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Reinventing Python's AsyncIO
The post explores a redesign of Python’s async runtime, arguing that the current async/await and event-loop model adds unnecessary complexity, and proposing a simpler runtime where concurrency is handled automatically without explicit async syntax.The author experiments with a new runtime approach that can run async workloads 2–3.5× faster than traditional asyncio, suggesting Python’s co...

Would it have been better if Meta bought Astral.sh instead?

How we optimized Dash's relevance judge with DSPy
Dropbox used DSPy to turn prompt engineering for our relevance judge into a measurable, automated optimization loop, improving task performance, cost, and how reliably it works in production.

Using Claude to fix PyPy3.11 test failures securely
This post describes using Claude to assist in fixing PyPy 3.11 test failures, with all generated changes run in a sandbox and verified locally. It highlights a practical workflow where AI suggests patches but humans validate results, enabling faster debugging without sacrificing safety.

Python Type Checker Comparison: Typing Spec Conformance
When you write typed Python, you expect your type checker to follow the rules of the language. But how closely do today's type checkers actually follow the Python typing specification? In this post, we look at what typing spec conformance means, how different type checkers compare, and what the conformance numbers don't tell you.

💖 Most Popular in the Last Two Weeks

Rewriting a 20-year-old Python library
The article covers a full rewrite of the Akismet Python client to add async support, modern HTTP handling, and a richer response model while preserving usability. It emphasizes API ergonomics, testing support, and maintainability, while honoring the original author and evolving the library for modern Python.

Stop Passing Primitives Everywhere (Use Value Objects)
By replacing primitives with immutable Value Objects, you can centralize validation and eliminate domain ambiguity for types like prices and percentages. This pattern ensures that invalid states are impossible to represent, creating a self-documenting and more reliable codebase without the overhead of heavy frameworks.

Lessons from Pyre that Shaped Pyrefly
High-performance type checking at Meta required a performance-first architecture and tight integration with developer workflows, enabling fast, incremental analysis at massive scale. The key lesson is that large Python codebases adopt typing successfully through gradual, low-friction tooling that prioritizes developer ergonomics and fast feedback over strict correctness.

Comparing Python packages for A/B test analysis (with code examples)

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Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.