Python Hub Weekly Digest for 2025-05-18
This week in Python, Procrastinate was introduced as a PostgreSQL-based task queue for Python, offering a simpler alternative to Celery. Cory Zue shared his experience with "vibe coding" and building a Django/React app. PyPI's test suite was optimized to run 81% faster. A detailed article demystified Python’s asyncio by rebuilding its core concepts from scratch. Meta is enhancing the Python ecosystem by advancing type checking and introducing free-threading capabilities. Lastly, Python's scripting capabilities were highlighted for automating 3D workflows using OpenUSD. Have a great week and happy coding!
💖 Most Popular
Procrastinate - PostgreSQL Task Queue for Python (Celery Alternative!)
This video introduces Procrastinate, a PostgreSQL-based task queue for Python, offering a simpler alternative to Celery by leveraging Postgres for task management, eliminating the need for additional services like Redis or RabbitMQ. The tutorial covers setting up Procrastinate with Django, defining and triggering tasks, including scheduled tasks, and integrating it with the Django admin ...
Ty: A fast Python type checker and language server, written in Rust
Vibe Coding with Django, React and Cursor: My Experience and Takeaways
Cory Zue shares his experience with "vibe coding," a new approach to software development using AI tools, and how he built a Django/React app calleddjobs.devin just two days. He emphasizes the importance of planning, iterating, and reviewing code when working with AI, and shares tips for effectively using tools like Cursor and managing rules files.
Making PyPI's test suite 81% faster
See how we slashed PyPI’s test suite runtime from 163 to 30 seconds.
pipask
Safer python package installs with audit and consent 𝘣𝘦𝘧𝘰𝘳𝘦 install.
📖 Articles
Asyncio Demystified: Rebuilding it From Scratch One Yield at a Time
The article demystifies Python’s asyncio by rebuilding its core concepts from scratch, starting with basic generators and coroutines, then constructing a cooperative multitasking scheduler, and finally integrating non-blocking I/O using custom awaitable Future objects. Through step-by-step code examples, it shows how Python’s async/await syntax is just syntactic sugar over these primitiv...
The REAL Reason You Should Use Type Hints in Python
This video explains that the true power of type hints in Python lies in how they encourage better code design by prompting developers to think more generically about data structures. The video also highlights the principle that inputs should be as generic as possible (contravariant), while outputs should be as specific as possible (covariant).
Enhancing the Python ecosystem with type checking and free threading
Meta describes its efforts to enhance the Python ecosystem by advancing type checking and introducing free-threading capabilities, aiming to improve code reliability and performance at scale. The post highlights the technical challenges, solutions, and community collaboration involved in making Python more robust and concurrent for large-scale applications.
Using Python to Automate 3D Workflows with OpenUSD
The post explains how Python’s scripting capabilities can automate and streamline 3D workflows using OpenUSD, making tasks like data transformation, validation, and scene creation more accessible and efficient. It highlights NVIDIA’s tools, SDKs, and learning resources that empower developers to build, validate, and optimize complex 3D scenes with Python in the OpenUSD ecosystem.
Python Hub Weekly Digest for 2025-05-11
Polycompiler: Merge Python and JavaScript code into one file that runs in both
US Routing – Python library for fast local routing in the United States
djobs.dev
Asyncio Demystified: Rebuilding it From Scratch One Yield at a Time
Python type hints: mixin classes
The article explains how to properly add Python type hints to mixin classes, addressing common challenges with attributes that mixins assume exist in their base classes. It shows solutions using subclassing or protocols to satisfy type checkers like Mypy without compromising runtime behavior or type safety.
Function calling using LLMs
While LLMs excel at generating cogent text based on their training data, they may also need to interact with external systems. Function calling allows them to construct such calls. The LLM does not execute these calls directly, instead it creates a data structure that describes the call, passing that to a separate program for execution and further processing. The LLM's prompt includes de...
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👾 Reddits
How SHOULD you install Python on Mac OS?
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