Ref: https://gist.github.com/rnwolf/e09ae9ad6d3ac759767d129d52cab1f1
| Key Binding | Description |
|---|---|
| SPC | < space > |
| RET | < return > |
| C | < ctrl > |
| M | < alt >, M stands for Meta |
| // Converts an ArrayBuffer directly to base64, without any intermediate 'convert to string then | |
| // use window.btoa' step. According to my tests, this appears to be a faster approach: | |
| // http://jsperf.com/encoding-xhr-image-data/5 | |
| /* | |
| MIT LICENSE | |
| Copyright 2011 Jon Leighton | |
| Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: |
| <?php | |
| namespace Custom_Reg; | |
| class Custom_Reg { | |
| protected $form; | |
| protected $saver; | |
| function __construct( Form $form, Saver $saver ) { |
| # IE is still braindead so still use favicon.ico | |
| convert -resize x16 -gravity center -crop 16x16+0+0 -flatten -colors 256 input.png output-16x16.ico | |
| convert -resize x32 -gravity center -crop 32x32+0+0 -flatten -colors 256 input.png output-32x32.ico | |
| convert output-16x16.ico output-32x32.ico favicon.ico | |
| # Then, HTML needs to specify size="XxY" as largest size due to browser bugs | |
| <link rel="shortcut icon" href="/favicon.ico" sizes="32x32"> |
Ref: https://gist.github.com/rnwolf/e09ae9ad6d3ac759767d129d52cab1f1
| Key Binding | Description |
|---|---|
| SPC | < space > |
| RET | < return > |
| C | < ctrl > |
| M | < alt >, M stands for Meta |
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.
A practical LLM wiki pattern for vibe-coded apps, AI-built products, and high-trust software systems.
Inspired by Andrej Karpathy's original LLM Wiki.
This is a simpler, gist-native version of the KIOSK idea. It is designed to be copy-pasted into an AI coding agent like Lovable, Cursor, Claude Code, Codex, or similar. The goal is to communicate the concept clearly enough that the agent can build a primitive but useful version directly in your repo.
The original LLM wiki idea is about giving LLMs a persistent knowledge layer instead of making them rediscover everything through RAG every time. KIOSK keeps that idea, but adapts it for codebases, documentation-heavy products, AI-assisted development, and high-compliance software.