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- Python Weekly (Issue 730 January 29 2026)
Python Weekly (Issue 730 January 29 2026)
Rahul Chaudhary
January 29, 2026
In partnership with
Welcome to issue 730 of Python Weekly. Let's get straight to the links this week.
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Articles, Tutorials and Talks
The tutorial walks through building pipeline parallelism from the ground up, explaining how to split large AI models and training workloads across multiple GPUs to improve training efficiency. It breaks down concepts with step-by-step examples so developers can understand how data and compute are partitioned and coordinated in a distributed training system.
Dropbox’s VP of Engineering explains how the company built Dropbox Dash, an AI-driven cross-app search and knowledge system that uses indexing, knowledge graphs, and contextual reasoning to unify work content from many tools into a single, context-aware platform. The piece highlights engineering decisions about when to use indexed retrieval over federated approaches, how MCP tool calls affect performance, and how LLMs and prompt optimizers like DSPy help improve result relevance and system efficiency.
pandas 3.0 has just been released. This article uses a real-world example to explain the most important differences between pandas 2 and the new pandas 3 release, focusing on performance, syntax, and user experience.
Scattered business rules and duplicated conditionals slowly rot a codebase by drifting out of sync and creating subtle bugs. The video shows how refactoring with the Specification Pattern makes rules composable, testable, and even configurable as data instead of hardcoded logic.
The author benchmarks and improves Pillow’s image open/save performance by avoiding unnecessary plugin imports and using lazy loading, leading to large speed gains in Python. Results show opening PNG images can be around 2.6 times faster and WebP up to 14 times faster, with similar improvements in saving images, and the changes are included in upcoming Pillow releases.
Learn about 4 ways Pyrefly narrows types, reducing the need to explicitly cast in your code.
The author built a multiplayer Snake game in the browser using only Python and Django LiveView, with no custom JavaScript, by keeping game state on the server and broadcasting rendered HTML over WebSockets.
In this video, you'll learn how to use the Matplotlib library in Python. If you're interested at all in data science, AI, machine learning, or scientific computing, then Matplotlib is a must learn.
The post explains how Python’s long-standing busy-loop approach to waiting on child processes has finally been replaced with true event-driven waiting using OS primitives like pidfd_open() with poll() on Linux and kqueue() on BSD/macOS. This change eliminates constant polling, reduces CPU wakeups, and has now been adopted both in psutil and upstream into Python’s standard subprocess module for more efficient process management.
A worked example of packaging a from-scratch GPT-2-style model for the Hugging Face Hub so it loads via from_pretrained, runs with pipeline, and trains with Trainer -- with notes on tokeniser gotchas.
The article shows how to build a real-time product recommender prototype in Python using Contextual Multi-Armed Bandits to simulate user behavior and validate online learning algorithms like LinUCB. It explains why bandits handle cold-start and context better than traditional models, walks through data generation, feature engineering, offline evaluation, and sets up a live simulation as the foundation for a scalable recommender.
Django’s field lookups are one of the ORM’s best features, but time-based lookups can quietly bypass database indexes, turning fast queries into expensive full table scans.
This is part one of a series covering core DynamoDB concepts and patterns, all the way up to single-table design; the goal is to get you to understand idiomatic usage and trade-offs in under an hour. Today, we're looking at what DynamoDB is and why it is that way.
Production RAG for legacy systems: model-agnostic reranking validated across four LLM families. Real metrics, no vendor lock-in, 7,432 pages to 3s queries.
Interesting Projects, Tools, and Libraries
Kimi Code CLI is an AI agent that runs in the terminal, helping you complete software development tasks and terminal operations. It can read and edit code, execute shell commands, search and fetch web pages, and autonomously plan and adjust actions during execution.
Algorithm powering the For You feed on X.
Outcome driven agent development framework that evolves.
Microcode is an efficient terminal-based AI agent with an internal REPL environment for coding assistance. It leverages Reasoning Language Models (RLMs) to help developers with coding tasks directly from the command line.
Reactive Declarative State Management Library for Python - automatic dependency tracking and reactive updates for your application state.
bzfs is a reliable near real-time, parallel replication and backup command-line tool for ZFS.
A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines.
Learning to Discover at Test Time.
High-performance rust powered websocket server for Python.
Polymcp provides a simple and efficient way to interact with MCP servers using custom agents.
Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses Stream's edge network for ultra-low latency.
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
Upcoming Events and Webinars
There will be following talks
SVD-ROM: Reduced Order Modeling of huge arrays using the Singular Value Decomposition
From Zero to HyperPod: Cutting Infrastructure Complexity for Distributed Model Training on AWS
Agentic, Agentic AI for Personalized Care — Lessons and Challenges from Holisticare.io
Mapping the International PyData Community featuring Web Scraping and Data Wrangling
There will be a talk, Quantum vs Classical Randomness: What Is Truly Random?
There will be a talk, Intro to Python.
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