Coconut
julia
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Coconut | julia | |
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27 | 350 | |
3,943 | 44,510 | |
- | 0.9% | |
9.4 | 10.0 | |
7 days ago | 3 days ago | |
Python | Julia | |
Apache License 2.0 | MIT License |
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.
Coconut
- Coconut: Simple, elegant, Pythonic functional programming
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Mojo is now available on Mac
> to be part of the Python ecosystem
I'd rather use Python if I'm in the Python ecosystem. So many attempts were made in the past to make a new language compatible with the Python ecosystem (look up hylang and coconu -- https://github.com/evhub/coconut). But at the end of the day, I'd come back to Python because if there's one thing I've learnt in recent years it's this:
minimize dependencies at all costs.
- I modified and hacked away xonsh source code
- Show HN: I mirrored all the code from PyPI to GitHub
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Leaving Haskell Behind
Have you had a look at Coconut? I don't know if it'll push all your buttons but whenever I hear someone who's reasonably content with Python but wants more FP goodies I always think of it. https://github.com/evhub/coconut . It's basically a superset of Python3 that transpiles into Python3 and is compatible with MyPy. I don't think I'd code Python w/o it ever again assuming I had the choice. The biggest negative for me is that there's no IDE support for the language last I looked, though of course you can work with the transpiler output (plain Python) in your favorite Python IDE. It might be fun to play around with, I know that I really enjoyed it but then I got spoiled by the language+tooling of Scala3, but if you don't have that option ...
- Codon: A high-performance Python compiler
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[2022 Day 1-7] Going for 1 language per day, looking good so far
If you're looking for suggestions I want to put forward zig lang if you like C/C++ and Coconut Lang if you like Python!
- Show HN: Programming Google Flutter with Clojure
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What is your favourite programming language? (other than Scala)
F# and also the fun, compile-to-Python, functional language called Coconut.
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Eleven strategies for making reproducible research the norm
I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.
https://github.com/JuliaLang/julia/issues/34753
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
What are some alternatives?
Toolz - A functional standard library for Python.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
fn.py - Functional programming in Python: implementation of missing features to enjoy FP
NetworkX - Network Analysis in Python
Pyrsistent - Persistent/Immutable/Functional data structures for Python
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
funcy - A fancy and practical functional tools
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
returns - Make your functions return something meaningful, typed, and safe!
Numba - NumPy aware dynamic Python compiler using LLVM
effect - effect isolation in Python, to facilitate more purely functional code
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp