Crafting Interpreters
Numba
Crafting Interpreters | Numba | |
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45 | 124 | |
8,166 | 9,471 | |
- | 1.1% | |
0.0 | 9.9 | |
29 days ago | 3 days ago | |
HTML | Python | |
GNU General Public License v3.0 or later | BSD 2-clause "Simplified" 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.
Crafting Interpreters
- Crafting Interpreters
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The Top 10 GitHub Repositories Making Waves 🌊📊
Build an Interpreter (Chapter 14 on is written in C)
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Writing a Debugger from Scratch: Breakpoints
I’m guessing you’ll have to work with the scopes in the resolver:
https://github.com/munificent/craftinginterpreters/blob/mast...
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loxcraft: a compiler, language server, and online playground for the Lox programming language
Better open an issue/request wiki edit at https://github.com/munificent/craftinginterpreters/wiki/Lox-implementations
- Gigachad Ken Thomson.
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Show HN: Yaksha Programming Language
I'm late to the party, but I want to say thank you for sharing this. It's inspiring to look at how much you've built and (hopefully) enjoyed the process of building! I'm loving everything -- your site, your language design, your docs, your builtin libraries, your dev tools. Beyond impressive. People like you are the ones who make HN one of my best places on the internet.
For context on where I'm coming from, about two weeks ago I picked up Crafting Interpreters [1] for fun. I'm finding your clear-yet-concise Compiler internals [2] to be particularly compelling reading, and jumping back and forth between those "how this all works" docs and the live example of this language you actually built do a WASM-compiled tree-blowing-in-the-wind animation is just... just wow. So freaking cool!
I also enjoyed reading the comment thread that inspired you to start on Yaksha and seeing how this project has a wholesome start as inspiration-by-programming-hero. I hope you recognize that a few years later you've now ascended from inspiree to inspirer. I also hope you're still having tons of fun building out Yaksha!
[1] https://www.craftinginterpreters.com/
[2] https://yakshalang.github.io/documentation.html#compiler-int...
- Keeping track of returned and break-ed values between code blocks
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How do you start your own programming language?
There are books which will talk you through the process. Crafting Interpreters is highly spoken of; I used Writing an Interpreter in Go, because I like Go. Then there's Compilers: Principles, Techniques, and Tools (the "Dragon Book"). This is considered heavy, but a classic, it's been around since '86.
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Designing a new language
I cannot recommend Crafting Interpreters by Robert Nystrom enough, it covers a lot of the stuff you need to know, completely for free.
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A roadmap to design programming languages
Crafting Interpreters is a fun primer on language design. It has a complete roadmap to build a fairly simple language, twice. There are some topics it won't touch on, like static type systems, but it provides a great introduction so that you can start tinkering and learn by doing.
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
git-internals-pdf - PDF on Git Internals
NetworkX - Network Analysis in Python
You-Dont-Know-JS - A book series on JavaScript. @YDKJS on twitter.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
tinyrenderer - A brief computer graphics / rendering course
Dask - Parallel computing with task scheduling
paip-lisp - Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
cupy - NumPy & SciPy for GPU
CppCoreGuidelines - The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
30-days-of-elixir - A walk through the Elixir language in 30 exercises.
SymPy - A computer algebra system written in pure Python