http://www.willa.me/2013/11/the-six-most-common-species-of-code.html
| #!/usr/bin/env bash | |
| set -o errexit | |
| set -o nounset | |
| set -o pipefail | |
| # Automatically update your CloudFlare DNS record to the IP, Dynamic DNS | |
| # Can retrieve cloudflare Domain id and list zone's, because, lazy | |
| # Place at: | |
| # /usr/local/bin/cf-ddns.sh |
| #!/bin/sh | |
| for dir in $(ls -d */) | |
| do | |
| if [ -d "$dir"/.git ]; then | |
| echo "$dir" && cd "$dir" && git pull && cd .. | |
| fi | |
| done |
NOTE: the list has moved to https://github.com/sketchplugins/plugin-directory
A list of Sketch plugins hosted at GitHub, in no particular order.
- brandonbeecroft/Lorem-Ipsum-Plugin-for-Sketch This is a plugin for quickly creating Lorem Ipsum text in Sketch
- sebj/Sketch Templates and Plugins for Sketch by Bohemian Coding
- FredericJacobs/crop_Artboard A script to export the Sketch App artboards to the clipboard
- almonk/SketchGit A simple Git client built right into Sketch.
What I did to get Python 3.4.2 on Ubuntu 14.04. The stock version of Python 3 on Ubuntu is 3.4.0. Which is missing some of the best parts! (asyncio, etc). Luckily I discovered pyenv which solved my problem.
Pyenv (not to be confused with pyvenv) is the Python equivelant of rbenv. It lets you configure which Python environment/version is available per directory, user, or other session variables.
I followed the instructions here to install pyenv in my home directory. Verbatem, those instructions are:
sudo apt-get install git python-pip make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev
| * { | |
| font-size: 12pt; | |
| font-family: monospace; | |
| font-weight: normal; | |
| font-style: normal; | |
| text-decoration: none; | |
| color: black; | |
| cursor: default; | |
| } |
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.