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.

wookiehangover

@supports (animation-timeline: scroll()) {
    :root {
      --scroll-fog-size: 120;
    }
    .scroll-fog {
      animation:
        mask-up both linear,
        mask-down both linear;
      animation-timeline: scroll(self);
      animation-range:

TheAwesomeTheory

Open Mind: Shared Memory Across Every LLM You Use

The Problem

Every LLM conversation starts from zero. You tell Claude about your project on mobile, then open Desktop and it has no idea. You build context in Claude Code, switch to the web app, gone. Vendor "memory" features are opaque, bounded, non-portable, and siloed per-instance.

Your LLMs have amnesia, and you're the one paying for it — repeating yourself across every session.

The Idea

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

garrytan

Garry Tan's Mega Plan Review Mode

name: plan-mega-review
version: 2.0.0
description: |
  The most thorough plan review possible. Three modes: SCOPE EXPANSION (dream big,
  build the cathedral), HOLD SCOPE (review what's here with maximum rigor), and
  SCOPE REDUCTION (strip to essentials). Context-dependent defaults, but when the
 user says EXPANSION — go full send. Challenges premises, maps every failure mode,

montasim

KFZUS-F3JGV-T95Y7-BXGAS-5NHHP
T3ZWQ-P2738-3FJWS-YE7HT-6NA3K
KFZUS-F3JGV-T95Y7-BXGAS-5NHHP
65Z2L-P36BY-YWJYC-TMJZL-YDZ2S
SFZHH-2Y246-Z483L-EU92B-LNYUA
GSZVS-5W4WA-T9F2E-L3XUX-68473
FTZ8A-R3CP8-AVHYW-KKRMQ-SYDLS
Q3ZWN-QWLZG-32G22-SCJXZ-9B5S4
DAZPH-G39D3-R4QY7-9PVAY-VQ6BU
KLZ5G-X37YY-65ZYN-EUSV7-WPPBS

shirley-yp

name	substance-writing-review
description	Use when reviewing, rewriting, or quality-checking essays, articles, scripts, posts, or long-form drafts for substance-first writing in the spirit of On Writing Well: clear ideas, concrete evidence, reader respect, no AI tone, no mystification, and no empty rhetorical packaging.

Substance Writing Review