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Julia Alternatives
Similar projects and alternatives to julia
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Nim
Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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StaticCompiler.jl
Compiles Julia code to a standalone library (experimental)
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jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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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.
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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Octavian.jl
Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
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zig
General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better julia alternative or higher similarity.
julia reviews and mentions
Posts with mentions or reviews of julia.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-29.
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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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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...
- Julia and Mojo (Modular) Mandelbrot Benchmark
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Ask HN: Does Your GitHub Repo Need a Landing Page
I'm really not fond of that agpt landing page. So many red flags; the AI-generated background, mailing letter box with accompanying email-beggar text, the Discord button (!!!) being given as much space as the Github repo click-through... it's a mess. The whole website feels more boilerplate than content. I mean, look at these quotes!
> With the help of the incredible open-source community, we’re making approximately a month’s progress every 48 hours.
> Auto-GPT is pushing for the best, autonomous AI assistant for every device for every person. In the near future, we want you to be able to accomplish more everyday.
> We have come to define ourselves by what we do. If this can be automated, how may we then define ourselves? By what we create!
Every line of copy I read from that site makes me feel like I'm getting dumber instead of learning about their product. If you are building a landing page for a serious software project, you need a more professional approach. You can be playful if you want, but the landing page somehow manages to be less informative than the Github repo in the example you've listed.
Since everyone will ask, here are some software project landing pages that strike me as well-designed:
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Julia 1.10 will have multithreaded garbage collection
See also https://github.com/JuliaLang/julia/pull/50137 and https://github.com/JuliaLang/julia/pull/50013 that improve the sweeping (the linked PR is only the mark phase)
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Any Good Alternatives for Matlab?
Julia is a great alternative in terms of raw speed/performance (not a compatible language)
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What Apple hardware do I need for CUDA-based deep learning tasks?
If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.
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How does Elixir stack up to Julia in the future of writing machine-learning software?
Lots of this is in progress and doesn't require a 2.0. Better error messages landed last week, see https://twitter.com/ChrisRackauckas/status/1661014235466563591 which was https://github.com/JuliaLang/julia/pull/49795. That makes them a ton more readable, and there's a few more of these kinds of things in process. The parser change is set to make it into v1.10, https://github.com/JuliaLang/julia/pull/46372. v1.10 is supposed to branch in just a few weeks and will possibly be LTS. With that all in mind, there's a time to take stock of what has been a large amount of pretty huge changes and seeing what's next to help Julia improve static compilation.
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A note from our sponsor - InfluxDB
www.influxdata.com | 2 Dec 2023
Stats
Basic julia repo stats
342
43,572
9.9
3 days ago
JuliaLang/julia is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of julia is Julia.
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