Python Weekly (Issue 698 May 8 2025)

Author

Rahul Chaudhary
May 08, 2025

Welcome to issue 698 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

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 called djobs.dev in 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.

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 primitives, making asynchronous programming more approachable and transparent.

See how we slashed PyPI’s test suite runtime from 163 to 30 seconds.

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).

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.

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.

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 interface for monitoring job execution.

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 details about possible function calls and when they should be used.

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.


Interesting Projects, Tools, and Libraries

Have a natural, spoken conversation with AI!

The framework to detect and mitigate AI centric security risks.

An open source platform that connects your AI agents to 600+ tool integrations with multi-tenant auth, granular permissions, and access through direct function calling or a unified MCP server.

A high-performance serving engine for web browsing AI.

Effortless LLM extraction from documents.

Empowering Large Reasoning Models with Deep Research Capability.

Safer python package installs with audit and consent 𝘣𝘦𝘧𝘰𝘳𝘦 install.

A declarative, native data frame validation library

A tiny, useful Python lib of string, file, and object utilities.

A declarative PySpark framework for row- and aggregate-level data quality validation.

Build a machine learning model from a prompt.

Open Source Generalist AI Agent.

SQL-tString allows for f-string like construction of sql queries.


New Releases

Upcoming Events and Webinars

There will be following talks

  • Conquering PDFs: document understanding beyond plain text

  • Building EU-AI Act Compliant AI Agents for Legacy Systems

  • Implementing Distributed Systems in Python

There will be following talks

  • Rust in the data science and machine learning stack

  • Dataframely — A declarative, polars-native data frame validation library

There will be following talks

  • Modernizing Data Pipelines on Databricks: How We Unified Batch and Streaming with Hera

  • Climate change: from data ingestion to knowledge ingestion

There will be a talk, Python Developer’s Guide to Model Context Protocol and BioMCP.

There will be following talks

  • From Rules to Reality: How Natural Language Becomes Data Governance Logic

  • Hacking and defending CI/CD pipelines


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