PythonHub

How I write Django views The author advocates using Django's base View class over generic class-based or function-based views for simplicity and flexibility in handling HTTP requests. By avoiding complex mixins and leveraging straightforward helper methods, developers can write clearer, more maintainable view code with minimal cognitive overhead. https://www.loopwerk.io/articles/2025/django-views/

Niche Python tools, libraries and features - whats your favourite? https://www.reddit.com/r/Python/comments/1n7r4xb/niche_python_tools_libraries_and_features_whats/

PageIndex PageIndex is a reasoning-based RAG system that simulates how human experts navigate and extract knowledge from long documents through tree search, enabling LLMs to think and reason their way to the most relevant document sections. https://github.com/VectifyAI/PageIndex

Speeding up PyTorch inference by 87% on Apple devices with AI-generated Metal kernels The post describes how AI models can automatically generate optimized Metal GPU kernels that speed up PyTorch inference on Apple devices by an average of 87% across 215 modules, with some kernels running hundreds of times faster than baseline. Using an agentic swarm approach and adding context like CUDA references and profiling data, the system outperforms standalone models, making kerne... https://gimletlabs.ai/blog/ai-generated-metal-kernels

When You No Longer Need That Object • Dealing With Garbage in Python Let's explore reference counting and cyclic garbage collection in Python. https://www.thepythoncodingstack.com/p/python-garbage-collection-reference-counting-and-cyclic

Python: capture stdout and stderr in unittest The article explains how to capture stdout and stderr during Python unittest runs using contextlib.redirectstdout and redirectstderr, enabling tests to programmatically access console output. It also provides examples and custom context managers to simplify capturing both streams simultaneously, improving test logging and debugging capabilities. https://adamj.eu/tech/2025/08/29/python-unittest-capture-stdout-stderr/

playwright-use playwright-use turns natural-language UI test goals into executable Playwright steps using AI, then produces human-friendly and machine-readable reports with screenshots, video, and traces. https://pypi.org/project/playwright-use/

Build an AI Coding Agent in Python This tutorial teaches how to build a functional agentic AI coding assistant in Python using the free Gemini Flash API, covering agentic loops, tool-calling, file manipulation, and autonomous debugging. By constructing an agent that can read, modify, and execute code, viewers gain practical skills and deep insight into how modern coding agents operate beneath the surface. https://www.youtube.com/watch?v=YtHdaXuOAks

Elysia Elysia is an agentic platform designed to use tools in a decision tree. A decision agent decides which tools to use dynamically based on its environment and context. https://github.com/weaviate/elysia

Scheduling Background Tasks in Python with Celery and RabbitMQ We'll build background tasks using Celery and RabbitMQ to create a weather notification service. https://blog.appsignal.com/2025/08/27/scheduling-background-tasks-in-python-with-celery-and-rabbitmq.html

TIL: Using SQLModel Asynchronously with FastAPI (and Air) with PostgreSQL This post explains how to leverage SQLModel with FastAPI and PostgreSQL to enable fully asynchronous database operations, improving scalability and efficiency for concurrent web applications. Key steps include setting up async database engines and sessions, using dependency injection in FastAPI, and aligning everything with non-blocking patterns. https://daniel.feldroy.com/posts/til-2025-08-using-sqlmodel-asynchronously-with-fastapi-and-air-with-postgresql

oLLM oLLM is a lightweight Python library for large-context LLM inference, built on top of Huggingface Transformers and PyTorch. It enables running models like Llama-3.1-8B-Instruct on 100k context using ~$200 consumer GPU with 8GB VRAM. Example performance: ~20 min for the first token, ~17s per subsequent token. https://github.com/Mega4alik/ollm

Examples of using UV https://www.reddit.com/r/Python/comments/1mx53x9/examples_of_using_uv/

GENIE Experience near-instantaneous speech synthesis on your CPU. https://github.com/High-Logic/Genie

LEANN RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device. https://github.com/yichuan-w/LEANN

Python Hub Weekly Digest for 2025-09-07 https://pythonhub.dev/digest/2025-09-07/

DiffMem Git Based Memory Storage for Conversational AI Agent. https://github.com/Growth-Kinetics/DiffMem

How to Write Great Unit Tests in Python The video teaches how to write effective and maintainable unit tests in Python, focusing on practical techniques such as mocking, monkey patching, fixtures, and parametrization with pytest. It uses a realistic WeatherService example to demonstrate these concepts, emphasizing best practices for robust testing and improving code quality in production systems. https://www.youtube.com/watch?v=EIV_ixKGPmc

From GPT-2 to gpt-oss: Analyzing the Architectural Advances The article analyzes the architectural advances from GPT-2 to OpenAI’s new open-weight gpt-oss models, highlighting innovations like Mixture-of-Experts, grouped query attention, and sliding-window layers for efficiency and scaling. He compares these changes with models like Qwen3 and notes how gpt-oss is optimized for reasoning, tool use, and agentic workflows, while remaining memory-eff... https://magazine.sebastianraschka.com/p/from-gpt-2-to-gpt-oss-analyzing-the

AI Agents for Beginners 11 Lessons to Get Started Building AI Agents. https://github.com/microsoft/ai-agents-for-beginners