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.

liuran001

# AFF
# 如果你想支持我，可以通过我的邀请链接购买机场
# 感谢支持
# 1. ssLinks 邀请码: fSo2OhzH https://98a6251b6cd7471da86cca993b6dbe6f.36d.biz/#/register?code=fSo2OhzH
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# 一定要填我的邀请码，不填我哭给你看😭

# mihomo (Clash Meta) 懒人配置
# 版本 V1.23-251221

docularxu

在中国使用 Claude Code 解决 403 错误 - 完整指南（中文版）

在中国使用 Claude Code 解决 403 错误

2026 年 2 月 12 日

如果你在中国尝试使用 Claude Code，大概率会撞上 403 错误。本文覆盖三种使用场景的解决方案：

• macOS 终端 (shell) - 在终端里直接使用 claude 命令行
• VS Code 终端 - 在 VS Code 内置终端里使用 claude 命令行

badlogic

import { spawnSync } from "node:child_process";
import fs from "node:fs";
import os from "node:os";
import path from "node:path";
import type { ExtensionAPI, SessionEntry } from "@mariozechner/pi-coding-agent";
import type { AssistantMessage } from "@mariozechner/pi-ai";

function getLastAssistantText(branch: SessionEntry[]): string | undefined {
	for (let i = branch.length - 1; i >= 0; i--) {
		const entry = branch[i];

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, 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.

royosherove

OpenClaw: Architecture Deep Dive (Feb 2026, Opus 4.6)

For Software Architects — How an AI Agent System Actually Works Under the Hood

---

1. Executive Summary

OpenClaw is an open-source, self-hosted AI agent framework that turns large language models into persistent, tool-using assistants with real-world integrations. Unlike chatbot wrappers that simply proxy API calls, OpenClaw implements a full agent runtime with session management, memory persistence, context window optimization, multi-channel messaging, sandboxed tool execution, and event-driven extensibility.

jesstelford

Read Shape Up by basecamp on a kindle / reMarkable / eReader

Basecamp's new book Shape Up is now available online (https://basecamp.com/shapeup) to read page-by-page.

There is a .pdf version, but that's not the best format for Kindle / other eReaders. Instead, we can convert the page-by-page into an eReader friendly format.

Part 1: Convert to a single page

NOTE: This has only been tested on Chrome

t3dotgg

// Types for the result object with discriminated union
type Success<T> = {
  data: T;
  error: null;
};

type Failure<E> = {
  data: null;
  error: E;
};