alt Hacker NewsIf AI writes your code, why use Python?
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Is there a blocker that would prevent future AI to write perfect assembler (for n architectures) in 1st pass?
Claude writes java pretty well, and faster than Rust. It's a great middle ground for some projects. I've switched back from Rust to Java for some things.
I don't know why you would use Python at all except for small iterative projects. If you hate java for some reason, there's Go...
As others have said, the main benefit with Python over Rust is library support especially with ML features. The other gap as I see it with Rust is the lack of native flexible UI support. The nice thing about Rust though is it can serve as a very fast and stable core for an app and offload specifics to TS and Python as their strengths allow, so you get the best of all worlds.
My current goto for desktop apps is Tauri, which give us a rust backend and TS fronted (usually React). Local ML features can be easily loaded as a python sidecar. Production bundling can be a little challenging but it seems to work well so far.
Sidenote: Golang is also an amazing language for LLM use, I generally do most of my "infra" stuff in Golang over Rust, but either work fine most of the time.
Two things to consider: - When reading generated code, which programming language would be the most readable to you? - Which programming language guides AI to write correct code using language features or guardrails?
There you will find your answer.
I find if I ask most LLMs to write a self contained script/utility, even in codebases that are 90-100% written in some other language most will default to using python for it, or sometimes bash.
Usually those kinds of utility scripts are one-shotted without any further input from me, and once they're there and doing what I need I usually don't bother converting them to whatever I would have written them in otherwise (bash would be my usual preference for really small scripts, typescript or rust for bigger utilities, I hate writing python but reading it is fine... kind of).
I had agents code up an app for me in Swift a while back and the entire experience was so much better than your typical Python experience. The agents took full advantage of the compiler and static typing. There were far fewer bugs than expected.
Because the SWE benchmarks for LLM coding are done on python code bases, hence you are likely gonna have superior results
I build all my projects with Rust and Typescript (https://github.com/brainless). I had started learning Rust around 2023 but was progressing very slow. Since I left writing (or even reading) code line by line about a year ago, I build exclusively with Rust and Typescript. API types are generated from Rust. All my projects have a shared-types folder with a utility to generated Typescript types. I have a template that I use for each of my projects: https://github.com/brainless/rustysolid.
I am from a Python background (11 years or so), PHP before that and C/C++ in college days. Rust works very well with coding agents. The amount of code in training data may be less but I would rather have the agent fight the compiler. Given that OpenAI and Anthropic seem interested in Rust, chances are that there is a ton of synthetic code generated with Rust.
I haven't read the article (because I hate Medium) but I reckon the biggest reason why LLM-assisted projects use Python is because there is a metric buttload of python code on the web to be slurped up and used as training fodder.
I'm still not sure. Would love thoguhts on this.. but in this new ai world we are in... is it better to go fullstack typescript? or go with proven mature frameworks? .net, ruby, django, etc? Seems TS is moving fast but maybe its time to not reach for the shiny object and stick with proven tech? or in 5 years will we regret it?
Why not use AI to speed up the Python runtime? V8 showed what focused engineering can do for JavaScript, and Astral showed how much room there is to improve Python tooling. The same tricks may not apply directly, but AI could definitely accelerate the work.
In my case: AI might write the code, but I have to architect the system, read the code, iterate and learn from it. Validate whether an approach makes sense, whether the chosen dependencies make sense, whether the testing is adequate and covers known failure paths ... good luck if this is a language and ecosystem you are not proficient in.
I still use TypeScript because I know it best. When AI makes a mistake, I can find the bug much faster. For me, the speed of writing code doesn't matter as much as the speed of fixing it.
Python is incredibly readable too. I can scan through LLM Python changes in minutes instead of hours of other languages.
I have been wondering on a similar thing; am looking for feedback:
There are many existing, often mature, third-party software libraries or solutions that a new project could use but which hide the internals, including how the data is organized behind the scenes*. Vibe-coding for the specific project requirements, instead of using the pre-existing third-party libraries, is now becoming a feasible option. The latter may be simpler (no features beyond the actual need), more flexible (easier to add new needed features), and the data/model behind could be more accessible.
Looking for feedback on pros/cons and experiences along this.
* I care for the data as it is can be longer-lived than the code itself.
