Distributions.jl
Nim
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Distributions.jl | Nim | |
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6 | 346 | |
1,070 | 16,060 | |
0.9% | 0.8% | |
7.6 | 9.9 | |
5 days ago | 3 days ago | |
Julia | Nim | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Distributions.jl
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Yann Lecun: ML would have advanced if other lang had been adopted versus Python
If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.
This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.
If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).
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Don't waste your time on Julia
...so the blog post you've posted 4 times contains a list of issues the author filed in 2020-2021... and at least for the handful I clicked, they indeed have (long) been sorted. e.g., Filed Dec 18th 2020, closed Dec 20th
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Julia ranks in the top most loved programming languages for 2022
Well, out of the issues mentioned, the ones still open can be categorized as (1) aliasing problems with mutable vectors https://github.com/JuliaLang/julia/issues/39385 https://github.com/JuliaLang/julia/issues/39460 (2) not handling OffsetArrays correctly https://github.com/JuliaStats/StatsBase.jl/issues/646, https://github.com/JuliaStats/StatsBase.jl/issues/638, https://github.com/JuliaStats/Distributions.jl/issues/1265 https://github.com/JuliaStats/StatsBase.jl/issues/643 (3) bad interaction of buffering and I/O redirection https://github.com/JuliaLang/julia/issues/36069 (4) a type dispatch bug https://github.com/JuliaLang/julia/issues/41096
So if you avoid mutable vectors and OffsetArrays you should generally be fine.
As far as the argument "Julia is really buggy so it's unusable", I think this can be made for any language - e.g. rand is not random enough, Java's binary search algorithm had an overflow, etc. The fixed issues have tests added so they won't happen again. Maybe copying the test suites from libraries in other languages would have caught these issues earlier, but a new system will have more bugs than a mature system so some amount of bugginess is unavoidable.
- The Julia language has a number of correctness flaws
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Does a Julia package have to live in a separate file?
See the Distributions.jl package for an example .jl file structure: https://github.com/JuliaStats/Distributions.jl/tree/master/src
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Organizing a Julia program
Structure your program around your domain specific constrains, e.g if you look at Distributions.jl they have folders for univariate/multivariate or discrete/continuous with a file per distribution containing the struct + all its methods :
Nim
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Top Paying Programming Technologies 2024
22. Nim - $80,000
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"14 Years of Go" by Rob Pike
I think the right answer to your question would be NimLang[0]. In reality, if you're seeking to use this in any enterprise context, you'd most likely want to select the subset of C++ that makes sense for you or just use C#.
[0]https://nim-lang.org/
- Odin Programming Language
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Ask HN: Interest in a Rust-Inspired Language Compiling to JavaScript?
I don't think it's a rust-inspired language, but since it has strong typing and compiles to javascript, did you give a look at nim [0] ?
For what it takes, I find the language very expressive without the verbosity in rust that reminds me java. And it is also very flexible.
[0] : https://nim-lang.org/
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The nim website and the downloads are insecure
I see a valid cert for https://nim-lang.org/
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Nim
FYI, on the front page, https://nim-lang.org, in large type you have this:
> Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula.
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Things I've learned about building CLI tools in Python
You better off with using a compiled language.
If you interested in a language that's compiled, fast, but as easy and pleasant as Python - I'd recommend you take a look at [Nim](https://nim-lang.org).
And to prove what Nim's capable of - here's a cool repo with 100+ cli apps someone wrote in Nim: [c-blake/bu](https://github.com/c-blake/bu)
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Mojo is now available on Mac
Chapel has at least several full-time developers at Cray/HPE and (I think) the US national labs, and has had some for almost two decades. That's much more than $100k.
Chapel is also just one of many other projects broadly interested in developing new programming languages for "high performance" programming. Out of that large field, Chapel is not especially related to the specific ideas or design goals of Mojo. Much more related are things like Codon (https://exaloop.io), and the metaprogramming models in Terra (https://terralang.org), Nim (https://nim-lang.org), and Zig (https://ziglang.org).
But Chapel is great! It has a lot of good ideas, especially for distributed-memory programming, which is its historical focus. It is more related to Legion (https://legion.stanford.edu, https://regent-lang.org), parallel & distributed Fortran, ZPL, etc.
- NIR: Nim Intermediate Representation
What are some alternatives?
MLJ.jl - A Julia machine learning framework
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
HypothesisTests.jl - Hypothesis tests for Julia
go - The Go programming language
Optimization.jl - Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Odin - Odin Programming Language
StatsBase.jl - Basic statistics for Julia
rust - Empowering everyone to build reliable and efficient software.
Lux.jl - Explicitly Parameterized Neural Networks in Julia
crystal - The Crystal Programming Language
StaticLint.jl - Static Code Analysis for Julia
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io