Python Weekly (Issue 715 September 25 2025)

Author

Rahul Chaudhary
September 25, 2025

In partnership with

Welcome to issue 715 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

The video teaches beginners how to build advanced AI agents, such as voice sales agents, research assistants, and multi-agent workflows, using LiveKit, Exa, LangChain, and Cerebras. It provides step-by-step guidance, hands-on code, and free API credits to help developers quickly create real-world AI applications.

The article discusses an innovative approach to compiling Python code into cross-platform, ahead-of-time optimized machine code executables without modifying the original Python source. It details building a custom symbolic tracer, propagating types for lowering to C++, leveraging AI to generate C++ operators, and empirically optimizing performance across multiple hardware targets to enable Python functions to run efficiently anywhere.

The video tutorial covers the new features introduced in Django 6.0 alpha, including built-in Content Security Policy (CSP) nonce support, simpler background task management, and reusable template partials for cleaner code. It provides practical examples and explanations for implementing these features, highlighting improvements in security, asynchronous task handling, and template design in the latest Django release.

The Python type checking landscape in 2025 includes three new Rust-based tools: Astral's ty, Meta's pyrefly, and Zuban. Ty emphasizes gradual adoption with fewer false positives, pyrefly focuses on aggressive inference to catch more issues early, and Zuban aims for seamless mypy compatibility; while conformance tests reveal differences, all show promise for real-world Python development.

This article explains how to build scalable, cloud-native scientific data processing pipelines using Prefect for workflow orchestration and Dask for parallel computation. It covers cloud-optimized formats (like Zarr), integration with tools like xarray and echopype, and demonstrates end-to-end ETL pipelines that load, process, and store multidimensional data directly in the cloud.

A description of the performance optimizations made to the free-threaded garbage collector for Python 3.14.

This video provides a hands-on guide to building a large language model entirely from scratch in PyTorch, covering every step from core transformer design to advanced alignment with RLHF. By the end, viewers gain practical experience in implementing, training, scaling, and aligning their own custom LLMs.

The migration of a large Bash container test suite to Python using the Cursor AI code editor saved about 1.5 months of development time, with Cursor handling script conversion, function replacement, and automated PyTest suite generation. Although the migration was not entirely smooth and required some manual fixes, the resulting Python test suite passed tests successfully, demonstrating Cursor AI’s effectiveness in accelerating language migration under human supervision.

Django Watchfiles is a library that improves Django's development server by replacing the default autoreloader with the faster, more reliable watchfiles backend. It simplifies setup, enhances reload speed, and brings better cross-platform support with minimal configuration for Django projects.

The post introduces how to use the "kdrag" proof assistant, a Python library, by walking through its approach to constructing and automating proofs with tactics and integration with the Z3 solver. It demonstrates the proof process with examples of inference rules, case analysis, lemma application, and automation for formalizing mathematical logic in Python.

This video analyzes the strengths and weaknesses of the singleton pattern in Python, explaining why global state is risky but controlled instantiation can be valuable in certain cases. It recommends module-level singletons and thread safety measures, while cautioning against tight coupling and testing pitfalls with traditional singleton implementations.

The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data

The Kaggle Grandmasters Playbook presents seven proven techniques for tabular data modeling, emphasizing fast experimentation and careful validation powered by GPU acceleration to handle large-scale data effectively. Key strategies include advanced exploratory data analysis, building diverse baselines, extensive feature engineering, ensembling with hill climbing and stacking, pseudo-labeling to leverage unlabeled data, and extra training with multiple seeds to boost model robustness.

LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inference costs decline.


Interesting Projects, Tools, and Libraries

The new web framework that breathes fresh air into Python web development. Built with FastAPI, Starlette, and Pydantic.

Modular AI runtime for robots.

Universal Feed-Forward Metric 3D Reconstruction

A powerful debugging dashboard for FastAPI applications. Monitor HTTP requests, SQL queries, and exceptions in real-time with a beautiful React UI. One-line integration, zero configuration needed.

enso is a functional programming framework for Python.

Modern Django REST Framework authentication toolkit with JWT cookies, social login, and 2FA support.

Cutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote (speaker identification).

The SQLite for vector embeddings — A simple, embedded vector database that stores everything in a single file.

Open and Advanced Large-Scale Video Generative Models.

A PyTorch library for memory-efficient deep learning that enables training and inference of large models that don't fit in GPU memory.

MCP integration layers that let AI agents use thousands of tools reliably.


Upcoming Events and Webinars

There will be a talk, Modern Tools for Python Development: Code for Collaboration.

There will be a talk, Decoding Spatial Biology with Python.

There will a workshop, Building data pipelines with dbt Core and Snowflake.

There will be a talk, Agents: complexity vs scalability.


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