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.

mendhak

$rawdisks = gwmi win32_diskdrive | where {$_.partitions -eq 0}
foreach ($r in $rawdisks)
{
    $available=ls function:[d-z]: -n | ?{ !(test-path $_) } | SELECT -First 1
    $diskIndex = $r.Index
    Write-Host "Initializing Disk $diskIndex as $available. This will take a while."
    (echo "list disk
    select disk $diskIndex
    online disk
    attributes disk clear readonly

eddvilla486-lang

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.

wobedi

import type { Stripe } from 'stripe';

export type Replace<T, K extends object> = Omit<T, keyof K> & K;

/** https://stripe.com/docs/expand#with-webhooks */
export interface StripeSubscriptionUnexpanded extends Stripe.Subscription {
  customer: string;
}

export interface StripeSubscriptionEvent extends Stripe.Event {

k16shikano

name	japanese-tech-writing
description	日本語の技術文書・書籍原稿の文章規範。整形（一文一行、引用ブロック、脚注、コラム記法）、段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

整形

wovenstarlight

<!-- Chapter 1: Basic light mode and dark mode -->
<!-- FOR DARK MODE (separate "discord" and "darkmode/lightmode" by a single space) -->
<div class="discord darkmode">

	<!-- CHANNEL HEADER -->
	<div class="channel">
		<p><span class="hash">#</span><span class="channelname">test-channel-name</span></p>
	</div>

	<!-- THE ACTUAL MESSAGES (each new user's messages in a separate block) -->

mac641

Install Alpine Linux on WSL and connect it to Docker Desktop

• Open an elevated PowerShell prompt.

Set up WSL and install AlpineWSL

• Enable Windows Subsystem for Linux feature.

dism.exe /online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux /all /norestart
dism.exe /online /enable-feature /featurename:VirtualMachinePlatform /all /norestart