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Python Hub Weekly Digest for 2026-09-13
K-Dense-AI / scientific-agent-skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 190,000+ scientists worldwide. 165 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
OpenExecutive
AI-powered virtual executive team, a single coherent executive persona backed by 8 specialist Claude agents (FastAPI + Next.js).
Fine-Tuning SOTA Object Detection Models on Real-World Datasets
Learn how to use these models, how to fine-tune them on diverse, specialized datasets that look nothing like their training data, and how to evaluate the results – all within PyCharm.
Neocarta
Library built for generating semantic layer graphs for query routing, query generation and data discovery.
💖 Most Popular in the Last Two Weeks
HTTPX2 – A next-generation HTTP client for Python
K-Dense-AI / scientific-agent-skills
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 190,000+ scientists worldwide. 165 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Building AI Agents in Pure Python - Beginner Course
Build a fully functioning AI agent from scratch in pure Python, without frameworks, third-party tools, or vibe coding. The course focuses on the core mechanics behind agents so you understand how they work under the hood, not just how to prompt existing tools.
IPython is All You Need
Nathan Cooper shows how to turn IPython into a full terminal shell that runs Bash commands without !, displays images, and retains rich execution history. He then adds an AI assistant that can understand shell context, outputs, images, and errors, and safely execute Python and Bash commands through allowlisted tools.
KV, Prefix, Prompt and Semantic Caching in LLMs, clearly explained
The post explains the four caching layers in LLM stacks: KV cache, prefix caching, prompt caching, and semantic caching, covering what each stores, their trade-offs, interactions, and the most common issues that prevent reuse. It includes first-principles explanations, code demos (with transformers and vLLM-style logic), production pitfalls, and practical takeaways for reducing recomputa...
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