest argument for Python and JavaScript was never the languages themselves. It was the ecosystems
That's already a glaring mistake. People could say perl's CPAN is great. Well, it did not save perl from declining in the last 20 years.
> The Python ecosystem is increasingly a Rust ecosystem wearing a Python hat.
Without statistics to prove this, this claim is useless.
Also, depending on Rust isn't that strange if a language is based on ... C. The only way I would disagree with such an argument were if Python were written in Python. But since it is syntactic sugar over C - just like ruby or perl are too - the argument to use Rust here is simply not different to using C. Perhaps Rust is better than C, but it is not fundamentally different. Whether Python were written in Rust or C is not a functional difference here.
As for AI becoming our new Overlord: I honestly do not want to depend on US mega-corporations. I am not disputing the fact that AI has objective use cases. I am objecting this herd mentality of everyone putting an AI chip into their brain now.
Damn AI slop zombies everywhere - it's like in the old B movie "They Live". But with less entertainment value than that. If they chew bubblegum then it is to slop up everything, not to kick ass.
Why use any general programming language at all? Just write it in assembly or binary. Skip the middleman bro
Let's go through some of the arguments, in no particular order:
> Klabnik vibe-coded a new language in Rust, therefore Claude + Rust = Good.
I argue the inverse -- Rust, being an ML-family language, is well suited for parsing, and language design (I know! Shocker!). In more moderate translation -- ML-style languages are good for parsing, interpreting and compiling code. Claude is not the magic here -- ML is.
I would also add that I've had decent success vibe-coding+human-coding Haskell (contrary to the article). My experience is that if I can hand-write a rich set of types (blessed be IxMonad), I can throw Claude to fill in the blanks for the implementations. If I can design the data structures that make the program tick, bridging them is something Claude is awesome at. Again, no surprise -- it's intern-level work.
The key distinction between C, Zig and Rust is that Rust is designed around types. C and Zig are more memory-oriented -- they really see most of your program as flat memory and you can kind of shoehorn a little bit of data layout in that flat memory. While this offers a large amount flexibility, this philosophy isn't well suited for proving out correctness. But again -- this doesn't mean they don't have a spot.
When I was a junior at Tesla, I used to joke that senior staff had a VMs in their heads, because that's really how you analyze C programs -- you try to execute it in your head, with interesting inputs, but that's about it. Claude's head-VM is quite fuzzy and often makes errors.
With Rust, if you design your type system, you prevent yourself from making dumb mistakes. Swap out "yourself" with Claude here and it's the same story.
I've yet to see Claude design really nice type systems, fwiw.
But the point is -- Claude is the enemy of beauty and correctness -- it's up to the SWE to design a type-system which will prevent it from doing so. To be clear, I obsess over type-systems personally, but that's not the only way -- incredibly rich, comprehensive, huge type systems, fuzzing, Antithesis, proptesting are all things you can do to minimize the impact of slop, and those are all valid things to do.
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> Code is not written by humans therefore it doesn't matter that you don't know Rust.
Wouldn't say this was explicitly stated, but I definitely smelt this undertone throughout the article. If you don't understand the language you're reading, how can you understand whether the code in front of you is correct or not? If you have a systems engineer sitting across you to clean your PRs up, you can pass that responsibility onto them, but what about when they give their two weeks?
If all you know is Python, chances are you're going to make better software in Python than in Rust. Stick an `Arc<Mutex<T>>` everywhere and chances are your code will be slower, as a matter of fact. Use If you want to learn Rust, please join us! But if all you're trying to do is vibe-code better code -- do it in the language you know and can actually debug when shit hits the fan.
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> Anthropic C Compiler
It is impressive that Claude is awesome at taking existing code and rewriting it, this is certain, but I'd like to repeat the exact same rhetoric that many have given -- rewriting =/= original authorship. Awesome, we have a C compiler, but we already had one, and we just rewrote it? Seems like a little bit of wasted electricity.
To build on top of this, I am really happy that Bun is exploring Rust, and the Claude rewrite is truly impressive, but quite surprising at times, preserving strange anti-patterns (my name being said anti-pattern, teehee): https://github.com/oven-sh/bun/blob/ffa6ce211a0267161ae48b82.... It's hard to determine why Claude decided this -- I assume a really strict input prompt.
Do note that the current stage of that PR is much better than what it was at the state of that commit, and obviously Jarred isn't merging blind slop, but that is still human-driven by someone who has an understanding of their product.
My bet is actually that _rewrites_ of already-functioning, well-tested code, are likely to be more common as time progresses. I think that's what Claude is really awesome at, and I think Claude can often achieve 80-20 improvements through rewrites. Again, Claude alone will not be a silver bullet -- it won't generate data-oriented programs if the source material wasn't data-oriented. It won't optimize for cache coherency, if the source didn't, but moving from Python to Rust alone, with more-or-less the same code structure, you're likely to see improvements by virtue of common operations being memory-coherent and avoiding the GIL and so on.
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> A C compiler written in Rust used to be a graduate thesis. It isn’t anymore.
Come on, this is disingenuous -- a simple C compiler is a 1-day long project. LLVM is a graduate thesis (and for good reason). Copy-pasting prior-art is academic dishonesty and Claude does a lot of that.
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For transparency: I work with Noah.
EDIT: Wanted to add that not a single line of my comment was AI generated.
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This idea is already being taken to the next step in labs; why generate code?
When I run a game I don't care of the dev used C or whatever. Only programmers care about the syntactic representation.
I need the machine code/byte code patterns/geometric/color gradient data.
Eventually Python will be what you see on screen but no cPython interpreter program as we know it will be running
The model will have an internal awareness of the result to return without running an actual REPL
https://dev.to/zijianhuang/prompt-to-ai-generated-binary-is-...
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Because once you leave Python or JS the quality of LLM-produced code degrades catastrophically.