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Python Hub Weekly Digest for 2026-06-28
How I made dependency injection in Python 130× faster: from reflection to compiling the graph
The post shows how a Python dependency injection container was optimized from ~53 µs to 0.40 µs per resolve by caching plans, eliminating unnecessary checks, and compiling dependency graphs into generated code. Along the way, it demonstrates practical performance engineering techniques, including code generation, common-subexpression elimination, fuzz testing, and reproducible benchmarking
Libraries for your Python Polars workflows
The post highlights a set of Python libraries that work natively with Polars, letting you stay in the Polars ecosystem for validation, tables, visualization, and LLM-assisted analysis.
Showcasing allauth IdP: build an MCP server
This article demonstrates how to build a Model Context Protocol (MCP) server using Django and django-allauth, with OpenID Connect (OIDC) authentication handled by the allauth.idp package. It shows how MCP hosts such as Claude can dynamically register and authenticate clients without relying on separate third-party identity providers.
BugHunter
AI-powered bug bounty hunting from your terminal - recon, 20 vuln classes, autonomous hunting, and report generation. All inside Claude Code.
💖 Most Popular in the Last Two Weeks
GitHub Pages alternative with native Python
This article shows how to recreate core GitHub Pages functionality using only Python's standard library, including static file serving, automated deployments, and HTTPS support. By extending http.server in just a few dozen lines of code, it demonstrates how native Python tools can power a lightweight static website hosting platform.
free-llm-api-keys
The easiest way to access LLM APIs for free - no credit card, no registration. Copy a key from the table below, paste it into your app, and start building.
Choosing a Python task queue library in 2026: Celery vs Dramatiq vs FastStream vs Taskiq vs Repid
pyappdist – Build installers for Python apps
Testing in Python with pytest: from the basics to advanced techniques
This comprehensive guide covers pytest from basic testing concepts to advanced techniques such as fixtures, parametrization, mocking, and asynchronous testing. It provides practical patterns for building maintainable, scalable Python test suites and leveraging pytest effectively in production environments.
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