Item — HN Reader

Have you ever thought "Ugh, this bit of Python code is running much slower than I expected on my computer. Wonder if anyone has written a native library for this"? That's probably the closest use case for someone who matches your description -- a language that is much more ergonomic, much more 'modern' feeling (in all the good ways), while still extremely compatible with C.

As for the language itself, it's going to be more verbose than your Python code. Cons: you'll have to spell out a lot of things that you thought were obvious assumptions. Pros: you will be able to look at a page of code and know with a great degree of certainty that there are no hidden gotchas. No monkey patching, no __init__. Basically, it just does what it says on the tin.

And finally, about the std lib and batteries: there's HTTP(S), compression algorithms, hash algorithms, RNG, I/O, the basic data structures you'd expect, JSON. Third-party libraries, if you choose not to vendor, are handled by including the repository url in a file (also automated by a CLI command), and then adding it to the build script (not automated). The `zig` command handles fetching and ensuring sanity, but otherwise assume a bit of elbow grease will need to be involved.

3 comments

That's actually a great argument for Nim[0]. Easy interop with C, native-speed performance, and a syntax very close to Python in both readability and how quickly you can get something working.

Batteries included, automatic memory management without a conventional GC and metaprogramming - is a really cool combination.

[0] - https://nim-lang.org/

Aggh if only its LSP was better! I have always run into issues when using Helix with it (it kept crashing), and I'm absolutely spoiled by good LSPs in other languages :(

Wish I had the time and skill to actually contribute to the LSP, if you have ever used Nim it's a seriously underrated language.

Araq the Nim creator is working on a rewrite of the Nim compiler called Nimony that'll become Nim 3.

It's making fast progress and will provide the basis for a proper LSP! Nimony already supports incremental compilation and parallel which are key pieces for good fast LSP.

it is my second choice next to Zig and does have a lot of cool features, for sure.

The nice thing is that all these languages feature easy C interop so you can use a C FFI as the interface between them if you want to experiment with, for example, writing a module in Nim

Rarely. Most tinkering tasks just don't have enough heavy duty computation in them to as much as strain a modern CPU. And most of the rest are covered by packages like numpy or pytorch.

For the rare exceptions, I make a C lib and call into it to get my numbers crunched. I get that Zig is a viable replacement for C there. But I don't see it replacing Python.

And if you really need more performance (or, more often, fast startup times), Go gives you 90% of the speed with 30% of the effort. Rust if you really want to squeeze everything that can possibly be squeezed of that CPU.

Zig gives you more control than Rust, which should theoretically lead to a higher performance ceiling.

There's not much magic in Zig. Keep hitting goto-definition and you can eventually see the OS switch statements and syscalls.

Neither Rust nor Zig give you any more control than the other. If you want raw pointers or inline assembly, Rust supports that.

Technically true, but unsafe Rust is much harder to deal with because it makes assumptions (sometimes very non-obvious) regarding aliasing that Zig does not. You might think you can avoid problems by using T* rather than T&, but every accessible local is effectively a T& so it's still very easy to do something you're not supposed to.

> every accessible local is effectively a T&

No, I'm not sure where you heard this but this is certainly not the case. If I have an i32 on the stack in Rust, there are no aliasing invariants that need to be upheld. You do, in fact, avoid problems in unsafe Rust by just using raw pointers instead of references. The idea that "unsafe Rust is harder to deal with than {Zig, C, C++}" is a long-outdated notion from old versions of Rust, before there was a dedicated way to produce a raw pointer without first producing an intermediate reference.

Go does not give you control. Much of the speed comes from choosing the right algorithms - including memory management. In Go you have GC, you don't have that in Zig.

Linked lists are a lot less slow if you use Arena allocation around your hotspots and make sure to allocate space for as many as you thing you need, since they will be carved out of a contigious block of memory and will stay in CPU cache.

Golang also requires you to write more code, as it lags Zigs try operator.

that’s not what the benchmarks say about Go, and based on multiple reports, Rust does not scale well into large codebases, which eventually become brittle and very difficult to change

Zig is a return to “no magical effects,” except with reasonable safety

I would be very surprised to see a large Rust codebase being harder to maintain than a large Zig codebase. The former makes it much easier to maintain invariants at scale.

