donbrae

// Source: ChatGPT 4
function similarText(first, second) {
    // Check for null, undefined, or empty string inputs
    if (first === null || second === null || typeof first === 'undefined' || typeof second === 'undefined' || first.trim().length === 0 || second.trim().length === 0) {
        return { matchingCharacters: 0, similarityPercentage: 0 };
    }
    
    // Type coercion to ensure inputs are treated as strings
    first += '';
    second += '';

mattppal

Frontend Security

	Security Measure	Description
☐	Use HTTPS everywhere	Prevents basic eavesdropping and man-in-the-middle attacks
☐	Input validation and sanitization	Prevents XSS attacks by validating all user inputs
☐	Don't store sensitive data in the browser	No secrets in localStorage or client-side code
☐	CSRF protection	Implement anti-CSRF tokens for forms and state-changing requests
☐	Never expose API keys in frontend	API credentials should always remain server-side

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.