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.

robsteriam

AZ-104: Microsoft Azure Administrator Study Guide

Additional Learning Resources

Documentation

• Microsoft Azure Administrator | Microsoft Docs
• Microsoft Study Guide | AZ-104
• AZ-104 Study Guide | Document belongs to Jordi Koenderink
• Azure Documentation

darkedges

Opus Orchestrator + Sonnet Subagent Workflow for Claude Code

Run Claude Code with Opus as the orchestrator and Sonnet as the implementation/review subagents — keeping the main session focused and cheap while delegating the heavy lifting to isolated workers.

---

File Layout

.claude/

dbilanoski

shariqriazz

import { tool } from "@opencode-ai/plugin"

export default tool({
  description: "Execute a search query using Tavily Search API to get real-time web search results",
  args: {
    query: tool.schema.string().describe("The search query to execute with Tavily"),
    topic: tool.schema.enum(["general", "news", "finance"]).default("general").describe("The category of the search"),
    search_depth: tool.schema.enum(["basic", "advanced"]).default("basic").describe("The depth of the search - advanced costs 2 credits, basic costs 1"),
    max_results: tool.schema.number().min(0).max(20).default(5).describe("The maximum number of search results to return"),
    include_answer: tool.schema.boolean().default(false).describe("Include an LLM-generated answer to the provided query"),

aamiaa

Caution

As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.

Some users have received the following system message:

There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.

Complete Recent Discord Quest