PythonHub

Build an AI Agent with Python, Django, LangGraph, and Permit This video demonstrates how to build AI agents using Django for data management, LangGraph for agent coordination, andPermit.iofor robust access control, enabling safe, granular, and flexible interactions with user data and APIs. The course walks through integrating these tools step-by-step, empowering developers to create scalable, multi-agent systems with strong permissions and rapid p... https://www.youtube.com/watch?v=rir9B0ZShug

Python Hub Weekly Digest for 2025-06-15 https://pythonhub.dev/digest/2025-06-15/

Create your customized running plan: A step-by-step guide using Python, Elasticsearch, and Agno 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 ... https://allthingsopen.org/articles/step-by-step-guide-python-elasticsearch-agno-agentic-ai-create-running-plan

Test Postgres in Python Like SQLite https://github.com/wey-gu/py-pglite

TurboDRF The dead simple Django REST Framework API generator with role-based permissions. https://github.com/alexandercollins/turbodrf

Create a React + Flask Project in 2025 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. https://blog.miguelgrinberg.com/post/create-a-react-flask-project-in-2025

Should You Replace Every For Loop With Map and Filter? 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. https://www.youtube.com/watch?v=ylzo04lU9Xs

lmms-eval Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval. https://github.com/EvolvingLMMs-Lab/lmms-eval

PyDoll – Async Python scraping engine with native CAPTCHA bypass https://github.com/autoscrape-labs/pydoll

pyleak Detect leaked asyncio tasks, threads, and event loop blocking in Python. Inspired by goleak. https://github.com/deepankarm/pyleak

Surprisingly Fast AI-Generated Kernels We Didn’t Mean to Publish (Yet) 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 promisin... https://crfm.stanford.edu/2025/05/28/fast-kernels.html

Publish a Python Wheel to GCP Artifact Registry with Poetry https://sergiolema.dev/2025/06/09/publish-a-python-wheel-to-gcp-artifact-registry-with-poetry/

How local variables work in Python bytecode 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 stora... https://fromscratchcode.com/blog/how-local-variables-work-in-python-bytecode/

20 Pandas One-Liners That Can Save You Hours of Work 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... https://www.nb-data.com/p/20-pandas-one-liners-that-can-save

No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL 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 ... https://huggingface.co/blog/vllm-colocate

Traffic meter per ASN without logs 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, ... https://anarc.at/blog/2025-05-30-asncounter/

BioReason Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model. https://github.com/bowang-lab/BioReason

Python Tutorial: Type Hinting vs Type Checking vs Data Validation - What’s the Difference? 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 e... https://www.youtube.com/watch?v=fM4O9bModsE

Q-Insight Understanding Image Quality via Visual Reinforcement Learning. https://github.com/bytedance/Q-Insight

Python Hub Weekly Digest for 2025-06-08 https://pythonhub.dev/digest/2025-06-08/