HTTP.jl
JET.jl
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HTTP.jl | JET.jl | |
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7 | 13 | |
619 | 684 | |
0.5% | - | |
7.7 | 9.1 | |
4 days ago | about 1 month ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | 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.
HTTP.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
The req.url field contains the URL of the received request, the req.method field contains request method, like GET or POST, the req.body field contains the POST body of the request in binary format. HTTP request object contains much other information. All this you can find in HTTP.jl documentation. Our web application will only check the request method. If the received request is a POST request, it will parse req.body to JSON object and send the data from this object to the isSurvived function to make a prediction and return it to the client browser. For all other request types, it will just return the content of the index.html file, to display the web interface. This is how the whole source of titanic.jl web service looks:
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Automate the boring stuff with Julia?
HTTP.jl and Gumbo.jl for web-scraping
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Recommendations on how to start web scraping with julia for price updates? (if possible)
I haven't seen that tutorial, but I agree that HTTP.jl, Gumbo.jl, and Cascadia.jl are the way. I used them to export public wishlists from bookdepository, which has no API nor a built in exporting tool.
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Why not Julia?
I find some of the library documentation hard to understand. Compare http.jl with python's requests, for example. Something as core as HTTP requests should have clear docs with tonnes of examples. Part of this is also a personal dislike of documenter.jl styling. Idk why the contrast is so low – would prefer a standard readthedocs theme.
- Julia 1.6: what has changed since Julia 1.0?
JET.jl
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Julia v1.9.0 has been released
For instance, https://github.com/aviatesk/JET.jl is still in its relative infancy, but it's played a big role in detecting quite a few potential bugs that had never been reported to use by users or caught in our testing infrastructure. There's also been a lot developments like interfaces to RR the time travelling debugger https://rr-project.org/ which helps us better understand and catch some very hard to debug non-deterministic bugs.
Yes, tooling around this is being developed in the form of linters (e.g. https://github.com/julia-vscode/StaticLint.jl) and through real compiler integration tools like the very cool https://aviatesk.github.io/JET.jl/dev/ but this is definitely somewhere that the tooling in julia is weaker than in other languages. It seems to be picking up a lot of speed though.
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Julia Computing Raises $24M Series A
Have you seen Shuhei Tadowaki's work on JET.jl (?)
If you're curious: https://github.com/aviatesk/JET.jl
This may seem more about performance (than IDE development) but Shuhei is one of the driving contributors behind developing the capabilities to use compiler capabilities for IDE integration -- and indeed JET.jl contains the kernel of a number of these capabilities.
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From Julia to Rust
- Pattern matching (sometimes you don't want the overhead of a method lookup)
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Julia is the best language to extend Python for scientific computing
You can use the `@code_warntype` macro to check for type stability, which is very helpful for detecting such performance pitfalls on single function level. In the future, https://github.com/aviatesk/JET.jl may give a more powerful way to do it.
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Julia 1.6: what has changed since Julia 1.0?
Technically it is catching the type error at compile time (but compile time is Just Ahead Of Time). If you want something that feels more like type checking in a statically compiled language, you should definitely check out https://github.com/aviatesk/JET.jl
What are some alternatives?
julia - The Julia Programming Language
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
StaticArrays.jl - Statically sized arrays for Julia
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
geni-performance-benchmark
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
PackageCompiler.jl - Compile your Julia Package
BinaryBuilder.jl - Binary Dependency Builder for Julia
Gumbo.jl - Julia wrapper around Google's gumbo C library for parsing HTML
IRTools.jl - Mike's Little Intermediate Representation