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exaloop
codon
Python2022–2026
exaloop / codon

codon

A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support

20Distinct people
21Mentions
0Explicit recommenders
16.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year21 dated mentions

Observed mentions: 2022-12-08 15:05:45 UTC to 2026-06-23 15:09:36 UTC.

024682022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 8 mentions; 0 submissions, 8 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 8 mentions; 2 submissions, 6 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC312
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC808
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC826
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

21 sources
nimic is a lightweight pure Python package that emulates Nim types and constructions, making it straightforward to transpile to Nim and compile AOT. Key principle: nimic code is valid Python that runs unmodified in CPython and also transpiles to equivalent Nim code. Because nimic code is just standard Python with type hints and ctypes shims, it is a fully valid CPython script, so you can use the Python REPL during development, drop a breakpoint in the middle of a heavy algorithmic loop and inspect the variables natively. Zero Lock-In: You don't need a special runtime engine. If the Nim compiler is not available, your script still runs (albeit slower than standard Python due to some emulation overhead) on any machine with Python installed. Seamless Distribution: You can use this to develop high-performance logic natively in Python, debug i
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t with Python tooling, and then compile to a native executable or C-extension via Nim. Why Nim? Its syntax maps well to Python, it is rather clear how to emulate its constructions in Python, and its performance is comparable to C (as it compiles to C). Port of the "trace-of-radiance" Nim project to nimic can be found in "ndsl_raytracer" in my GitHub repo (dima-quant). With the compiled executable the render time for a single 512x288 scene dropped from many hours in Python to just 10 minutes on a single M1 CPU core. The repo also includes nimic ppm to mp4 converter. Similar projects: - Pyccel (https://github.com/pyccel/pyccel): Python extension language using accelerators - SPy (https://github.com/spylang/spy) is a variant of Python specifically designed to be statically compilable while retaining a lot of the "useful" dynamic parts of Python. - Codon (https://github.com/exaloop/codon) is a high-performance Python implementation that compiles to native machine code without any runtime overhead. It is still work in progress, e.g. there is no JIT and multiprocessing support yet, but now I'm not sure what functionality would be best to implement next. Any suggestions?
In Show HN: Nimic – Pure Python as a systems language with AOT compilation
Well, the truth is there's a lot more to JITing than what I wrote in the article, just because JITing didn't fit the vibe of the rest of the article that well. Anyway, yes theoretically you can. Recently folks creating Codon (https://github.com/exaloop/codon) did it for Python using a cool form of type inference. That said, I don't think any mainstream JS VMs do it. Most recent work I know in AOT compilation for JS seems to use some profile-guided optimization and not static type inference. For example, look at the architecture at 6:38 and also discussion about types here: https://youtu.be/pVhcyKf3efM?t=469). Something similar seems to be true for the project described here: https://www.youtube.com/watch?v=_T3s6-C38JI The story seems to be a bit different for hopc: https://www.youtube.com/watch?v=iY1EXHQ6IeQ The video starts by giving goo
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d examples of why it is very hard to have good type inference for JS (which agrees with my intuition) but continues to talk about using type inference. However, the benchmark in this talk and in their paper are not super convincing. All that said, JS is not my expertise so I would be interested to know more about what mainstream VMs do in practice and any latest research.
In Common misconceptions about compilers
The license is the "Business Source License 1.1" [1]. The Business Source License (BSL) 1.1 is a software license created by MariaDB Corporation. It's designed as a middle ground between fully open-source licenses and traditional proprietary software licenses. It's kind of neat because it's a parameteric license, in that you can change some parameters while leaving the text of the license unchanged. For codon, the "Change Date" is 2028-03-01 and the "Change License" is "Apache License, Version 2.0", meaning that the license will change to Apache2 in March of 2028. Until then, I guess you need to make a deal with Exaloop to use codon in production. [1] https://github.com/exaloop/codon?tab=License-1-ov-file#readm...
In A high-performance, zero-overhead, extensible Python compiler using LLVM
Their fannkuch benchmark seems to be a bit dishonest. They claim an enormous perf delta on https://exaloop.io/benchmarks.html but fannkuch uses factorial a lot and they define factorial with a very small (n=20) table: https://github.com/exaloop/codon/blob/fb461371613049539654c1... Disclaimer: I've worked on several Python runtimes and compilers, but I'm not by any means out to get Codon. Just happened across this by accident while looking at their inline LLVM, which is neat.
In Codon: Python Compiler
Repo for more details: https://github.com/exaloop/codon > What is Codon? > Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. Typical speedups over Python are on the order of 10-100x or more, on a single thread. Codon's performance is typically on par with (and sometimes better than) that of C/C++. Unlike Python, Codon supports native multithreading, which can lead to speedups many times higher still. Codon grew out of the Seq project. > What isn't Codon? > While Codon supports nearly all of Python's syntax, it is not a drop-in replacement, and large codebases might require modifications to be run through the Codon compiler. For example, some of Python's modules are not yet implemented within Codon, and a few of Python's dynamic features are disallowed. The Codon comp
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iler produces detailed error messages to help identify and resolve any incompatibilities. > Codon can be used within larger Python codebases via the @codon.jit decorator. Plain Python functions and libraries can also be called from within Codon via Python interoperability.
In Codon: Python Compiler

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