Comment by dmit
7 hours ago
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.
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.
> 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?
1 reply →
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.
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.
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
10 replies →
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.