awestmoreland

// Set sizes without units
$basefont: 20;
$total-width-px: 1169;
$column-width-px: 72;

// Concatenation of units by addition results in conversion to string. This is bad
$basefont-px: $basefont+'px';  // = "20px"

// Conversion to pixels using multiplication
$basefont-px: $basefont*1px;  // = 20px;

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.

diegomarino

# adversarial audit prompts - fetch & run
#
# This gist contains three generic audit prompts:
# - fable-audit-codebase.txt: codebase/implementation audit.
# - fable-audit-docs.txt: documentation audit.
# - fable-audit-process.txt: end-to-end workflow/process audit.
#
# These files are /goal specs. The launcher below only NAMES the file; the agent
# opens it with its own Read tool, so the ~4KB /goal size limit never touches the
# prompt body. Keep the launcher tiny and let the file on disk carry the detail.