Thanks.
"The Python ecosystem is increasingly a Rust ecosystem wearing a Python hat"
If anything this is a reason to keep using Python.
If you're using GenAI, you should go through the process of selecting an optimal tech stack for each solution, but also take into consideration that Claude and other services probably the most knowledge of python, javascript, and typescript with go, rust, java, and c# following closely behind. Consider what you're building and what elements of the tech stack is optimal for your problem-space.
I don't know rust at all and I've built three applications using it with Claude because it has speed and correctness built-in.
I use Typescript for 90% of the things I build. For web development I've used a number of tools, but mostly react, nextjs, or raw html/css/js. But if I were building an enterprise application I'd consider my team and whether opinionated (Angular) was optimal over flexible (React).
Each project should consider its own optimal tech stack.
So you have a chance to be able to read the absurdly barroque code AI produces.
Better question is why use any code? Generate random functions and select based on measuring the distribution of output of these functions against metrics of interest. A pure black box of instruction that is more performant than any verbose code or algorithm we could come up with, because all we select for is performance above all. Directed evolution essentially of the codebase, generated through mutation and selection, just like everything else on planet earth.
Bullshit article. AI is not meant to be a black box, you just spit at it and it'll generate you a whole app and you don't even understand a single line. That WILL eventually fail. There was an article here some time ago where someone described it pretty well "use AI as autocomplete on steroids". Therefore, use any language you can actually debug well and know well and use AI as a tool, not as your replacement. And don't use it to port your electron app to rust if you don't know rust, Jesus.
Thats exactly what i did with https://panel-panic.com
The ideal language for AI coding:
1. Type safety as basic guard rails that LLM output is syntactically and schematically correct
2. Concise since you have to review a lot more code
3. Easy to debug / good observability since you can't rely on your understanding of the code. Something functional where you can observe the state at any moment would be ideal.
4. A very large set of public code examples across various domains so there's enough training data for the LLM to be proficient in that language
5. A large open source ecosystem of libraries to write less code and avoid the tendency for generated code to bloat
It's basically all the same things you look for in general. I think TypeScript scores high here but I'm curious if anyone knows of a language that fits these criteria better.
This seems to assume that all there is, is systems software, tools and frameworks. Why ignore the elephant in the room - business / enterprise / line-of-business software? The case for Rust, Go, Gleam and Zig vastly changes for these versus Java or C#.
Assuming you are thinking about software architecture and looking under the hood, you are likely to be reading much more code than before. Python is really nice on the eyes and you can easily get a good grasp of what the code is doing. Plus, it's dynamically but strongly typed, so what you see in the code is usually what you get.
I think the rule of thumb is to use the tool that is right for the job and that you are going to be able to understand the output.
I think it is an interesting question what kind of programming language one needs for an era of agents. It is clear that the programming language that was designed for humans is not necessarily the best for AI-driven software development. I guess the qualities one would want is some formal correctness guarantees, high performance. A question is whether this language is Rust or it is possible to design a better new language.
Most of the article makese sense but what is this supposed to mean? "Native Rust binaries are hostile to serverless runtimes" . I don't think that is true.
What are some concise languages that are well received by humans (on par with Python)? Token efficiency might be a marked advantage.
Clojure comes to mind at least.
Isn’t the answer usually - because the same ai said python is the right language for it?
Honestly I am in the exact same boat thinking why I don’t write in C if Claude is writing it. However I chickened out thinking if support for ml model or llm based flows doesn’t exist in c then it will be time consuming to go to python then.
Rust is the way!
Didn’t Tencent do a study comparing AI performance across about 20 languages showing that Elixir was the top performer?
Yeah, last year I discovered that AI writes better rust than C, so I switched to rust and it made some quick good code that it couldn't do in C.
But when I wanted to optimize and edit and reorganize bthe code it was difficult, so I did a rewrite in C and it was lighter and faster and simpler and less headache.
C for humans, rust for AI.
AI/ML stuff: Python
Personal: Rust/Go based on criticality of being able to glean code quickly, or memory usage, etc
Python has during the recent years become unnecessary complex and especially the type hints system is so dumb and already have a lot of legacy syntax that confuse AI agents.