Well, you could go ask Richard Feldman, who I believe cited that reason to rewrite the nascent Roc language from being implemented in Rust to Zig, or anyone else who is moving from Rust to anything else. I've seen multiple people at this point complain about the scaling issue with Rust; the larger the codebase, the more you end up fighting the compiler before anything will actually build.

Note that it doesn't matter if the compiler is correct about its claims; if the language doesn't actively discourage patterns that produce this outcome at scale, then the language does not scale, end of story.

The trend is basically either linear or exponential: as more LOC of Rust are added, the greater the percent of total time you spend fighting the compiler to get a successful build, especially in a team context (which is exactly what gets you to >1M LOC). Solo devs can contain the whole design in their minds and may not run into this issue as much; the problem specifically occurs on teams where the mental model MUST be fractured by necessity, and this results in "distributed knowledge of magic" that ends up constantly breaking.

Perhaps this explains WHY there aren't that many Rust projects done by more than 1 developer that approach that many LOC.

With Rust macros it's also possible to bolt on proper design by contract into the language.

Unless you enforce those macros somehow in a team setting, someone's going to forget to use them, and then you're still stuck with the original problem.

Neither has been battle-tested at the relevant scale.

What kind of scale are you thinking of?

By the time C++ and Java were as old as Rust is today there were thousands of programs that over 1MLOC that had been maintained for at least five years. Rust is a rather old language, yet I doubt there are even hundreds of Rust programs over 1MLOC.

Link to said benchmarks?

[flagged]

You are entirely right here, you're also incredibly rude. Please don't bother replying when the only thing you're actually doing is being condescending and spreading negativity

[flagged]

> Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.

Rudeness may be acceptable elsewhere. Not here.

> For the rare exceptions, I make a C lib

The problem is that most people using Python don't have enough expertise in C to do the same.

It also kinda destroys the argument that Python is good if your solution for performance is to use a different language alongside it.

The argument is that the ergonomics of using Python are worth the squeeze of learning two languages. Are the ergonomics of using Zig really enough to justify replacing Python on the happy path, or would it end up replacing just C?

I find Python extremely unergonomical. Sure, it's nice to read (pseudocode, yay!) and sure, its library ecosystem is beyond amazing.

But... the LSPs I've tried (and I've tried a bunch!) are all atrocious: false positives and false negatives galore. Perhaps I'm spoiled by LSPs from languages with better type systems. Our code is strewn with (to me) mysterious comments such as `# NOQA 1234` which my colleague uses to make his Python tooling work with the codebase. I'm used to languages like Elm or Gleam in which a LSP error means there is an actual problem with your code, and a lack of a LSP error means the code will compile and run.

Even if you are fine with Python's speed, its memory consumption DOES effect things and can be an extraordinary pain when you need to fit the result of your tinkering in any sort of constrained environment.

By the time I’m memory constrained even on my laptop the processing cost of whatever I’m doing has gone beyond shoving it in the first scripting language I can find. Every device I write code on has at least 16GB RAM - most of them are 64 or 128

Not to mention that where heavy computation is required, Python often has libraries that are much, much faster than anything you can quickly hack together in C or Zig.

As long as you can express everything you need on the library's terms. As soon as you write a Python loop, your performance plummets.

Only if you doing something thousands of people has done before. Anything new, even very simple and you are on your own and Python is 100x slower than naive C implementation on many tasks.

Last little project I remember is writing a solver for a puzzle game my friend published. Python just doesn't work at all for such tasks.

I think you are wrong about speed of those libraries as well. In my experience naive code designed for a specific task beats highly sophisticated general code and it doesn't take a rocket scientist to get huge speed-ups over some well established fast library.

I don't think performance has got much to do with tinkering. IMO the real benefit of Zig is you get the flexibility of C with the ergonomics of a modern language.

I like Python as a tool language, and I am very impressed by projects like Micropython, but you always eventually run into a wall. I.e. you are never going to write a compute shader in python, but I assume someone is going to try.

I think the programmer should meet the hardware in the middle, and Python has a few too many layers of indirection to do this well.

> I don't think performance has got much to do with tinkering.

Yes, in general, but also there are cases when you realize you can, idk, parse a CSV file in 0.2 seconds instead of 200 seconds. That kind of improvement unlocks a new level of tinkering.