Python Weekly (Issue 713 September 11 2025)

Author

Rahul Chaudhary
September 11, 2025

Welcome to issue 713 of Python Weekly. Let's get straight to the links this week.

Don’t be the one putting your company at risk 😳

Most meetings include confidential data - from project details to client information.

But not every AI notetaker protects that information with the security it deserves.

If the AI notetaker you’re using trains their AI models on your team’s conversations, you could be putting your company at risk without realizing it.

This AI Meeting Notetaker Security Checklist helps you avoid that.

In just two minutes, you’ll learn the 7 checks to ensure your team’s AI meeting notes stay private and secure.

Don’t let your meetings become someone else’s dataset.


Articles, Tutorials and Talks

This is a zero-to-one guide on scaling modern transformers with n-dimensional parallelism. Transformers have driven much of the deep learning revolution, yet no practical guide reflects SOTA architectures and the complexities of large-scale language modelling. While excellent resources such as DeepMind’s How to Scale Your Model and HuggingFace’s Ultra Scale Playbook exist, a gap remains between theory and end-to-end implementation. We aim to bridge that gap by showing you how to scale a model from scratch (in Jax, with code) to current standards.

The video highlights 10 powerful, sometimes overlooked Python standard library modules such as dataclasses for reducing boilerplate, pathlib for modern file path handling, functools for caching and functional utilities, and tomllib for parsing TOML configs. It showcases practical code examples illustrating how these modules can simplify development, improve performance, and reduce dependencies in Python projects.

This guide will walk you through building a robust logging system with Loguru. We'll directly address the patterns and pain points of the standard logging module and show how Loguru simplifies them.

Python 3.13 introduced a completely redesigned REPL with a much more modern feel. It's easy to overlook many of the features in the new Python REPL. Let's explore the various hidden features the new Python REPL supports, with a focus on tips that are useful for everyday usage.

The article provides a detailed walkthrough of using Hound, an AI-powered code auditing tool that builds adaptive knowledge graphs to model and analyze software systems, enabling detection of security bugs with reasoning across multiple code layers and ongoing refinement of findings. It demonstrates how Hound automates security audits from graph building to hypothesis generation, verification, and report generation, accelerating vulnerability discovery in complex projects like the Rustic Rust server.

This guide provides a comprehensive walkthrough for developers looking to integrate Gemini 2.5 Flash Image aka Nano Banana into their applications using the Gemini Developer API.

The post details how Bazel can optimize building Python projects by managing dependencies accurately, avoiding unnecessary rebuilds, and supporting multi-language setups like adding Rust extensions. Bazel’s declarative build configurations enable efficient and consistent builds, improving CI turnaround times and scalability as projects grow[attached file].

A recap on the work done in Python 3.14 to enable asyncio to scale on the free-threaded build of CPython.

Get started with local Large Language Models for R and Python using free tools like Ollama, ellmer, and chatlas for private and efficient AI-powered workflows.

The video demonstrates how to integrate Pydantic AI with Django to add agent-like features to web applications, allowing developers to enrich chatbots with app-specific context and tools such as real-time weather services and database interactions[attached file]. It showcases building customizable agents that can use dependencies, execute functions, and securely manipulate database records, enabling natural language interfaces for enhanced user experiences.

Testing the compiler optimizations your code relies on

Sometimes you can trick the compiler into generating more efficient code. How can you test this optimization continues to be applied?

This course explores key techniques for adapting pre-trained LLMs to specialized tasks, including supervised fine-tuning, RLHF, and efficient QLoRA-based methods. It covers both theory and hands-on implementation, guiding viewers through practical steps to fine-tune and deploy massive language models using Python, PyTorch, and Hugging Face tools


Interesting Projects, Tools, and Libraries

Agent-Oriented Programming for Building LLM Applications.

Practical Dual-Arm Mobile Home Robot for $660.

AWS for Python devs - made simple.

Toolkit to help you get started with Spec-Driven Development.

Starplot is a Python library for creating star charts and maps of the sky.

An open-source, all-in-one desktop application for network engineers, students, and enthusiasts.

A unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.

Your Ultimate Virtual Assistant.

Building General-Purpose Robots Based on Embodied Foundation Model.


Upcoming Events and Webinars

PyCon NL brings together Python enthusiasts, professionals, and learners for a dynamic celebration of the language’s possibilities. The event features insightful talks, hands-on tutorials, and rich networking, building on 4 years of successful conference experience.

There will be following talks

  • How to present your presentation (and other suggestions for software interviews)

  • Dictionary Subclasses in Python

  • Test-Driven Development in the AI Era: When Goats Meet Robots

There will be following talks

  • Taking a RAG application to 10k users: Lessons from exciting tech and meaningful impact

  • From Medical Robotics to Public Innovation: Data-Driven Learning with VRobotia

There will be a talk, Introduction to Kafka Connect.


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