Basic Memory

Plain Markdown files, semantic connections, and a knowledge graph that grows with every conversation. Open source, local-first, works with any AI.

https://basicmemory.com

neka-nat / freecad-mcp

FreeCAD MCP(Model Context Protocol) server

https://github.com/neka-nat/freecad-mcp

ShieldFont

An open-source typeface that protects written work by poisoning unauthorized AI training datasets.

https://github.com/isaqueseneda/shieldfont

15 Python Libraries That Deserve More Attention

This is a roundup of 15 Python libraries that can make code cleaner, faster, and easier to maintain across areas like configuration, serialization, testing, scheduling, and UI development. It also highlights practical tools for dependency injection, state machines, data analysis, and more reliabledatetimehandling.

https://www.youtube.com/watch?v=ssLO99uwPWI

claude-ads

Claude-first, portable paid-media operations for agencies, consultants, and in-house performance teams.

https://github.com/AgriciDaniel/claude-ads

SimplifyJobs / Summer2027-Internships

Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC.

https://github.com/SimplifyJobs/Summer2027-Internships

Tencent / AI-Infra-Guard

A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.

https://github.com/Tencent/AI-Infra-Guard

Origintracer

A low-level asynchronous web framework observability system. The system can trace nginx, gunicorn, Uvicorn, django and celery.

https://origintracer.app/docs/

When str.lower() is a security vulnerability in Python

A Python security vulnerability caused IDNA 2003 handling to use the interpreter’s current Unicode case-folding rules instead of the Unicode 3.2 rules required by the specification.The fix makes Python’s case-folding behavior match Unicode 3.2 for StringPrep, restoring standards-compliant IDNA encoding and addressing CVE-2026-17084.

https://sethmlarson.dev/when-str-lower-is-a-security-vulnerability

The Mullet Stack - JavaScript in the front, Python in the back.

This is a practical guide to building a modern full-stack application with FastAPI and Pydantic on the Python backend and React and TypeScript on the frontend. It explores how their different type systems work together and shows how generating frontend types from FastAPI’s OpenAPI schema prevents backend and frontend models from drifting apart.

https://seanhelvey.com/mullet-stack/guide/

What's missing to have reproducible builds on PyPI

The article explores what Python packaging is missing to support reproducible builds on PyPI, allowing third parties to verify that distributions match their source code and haven't been tampered with. It proposes recording source information and build tools, using SBOMs, and having trusted verifiers report successfully reproduced packages back to PyPI.

https://snarky.ca/whats-missing-to-have-reproducible-builds-on-pypi/

AIPerf

AIPerf is a comprehensive benchmarking tool that measures the performance of generative AI models served by your preferred inference solution.

https://github.com/ai-dynamo/aiperf

EuroPython 2026 Videos

The videos from the EuroPython 2026 talks are now available.

https://www.youtube.com/playlist?list=PLd3Y9yzyC5Uo

JupyterGIS 0.16: New visualization capabilities, collaborative Story Maps, and more

JupyterGIS 0.16 adds richer geospatial visualization with collaborative Story Maps, openEO layers, lazy Xarray rendering, new symbology, and support for GeoZarr and GeoPackage. The release also expands real-time collaboration to vector editing and introduces an R API alongside the existing Python workflow.

https://blog.jupyter.org/jupytergis-0-16-new-visualization-capabilities-collaborative-story-maps-and-more-03e6b78bacc0

Reinforcement Learning for LLMs: The Complete Guide

This is a comprehensive guide to reinforcement learning for LLMs, starting from first principles and building through the policy-gradient algorithms used in modern LLM training. It covers RLHF, verifiable rewards, REINFORCE, PPO, and the newer RL methods being used to improve reasoning, agents, reliability, and efficiency.

https://cameronrwolfe.substack.com/p/llm-rl

DBLift

DBLift is a Python-native database migration toolkit that brings application-style safety to schema changes with validation, dry runs, SQL previews, rollback support, and CI/CD integration.

https://github.com/dblift/dblift

How to Set Up Python for Machine Learning (The Right Way)

AI-generated ML code can run successfully while still containing subtle mistakes that waste hours or days of training. The video shows how to set up a reliable Python ML workflow with the right tools, validation steps, and verification habits to catch problems before expensive training runs.

https://www.youtube.com/watch?v=cAtKtvmds1o

Nifty Django Feature: Counting on Multiple Columns

Django’s Count expression only works on a single column, which makes counting distinct combinations such as unique pet-vet pairs awkward. A custom CountSubquery shows how Django’s expression system can generate the needed SQL while staying inside the ORM instead of dropping to raw SQL.

https://www.better-simple.com/django/2026/08/19/nifty-feature-counting-on-multiple-columns/