soderlind

1) Install cloudflared using homebrew:

brew install cloudflare/cloudflare/cloudflared

2) Create /usr/local/etc/cloudflared/config.yaml, with the following content

proxy-dns: true
proxy-dns-upstream:
  - https://1.1.1.1/dns-query
  - https://1.0.0.1/dns-query

hermanbanken

FROM nginx:alpine AS builder

# nginx:alpine contains NGINX_VERSION environment variable, like so:
# ENV NGINX_VERSION 1.15.0

# Our NCHAN version
ENV NCHAN_VERSION 1.1.15

# Download sources
RUN wget "http://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz" -O nginx.tar.gz && \

superseb

k3s etcd commands

etcd

Setup etcdctl using the instructions at https://github.com/etcd-io/etcd/releases/tag/v3.4.13 (changed path to /usr/local/bin):

Note: if you want to match th etcdctl binaries with the embedded k3s etcd version, please run the curl command for getting the version first and adjust ETCD_VER below accordingly:

curl -L --cacert /var/lib/rancher/k3s/server/tls/etcd/server-ca.crt --cert /var/lib/rancher/k3s/server/tls/etcd/server-client.crt --key /var/lib/rancher/k3s/server/tls/etcd/server-client.key https://127.0.0.1:2379/version

karpathy

LLM Wiki

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.

The core idea

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.