Python Weekly (Issue 686 February 6 2025)

Author

Rahul Chaudhary
February 06, 2025

Welcome to issue 686 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

Over the past decade, Machine Learning has become ubiquitous, transforming industries and scientific research, while requiring massive computational infrastructure. The talk will explore the evolution of ML systems like PyTorch, discussing how they've adapted to meet growing training demands, optimizing single-GPU performance, and scaling to thousands of GPUs.

The article explores different implementation strategies for "JIT decorator" patterns in Python, focusing on AST-based, bytecode-based, and tracing-based approaches. It demonstrates how these decorators can transform Python functions into domain-specific languages, compiling them to LLVM IR for efficient execution.

Google researchers and Mandiant unveiled ScatterBrain, a sophisticated obfuscation tool used by China-linked hackers to protect POISONPLUG.SHADOW malware. The analysis reveals ScatterBrain's advanced techniques for evading detection and hindering analysis, including control flow obfuscation, instruction mutation, and import protection, while also providing a deobfuscation library to counter these tactics.

This video covers blockchain software development from scratch using Python and Vyper, covering smart contract creation, Python scripting, and interaction with contracts. It also includes advanced topics like fuzzing, NFTs, algorithmic trading, AI, ERC20s, and DeFi, making it suitable for beginners and those looking to expand their blockchain development skills.

Commits are more than just added and removed lines. This tool recognizes updated, moved, and copy-pasted code inside a pull request to produce 30% slimmer PRs. SPONSOR

Quansight Labs's work for the napari project resulted in multiple beneficial side effects for the conda packaging ecosystem.

If you've read a fair amount of Python code, then you've probably seen this "init.py" file pop up quite a few times. It's especially common in larger Python projects. This post breaks down exactly what this file is, when you should use it, and exactly how it works.

Learn how to effectively manage Python content for producing Ansible execution environments.

It takes less than an hour and $3.

This video dives into using uv and Django together for project and dependency management, including how to install packages into different groups and run Django commands.

If you want to save memory when reading from a BytesIO, getvalue() is surprisingly a good choice.

PyPI now supports project archival, allowing maintainers to mark projects as not expecting future updates, helping users make informed decisions about dependencies. Archiving doesn't delete or prevent installation of projects, but signals their status and prevents new uploads, with options for maintainers to provide context and unarchive if needed.

Interesting Projects, Tools, and Libraries

Build specialised ML models from natural language.

A text extraction library supporting PDFs, images, office documents and more.

Open Full-song Music Generation Foundation Model, something similar to Suno.ai but open

FlashLearn provides a simple interface and orchestration (up to 1000 calls/min) for incorporating Agent LLMs into your typical workflows.

A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces.

Everything you need to build state-of-the-art foundation models, end-to-end.

Transform your $20 Cursor into a Devin-like AI Assistant.

A lightweight Python library to create, run and manage macOS and Linux virtual machines (VMs) natively on Apple Silicon.

New Releases

Upcoming Events and Webinars

There will be a talk, Navigating the Web: Lessons from Building Real-World Agents.

There will be a talk, Alumnium - open-source AI-powered test automation.

There will be a talk, Xarray and Rioxarray for Raster Data.

There will be a talk, Python: The gateway drug to DuckDB.

We will be learning how to make API calls to LLMs with Langchain as the first of three workshops on LLM usage.

There will be following talks

  • Docling: A Game-Changer for GenAI Data Ingestion

  • Unlocking the Power of AI: Collaboration and Open Knowledge Sharing for Scientific Progress

  • From o1 to olmo, why benchmark gaming makes open source ML non-negotiable

  • Why risk matters:the 25% tax on ignoring risk in AI based protein design

There will be following talks

  • Symbolic Regression

  • Mind the Gap: Making Technical Concepts Click with Any Audience

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