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Why pylock.toml includes digital attestations
Including digital attestations in pylock.toml allows developers to verify the origin and integrity of dependencies, not just their versions and hashes, improving protection against supply chain attacks. The broader point is that modern package security requires provenance, not just reproducibility, so lock files are evolving from “what to install” into “what can be trusted to install.”
How Clean Code Turns Into Overengineering
This video is about how code that looks clean can still hide a bad design, and why overusing tiny abstractions can make a program harder to understand and change. It refactors a Python reporting example by simplifying the structure, making the pipeline explicit, and focusing on cohesion over smallness.
claude-howto
A visual, example-driven guide to Claude Code - from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
diffly
Utility package for comparing polars data frames.
Smello
A developer tool that captures outgoing HTTP requests from your code and displays them in a local web dashboard.
💖 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.
Oxyde ORM
A type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core designed for clarity, speed, and reliability.
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.
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Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.