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.

jinjier

使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

• JavDB TOP250
• JavDB 有码 TOP250
• JavDB 无码 TOP250
• JavDB 欧美 TOP250
• JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

aarondfrancis

Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.

This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.

You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.

1. Establish the coverage contract

Inspect the repository and inventory every identifiable subsystem.

VivianBalakrishnan

OKF Graph Wiki — Agent-Maintained Knowledge Graph for Context Retrieval

I've been running Andrej Karpathy's LLM Wiki pattern for several months — reading sources, compiling them into a compounding, git-diffable wiki instead of re-deriving everything from scratch every session — and I'm a genuine convert. But two things kept nagging at me: query salience (finding the right page reliably, not just something plausible) and token economy (not re-reading half the wiki to answer one question).

adinata-id

Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.

This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.

You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.

1. Establish the coverage contract

Inspect the repository and inventory every identifiable subsystem.

traysr

Commonly used IETF language tags

The IETF language tags are made up of ISO 639 standards for representing language names and ISO 3166-1 standards for representing country codes. For a complete list, refer to links above

Other useful links

1. Language codes list on MSDN website
2. Language and Locale Designations on Apple Developer website.

| English name for language | Tag |

dhyegocalota

Você é especialista em verificação de software com agente: montar o ambiente onde a IA prova o que entregou antes de dizer que terminou.

Regra de ouro: você nunca marca check em item que você mesmo executou. Sem prova crua colada, o item continua aberto. Mock não conta como prova.

FASE 1, PLANO DE TESTE COMPLETO Antes de escrever qualquer código, escreva docs/prd//test-plan.md cobrindo:

• todo fluxo do usuário, passo a passo, com o resultado esperado de cada passo
• as funcionalidades óbvias e principalmente as NÃO óbvias: estado de carregamento, mensagem de erro visível pro usuário, foco e teclado, recarregar no meio do fluxo, voltar do navegador, clique duplo, e tudo que já existia e não pode quebrar
• os edge cases: campo vazio, valor zero, valor negativo, texto onde espera número, lista vazia, item duplicado, sessão expirada, rede lenta, resposta de erro da API Cada item vira uma linha com caixa de check e um id curto. Se você não conseguir imaginar como aquele item quebra, ele ainda não está be

r17x

Design Thinking

X → Graph → Effect<A, E, R>
│              │   │  │  │
│              │   │  │  └─ what each node needs     (§5)
│              │   │  └──── where the graph breaks   (§4)
│              │   └─────── what flows through nodes  (§2)
│              │
│ └─ nodes = functions, edges = data flow

davenicoll

#!/bin/bash
set -euo pipefail

GITHUB_USER=""
GITHUB_TOKEN=""
RAINDROP_API_TOKEN=""
RAINDROP_COLLECTION_ID=
BATCH_SIZE=100

github_graphql() {