This is a fairly crap post and the reasoning isn't sound but somehow the conclusion is still somewhat correct.
You do want to use Rust with LLMs.
The reason you want it is simple, it's more constrained.
LLMs thrive on constraint and drown in freedom.
The further you can constraint the solution space the more likely you are to end up with a solution you like/is actually good.
Rust has several properties that make it really good for LLMs:
* Really robust type system that is also very expressive, if guided LLMs can implement most of the invariants in types which substantially increases the chances of success.
* Great compile time errors, the specificity and brevity (vs say C++ template expansion) means token efficient correction of syntax and/or borrow mistakes etc.
* Protection against subtle errors at compile time, namely data races and memory safety issues.
* Great corpus of well designed code and patterns, higher quality on average than some other ecosystems more favored by begineers/mass-market programming.
* Stdlib is strong, small-ish number of blessed crates.
* Context friendly, type signatures, errors, etc are all dense information.
* Also bias towards compile time checks means less runtime tests which means less toolcall time (and less tests needed overall) which in turn makes the process a ton faster.
I have been continually using Rust, Python and Kotlin since ~Jan this year and keeping track of my thoughts and I increasingly bias towards Rust now where I would have previously chosen Python or Kotlin instead just because I am lazy and I prefer the tool that the computer writes better so I have to write less lol.
Because AI creates unmaintainable messes in any language, and ergonomic ones help humans clean up.
You can also use Julia. It is both easy for humans to write and read and for AI to generate because of the sane and powerful type system.
However, I expect that in the future some new language will take this role of dual use.
Really agree. Python is popular because it's easy for human to implement. But now if the coder becomes AI, then Rust would be preferable for agent, just like Python for human. In addition, it brings better performance.
For me, whether it's AI or my own handcrafted artisanal code, the choice of language comes down to what has the least friction. This means I turn to vite/react for a lot of frontend requirements, and that the backend will be in nodejs or python, because those are easier for me to debug than writing an equivalent application in C++ or Rust.
Great question. And I don’t think that Python, Ruby and PHP have a good answer. Scripting languages cater to human weaknesses. The 10-100x perf cost was never really worth it but now it’s impossible to justify.
The article applies to a narrow case of a totally green field application that's going to be completely vibecoded. This is the only case where you reasonably can be indifferent to what the language is, and so you can abandon familiar Python and go with unfamiliar Rust. (If you _are_ familiar with Rust, the point of the article is moot.)
This "fair weather development" approach feels very risky if that application is going to be exposed to any serious usage. There WILL be a situation when things break and the AI will be powerless to fix it (quickly) without breaking something else in a vicious loop. There WILL be a situation where things work fine and tests pass with 3 concurrent users but grind to a complete halt with 1000 because there is something O(N^2) deep in the code. And you NEED a human to save your day (which requires also proper architecture for that to be possible in the first place). If you don't plan for this, and just hope for the best, then you are building nothing more than a toy. And if you plan for this, then it matters again what the language is, and whether your team is proficient in it.
Or maybe I too old fashioned or too behind the state of the AI art...
For the utilities I write it is faster to iterate without having to compile. When I get to the point where I'm done adding changing features, and performance is an annoyance I can always ask the AI to "rewrite this in Go". (I've never gotten to that point.)
Nice perspective on languages in the AI era. I think AI should be used to build best performing and highly scalable software systems.
Agreed. People should just use JavaScript since it's the one with the largest training set.
Python is rather a UI for human logic comprehension. A mathematical notation of logics. Not a code to drive computer.
And prompt does not replace that.
So I can fix it when it breaks. I don’t understand anyone shipping real code without human review.
Give it 2 years, the ‘Blame the AI ‘ incidents will increase. Like an unfaithful partner you’ll always return to it
>Smaller languages like Zig, Haskell and Gleam don’t have the same quality when AI-generated (for now).
GPT 5.5 writes good haskell.
> You used Python or TypeScript because[…]. because Rust, Go, C++, and many more would give you 10–100x the performance, but you paid for it: […] a build system that fought you.
I would argue I spent more time fighting the TypeScript build system than Rust’s.
But up until recently I only used either just often enough to never remember what magic configuration needed to go in my tsconfig.json and package.json to get TypeScript to work.