PythonHub

apfel Apple Intelligence from the command line. On-device LLM via FoundationModels framework. No API keys, no cloud, no dependencies. https://github.com/Arthur-Ficial/apfel

Qdrant: Perfect Vector Store For RAG in Python This tutorial demonstrates how to use Qdrant, a specialized vector search engine, to combine semantic similarity with hard metadata filtering in Python for more precise RAG applications https://www.youtube.com/watch?v=DWP_-jMTNH0

Overglade-Badges Zero power PCB hackathon badges. https://github.com/KaiPereira/Overglade-Badges

I built a civic transparency platform with FastAPI that aggregates 40+ government APIs https://www.reddit.com/r/Python/comments/1sb6vly/i_built_a_civic_transparency_platform_with/

Decentralized AI in 50 Lines of Python A peer-to-peer AI that answers your friends' messages using local data and privacy controls. https://iamtrask.github.io/2026/04/07/decentralized-ai-in-50-lines/

dj-signals-panel Display registered Django signals and receivers, showing what fires and where. https://github.com/yassi/dj-signals-panel

A WYSIWYG word processor in Python https://codeberg.org/chrisecker/miniword

I Replaced Kafka, Redis, and RabbitMQ With One Tool. Here’s What I Learned. The post’s core idea is that NATS JetStream can replace separate Kafka, Redis, and RabbitMQ setups by covering streaming, queueing, and pub/sub in one system. It argues this can simplify architecture and reduce the operational overhead of running multiple messaging tools for different workloads. https://scalebites.substack.com/p/i-replaced-kafka-redis-and-rabbitmq

void-model An open-source video editing model that removes objects from footage and also models the scene’s resulting physical interactions, not just the missing pixels. https://github.com/Netflix/void-model

code-review-graph Local knowledge graph for Claude Code. Builds a persistent map of your codebase so Claude reads only what matters - 6.8× fewer tokens on reviews and up to 49× on daily coding tasks. https://github.com/tirth8205/code-review-graph

profiling-explorer Table-based exploration tool for Python profiling data (pstats files). https://github.com/adamchainz/profiling-explorer

GuppyLM A ~9M parameter LLM that talks like a small fish. https://github.com/arman-bd/guppylm

caveman A Claude Code skill/plugin and Codex plugin that makes agent talk like caveman - cutting ~75% of output tokens while keeping full technical accuracy. Plus a companion tool that compresses your memory files to cut ~45% of input tokens every session. https://github.com/JuliusBrussee/caveman

Components of A Coding Agent The post breaks down coding agents into a few core parts: a model, tools, instructions, and a loop for planning, acting, and verifying. Its main point is that good coding agents are less about raw model power and more about how well they coordinate context, tool use, and iterative feedback. https://magazine.sebastianraschka.com/p/components-of-a-coding-agent

Enforce Business Logic in the Database with Django Use transactions, locks, and constraints in Django to enforce business logic in the database. https://lincolnloop.com/blog/enforce-business-logic-in-the-database-with-django/

Python Hub Weekly Digest for 2026-04-12 https://pythonhub.dev/digest/2026-04-12/

Making friendly classes A friendly class accepts sensible arguments, has a nice string representation, and supports ... https://www.pythonmorsels.com/friendly-classes/

Timesliced reservoir sampling: a new(?) algorithm for profilers Reservoir sampling lets you pick a sample from an unlimited stream of events; learn how it works, and a new variant useful for profilers. https://pythonspeed.com/articles/reservoir-sampling-profilers/

OpenSpace Make Your Agents: Smarter, Low-Cost, Self-Evolving. https://github.com/HKUDS/OpenSpace

From zero to a RAG system: successes and failures Building a production RAG system is far more about data pipelines, indexing strategy, and infrastructure tradeoffs than model choice, with most failures coming from scaling, retrieval quality, and compute constraints. The key lesson is that RAG success depends on iterative engineering and system design discipline, not just plugging in an LLM, with real-world performance shaped by bottlen... https://en.andros.dev/blog/aa31d744/from-zero-to-a-rag-system-successes-and-failures/