Jinx
julia
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Jinx | julia | |
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26 | 350 | |
291 | 44,510 | |
- | 0.9% | |
0.0 | 10.0 | |
over 1 year ago | 5 days ago | |
C++ | Julia | |
MIT License | MIT License |
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Jinx
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DreamBerd is a perfect programming language
Check out jinx https://jamesboer.github.io/Jinx/
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what is your CI/CD pipeline setup and how are you handling larger binaries? are smaller game dev studios just brute forcing through LFS and building for each test?
Don't get me wrong, I'm a big fan of automated tests where it makes sense. I wrote a scripting language that I use for my personal game projects, and I never would have been able to do it if it weren't for the battery of tests for every feature, error, and corner case I could think of. But games are rarely like other software, with hard rules about what is "correct" or "incorrect". And it would be a nightmare to try to keep up with designers, constantly tweaking and tuning, so what's "correct" is literally a day to day, constantly moving target.
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any modern procedural programming languages?
A second trial for you might be Jinx. Depending on your definition of procedural, Jinx is 100% only procedural. https://jamesboer.github.io/Jinx/
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Which phases/stages does your programming language use?
Jinx (embeddable scripting language) works as following:
- How do I create a file that will automatically compile and run my c++ program when I double click it?
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Is I already know C and OOP, do I basically already know C++?
Feel free to look at my own interpreter, written in modern C++. You're welcome to ask me if you have any questions.
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I'm curious what a gameplay programmer would use a scripting language for
I use my own scripting language more like content, especially for things like one-off events and behaviors. Example: scripting special behaviors for a boss fight, or a room with a unique trap in it, or any other sort of one-off behavior that would be overkill for C++, but too complex for most other types of data-driven content. These days, visual scripting also helps to fill in these gaps between content and procedure.
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What are the best free books for learning to write interpreters in C++?
You're welcome to look at my scripting language Jinx, written in modern C++. Just let me know if you have specific questions. Data flow is JxLexer.cpp -> JxParser.cpp -> JxScript.cpp. Most everything else is implementation details. Also, note the parer is pretty complex, mostly because Jinx has a crazily flexible syntax for functions.
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Design examples for runtime scripting
Feel free to look at Jinx if you want an example of what I consider a fairly easy-to-use and integrate scripting system. Obviously, I'm a bit biased since I wrote it.
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Is just UTF-8 support good enough?
If you're working in UTF-8 internally, you could just write your own UTF-16 to UTF-8 conversion functions to convert strings at API boundaries. I did this in my scripting language because I didn't want to bring in dependencies.
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?
vigil - Vigil, the eternal morally vigilant programming language
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
funl - FunL programming language
NetworkX - Network Analysis in Python
RapidJSON - A fast JSON parser/generator for C++ with both SAX/DOM style API
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.
utf8.h - 📚 single header utf8 string functions for C and C++
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
langs
Numba - NumPy aware dynamic Python compiler using LLVM
zhetapi - A C++ ML and numerical analysis API, with an accompanying scripting language.
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp