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import Foundation
extension String {
//Know if self is only composed by numbers
var isNumber: Bool {
return !self.isEmpty && CharacterSet.decimalDigits.isSuperset(of: CharacterSet(charactersIn: self))
}
}
//Struct to check Regular Expresions
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hsleedevelop / History|-10016df2|entries.json
Last active January 31, 2026 01:37
Visual Studio Code Settings Sync Gist
{"version":1,"resource":"file:///Users/hsleeathome/Documents/Projects/web/react-sass-youtube-clone/src/scss/layout/index.scss","entries":[{"id":"wrcu.scss","timestamp":1705838985262},{"id":"suus.scss","timestamp":1705839013080},{"id":"NyEW.scss","source":"undoRedo.source","timestamp":1705839014151},{"id":"H4JN.scss","timestamp":1705839017424},{"id":"FAgg.scss","timestamp":1705839074268}]}
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hsleedevelop / llm-wiki.md
Created April 16, 2026 16:30 — forked from karpathy/llm-wiki.md
llm-wiki

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.