brunolemos

(() => {
  let count = 0;

  function getAllButtons() {
    return document.querySelectorAll('button.is-following') || [];
  }

  async function unfollowAll() {
    const buttons = getAllButtons();

SilentEchoGM

G91 ; use relative positioning
G0 Z5; move Z up 5mm prior to homing

M117 Heating bed and hot end... ; Message
M140 S[first_layer_bed_temperature] ; set bed temp
M104 S80 ; set hot end to a relatively low temp to save time after levelling
M190 S[first_layer_bed_temperature] ; wait for bed temp

M117 Waiting for bed expansion ; Message
G4 S60 ; wait 1 minute for the bed to expand

Cube707

A GitHub Markdown Cheatsheet

This show usable Markdown features on GitHub as a side by side comparison. This focuses on Markdown files, while most of also works in comments etc., there are some differences, so keep that in mind.

Table of Contents

Headings
Line Breaks
Inline text formatting
Links

coolaj86

Chat GPT "DAN" (and other "Jailbreaks")

• https://chat.openai.com/
• Is ChatGPT "DAN" Real? Gonna find out [Part 1]
  (https://www.youtube.com/watch?v=-q8woRG9FrI)
• Part 2: I thought ChatGPT DAN was a hoax, but...
  (https://www.youtube.com/watch?v=rHZRrDu3A2U&lc=UgxfrxX8aK7gnCzkend4AaABAg)

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.