kotlingrad
julia
| kotlingrad | julia | |
|---|---|---|
| 3 | 369 | |
| 537 | 47,283 | |
| 0.0% | 0.3% | |
| 3.7 | 10.0 | |
| 7 months ago | 6 days ago | |
| Kotlin | 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.
kotlingrad
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Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
and that there is a mature library for autodiff https://github.com/breandan/kotlingrad
- Show HN: Shape-Safe Symbolic Differentiation with Algebraic Data Types
- Kotlin∇: Type-safe Symbolic Differentiation for the JVM
julia
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Reflections on 2 years of CPython's JIT Compiler: The good, the bad, the ugly
> I was active in the Python community in the 200x timeframe, and I daresay the common consensus is that language didn't matter and a sufficiently smart compiler/JIT/whatever would eventually make dynamic scripting languages as fast as C, so there was no reason to learn static languages rather than just waiting for this to happen.
To be very pedantic, the problem is not that these are dynamic languages _per se_, but that they were designed with semantics unconcerned with performance. As such, retrofitting performance can be extremely challenging.
As a counterexample of fast and dynamic: https://julialang.org/ (of course, you pay the prize in other places)
I agree with your comment overall, though.
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Top Programming Languages for AI Development in 2025
Julia: Exceptional Numerical Processing
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Building a Secret Scanner in Julia: A GitLeaks Alternative
To use Julia – one of the best programming languages, which is unfairly considered niche. Its applications go far beyond HPC. It’s perfectly suited for solving a wide range of problems.
- My programming Cruise
- Ask HN: What less-popular systems programming language are you using?
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A data scientist's journey building a B2B data product with Julia and Pluto
In this post, I’m exploring dev tools for data scientists, specifically Julia and Pluto.jl. I interviewed Mandar, a data scientist and software engineer, about his experience adopting Pluto, a reactive notebook environment similar to Jupyter notebooks. What’s different about Pluto is that it’s designed specifically for Julia, a programming language built for scientific computing and machine learning.
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New Horizons for Julia
https://github.com/JuliaLang/julia/issues/57483 yes, yes it should.
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What is Open-Source? Beginners Guide How to Get Started.
Julia Seasons of Contributions (JSoC)
- I Chose Common Lisp
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Stressify.jl Performance Testing
_ __ _(_)_ | Documentation: https://docs.julialang.org (_) | (_) (_) | _ _ _| |_ __ _ | Type "?" for help, "]?" for Pkg help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 1.11.2 (2024-12-01) _/ |\__'_|_|_|\__'_| | Official https://julialang.org/ release |__/ | julia>
What are some alternatives?
lets-plot-kotlin - Grammar of Graphics for Kotlin
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
kmath - Kotlin mathematics extensions library
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).
uiua - A stack-based array programming language
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.