Behind the Commit

Why Python is Slow: Antonio Cuni on SPy and Statically Compiled Python

Antonio Cuni (principal engineer at Anaconda, author of SPy, developer of PyScript and PyPy, co-founder of the HPy project, and creator of PDB++, Fancy Completer, and VMProf) shares why Python is slow and how SPy, a new statically compiled variant of Python, aims to be as fast as C while staying as Pythonic as Python. We discuss the trade-off between dynamic features and performance, how SPy's "red and blue" code model replaces best-effort JIT with predictable errors, why PyPy struggles with C extensions, and what "Pythonic" really means. Outline 00:00 - Episode highlights and introduction 00:40 - Why is Python so slow? 01:31 - Would a static Python be fast? 02:13 - What is SPy? 02:28 - What motivated you to create SPy? (Spoiler: frustration) 03:52 - Which ideas from PyPy and HPy went into SPy? 06:36 - Biggest challenges of building SPy 07:49 - What does "Pythonic" actually mean? 10:39 - Current roadmap and the two-level language idea 12:24 - Walking through the SPy compilation pipeline 13:57 - Red-shifting and the blue/red AST nodes 14:40 - Why blue and red? (No, it's not from The Matrix) 15:52 - PyPy vs SPy: when to use which 19:34 - C extensions and the C API problem 20:04 - How to actually make your Python code faster 23:34 - Memory management and garbage collection in SPy 24:44 - When will SPy be production-ready? 25:44 - How to contribute to SPy 26:26 - Where does the name "SPy" come from? Note: the episode was recorded in July 2025 and some features, such as list type, garbage collector, and documentation, have been implemented in the meantime. In the section below you can find the links to those features. Episode links – SPy on GitHub: https://github.com/spylang/spy [https://github.com/spylang/spy] – PyPy: https://pypy.org/ [https://pypy.org/] – HPy project: https://hpyproject.org/ [https://hpyproject.org/] – PyScript: https://pyscript.net/ [https://pyscript.net/] – Anaconda: https://www.anaconda.com/ [https://www.anaconda.com/] – decorators in SPy: https://github.com/spylang/spy/pull/225 [https://github.com/spylang/spy/pull/225] – SPy roadmap: https://github.com/spylang/spy/blob/main/ROADMAP.md [https://github.com/spylang/spy/blob/main/ROADMAP.md] – list type in SPy: https://github.com/spylang/spy/blob/main/stdlib/_list.spy [https://github.com/spylang/spy/blob/main/stdlib/_list.spy] – garbage collector in SPy: https://github.com/spylang/spy/pull/390 [https://github.com/spylang/spy/pull/390] – SPy documentation: https://spylang.github.io/docs/dev/ [https://spylang.github.io/docs/dev/] – series of blog posts on SPy: https://antocuni.eu/2025/10/29/inside-spy-part-1-motivations-and-goals/ [https://antocuni.eu/2025/10/29/inside-spy-part-1-motivations-and-goals/]

Trailer – Why Python is Slow: Antonio Cuni on SPy and Statically Compiled Python

In this upcoming episode, I chat with Antonio Cuni, the creator of SPy and long-time PyPy developer. We discuss why Python is slow, how a statically compiled Python variant can be as fast as C while still feeling Pythonic, and the "red and blue" model behind SPy's compilation pipeline. Full episode coming out soon!

Maintaining 80 OSS Projects: Anthony Sottile on pre-commit and Developer Tooling

In this episode, I'm chatting with Anthony Sottile — creator of pre-commit, primary maintainer of flake8, core contributor to pytest, and maintainer of around 80 open source projects across the Python ecosystem. He's also a GitHub Star and a popular live coding streamer on Twitch under the name "anthonywritescode". We dig into how he actually manages all of it, the origin story of pre-commit, the psychological side of open source maintenance, and how to get started contributing. Outline 00:00 Episode highlights & Intro 0:59 The all-repos tool — distributed refactoring across repos 2:04 Where the idea came from (Yelp's microservices explosion) 2:42 Tools for managing multiple repositories 3:34 How pre-commit got started (a college group project) 4:15 Rewriting pre-commit for Yelp in 2018 4:46 Hardest technical challenge: supporting 13 programming languages 6:07 Surprising bugs found in NPM and Git 7:05 GitHub Stars and open source funding 8:10 How Sentry approaches funding open source 8:43 The psychological challenges of open source maintenance 10:06 What would you tell your past self? 11:32 How to start contributing to open source 13:05 Why Anthony started streaming on Twitch 13:52 What motivates him to keep streaming 14:58 Has community interaction changed how you design code? 15:48 Where to find Anthony online Episode links – pre-commit: https://pre-commit.com [https://pre-commit.com] – all-repos: https://github.com/asottile/all-repos [https://github.com/asottile/all-repos] – Anthony's YouTube: https://www.youtube.com/@anthonywritescode [https://www.youtube.com/@anthonywritescode] – Anthony's Twitch: https://www.twitch.tv/anthonywritescode [https://www.twitch.tv/anthonywritescode]

Trailer – Maintaining 80 OSS Projects: Anthony Sottile on pre-commit and Developer Tooling

In this upcoming episode, I chat with Anthony Sottile — creator of pre-commit and maintainer of around 80 open source projects. We talk about how he actually manages it all, the surprising bugs he's found in NPM and Git along the way, and the psychological side of open source that nobody talks about.

AI-Written Code: Armin Ronacher on AI Agents and the Future of Programming

Armin Ronacher (creator of Flask, previously Sentry’s VP of Platform, and currently founder of a startup Earendil) shares his experience building a startup where 90% of the code is AI-generated. We discuss which programming languages work best with AI agents, why Python's ecosystem makes life harder for AI, and what skills programmers need to stay relevant in the age of AI. Outline 00:00 Episode highlights and introduction 0:43 - How much code do you write yourself vs AI agents? 2:03 - What kind of problems are suitable for AI and what do you solve yourself? 4:02 - Why do AI agents work better with certain languages like Go vs Python? 7:15 - How to steer AI agents in certain directions? 12:01 - What patterns can AI agents handle well? 15:28 - When starting a new project, what language do you use now? 16:27 - Do you monitor agents and what safeguards do you have? 18:48 - How do you handle parallelization with multiple agents? 19:34 - How should we handle licenses for AI-generated open source libraries? 24:07 - What is the future of programming jobs? 26:31 - What skills should programmers learn to stay competitive? 31:05 - Tips on how to get started with AI agents? 33:44 - How to stay up to date with all the recent changes? 36:16 - Where can people find you online? Episode links – Claude Code: https://claude.ai/ [https://claude.ai/] – UV (Python package manager): https://github.com/astral-sh/uv [https://github.com/astral-sh/uv] – Simon Willison's blog: https://simonwillison.net/ [https://simonwillison.net/] – Armin's blog: https://lucumr.pocoo.org/ [https://lucumr.pocoo.org/]