Welcome to issue 702 of Python Weekly. Let's get straight to the links this week. |
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News |
Kaggle Wins with NVIDIA GPUs → S5E2 | S5E4 | S5E5 |
NVIDIA Kaggle Grandmasters recently clinched gold in three Playground Series competitions by supercharging their workflows with GPU-accelerated Python. From fast feature engineering to blazing model training, these write-ups offer practical insights for any Python dev working with ML. |
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Articles, Tutorials and Talks |
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Stanford researchers show that AI-generated CUDA kernels—created without relying on standard libraries—can now match or even outperform expert-optimized PyTorch kernels on specific tasks, thanks to parallel search and synthetic data generation. Their approach demonstrates that combining strong reasoning with broad exploratory search yields rapid performance gains, highlighting a promising path toward self-improving AI systems for code optimization. |
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The tutorial provides an updated 2025 workflow for building a combined React and Flask application, detailing how to structure, run, and connect a modern React frontend with a Flask backend using current tools and best practices. |
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Think map() and filter() are always better than for loops? Not so fast. This video walks you through four situations where functional code actually makes things worse—and explain why the classic for loop still deserves a place in your toolbox. |
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A deep dive into the workings of Reladiff, exploring the challenges and techniques in data engineering with SQL. |
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The article provides a step-by-step guide to building a personalized, AI-powered running plan using Python, Elasticsearch, and Agno, leveraging your workout history to generate a four-week training schedule. It walks through extracting fitness data, storing it in Elasticsearch, using agentic AI to create a tailored plan, and exporting the results to Notion for easy tracking and progress management. |
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In this video, we'll be learning about the differences between type hinting, type checking, and data validation in Python. These are three concepts that many developers get confused about, so we'll cover what each one does, when to use them, and how they work together. We'll also look at practical examples using tools like mypy for type checking and Pydantic for data validation. By the end of this video, you'll have a clear understanding of these important Python concepts and know when to apply each one in your own projects. |
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Hugging Face’s new co-location feature lets vLLM inference and model training share the same GPUs and process group, eliminating idle GPU time and costly hardware overhead that plagued the old server-based setup. This integrated approach delivers up to 1.73X faster throughput for large language models, maintains model quality, and simplifies scaling—though it requires careful GPU memory management and addresses some version-specific bugs. |
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The post explains how local variables are managed in Python bytecode: they’re stored in reserved slots at the bottom of each function’s stack frame, with the stack holding references to objects on the heap. By walking through a custom Python interpreter in Rust, the author illustrates how compiled bytecode uses indices (not names) to access these slots, demystifying the stack-based storage of locals during execution. |
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The author introduces asncounter, a Python tool that analyzes logs or network traffic to count and group incoming IPs by their Autonomous System Number (ASN), helping identify which organizations are generating the most traffic. It’s designed for quick deployment and practical insight—especially when logs are anonymized or attackers use distributed IPs—making it easier to spot patterns, manage abuse, and optimize peering or blocking strategies. |
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A curated set of 20 concise Pandas one-liners that leverage advanced features—like Arrow-backed dtypes, vectorized eval, and efficient group-by transforms—to optimize common data preprocessing, filtering, and aggregation tasks. These snippets are designed to streamline data analysis workflows on large datasets by reducing memory usage, speeding up computations, and minimizing boilerplate code. |
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Interesting Projects, Tools, and Libraries |
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A collection of production-ready Generative AI Agent templates built for Google Cloud. It accelerates development by providing a holistic, production-ready solution, addressing common challenges (Deployment & Operations, Evaluation, Customization, Observability) in building and deploying GenAI agents. |
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The dead simple Django REST Framework API generator with role-based permissions. |
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Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval. |
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Understanding Image Quality via Visual Reinforcement Learning. |
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Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model. |
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Detect leaked asyncio tasks, threads, and event loop blocking in Python. Inspired by goleak. |
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Advanced multiple dispatch for Python functions. |
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New Releases |
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The Python team has released updates for versions 3.13.4, 3.12.11, 3.11.13, 3.10.18, and 3.9.23, addressing multiple security vulnerabilities—including several critical tarfile CVEs and a use-after-free bug—alongside hundreds of bug fixes and improvements. |
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The Django team has released versions 5.2.2, 5.1.10, and 4.2.22 to address CVE-2025-48432, a moderate-severity vulnerability where unescaped request paths could allow log injection or forgery. |
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Upcoming Events and Webinars |
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There will be following talks |
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There will be following talks |
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There will be following talks |
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There will be a talk, Build a website in Python using Django and Wagtail. |
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There will be following talks |
Natural Capital Mapping in the Water Sector Beyond multiple choice: Using LLMs for Marking and Feedback in Secondary Science Presentation on how AI can be used for fuzzy matching
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