Catwalk.jl
jlpkg
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| Catwalk.jl | jlpkg | |
|---|---|---|
| 1 | 2 | |
| 82 | 89 | |
| - | - | |
| 3.7 | 0.0 | |
| 8 months ago | over 1 year 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.
Catwalk.jl
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What's Bad about Julia?
Julia does have a minimal compilation path with an interpreter. You can even configure this on a per-module basis, which I believe some of the plotting packages do to reduce latency. There is even a JIT-style dynamic compiler which works similarly to the VMs you listed: https://github.com/tisztamo/Catwalk.jl/.
IMO, the bigger issue is one of predictability and control. Some users may not care about latency at all, whereas others have it as a primary concern. JS and related runtimes don't give you much control over when optimization and are thus black boxes, whereas Julia has known semantics around it. I think fine-grained tools to externally control optimization behaviour for certain modules (in addition to the current global CLI options and per-package opt-ins) would go a long way towards addressing this.
jlpkg
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What's Bad about Julia?
You can expose it as a CLI tool if you wish: https://github.com/fredrikekre/jlpkg
- Introduction to Pluto.jl
What are some alternatives?
JuliaInterpreter.jl - Interpreter for Julia code
reticulate - R Interface to Python
Conda.jl - Conda managing Julia binary dependencies [Moved to: https://github.com/JuliaPy/Conda.jl]
Pluto.jl - 🎈 Simple reactive notebooks for Julia
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)