This guide is for homelab admins who understand IPv4s well but find setting up IPv6 hard or annoying because things work differently. In some ways, managing an IPv6 network can be simpler than IPv4, one just needs to learn some new concepts and discard some old ones.
Let’s begin.
First of all, there are some concepts that one must unlearn from ipv4:
Concept 1
| version: '3' | |
| services: | |
| app: | |
| image: nginx:alpine | |
| ports: ["80:80"] | |
| restart: unless-stopped | |
| restarter: | |
| image: docker:cli | |
| volumes: ["/var/run/docker.sock:/var/run/docker.sock"] |
It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive
There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support
to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder
which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!
Evidence-based maternal-fetal medicine references for clinicians, educators, and physician developers
Important
These resources are intended for professional education and clinical decision-support development. They do not replace the complete source guidelines, institutional protocols, consultation, or individualized clinical judgment.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
Wiki LLM for Multi-Agent Builds — extensions to Karpathy's LLM Wiki pattern for identity, security, concurrency, integrity enforcement, and framework self-improvement
A field report from multi-agent production Built on Andrej Karpathy's LLM Wiki pattern Toolkit repository: github.com/redmizt/wiki-llm-for-multi-agent-builds
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
