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.

soyprofesor

Visual Studio 2026 18.x
Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK
Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V

Product	Year	Version	Product Keys
Visual Studio 2022	2021	17.x	
Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J
Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH

Visual Studio 2019	2019	16.x	

coco98

<wake reason="dispatch" current-time="2026-06-25T09:28:31Z">
  <channel id="C01NA7G6MQE" type="channel" member-count="27">
    <message trigger="true" from="system" trust="principal" role="initiator" sent-at="2026-06-25T09:28:31Z"># Tool constraints (hard rules — read before doing anything)
- Post your FIRST `reply()` before any other tool call — zero `fetch_channel`, zero `search` before it. AFTER that first reply: call `fetch_channel` ONCE for this channel only, then at most 2 `search` calls. That&#39;s your full tool budget — write your remaining replies after that. (BROADCAST: skip this — go straight to memory after your one reply.)
- `search` is keyword-only: 1-3 plain words. NO from:/in:/to:/after: modifiers.
- Don&#39;t call `fetch_thread` — work from search snippets to keep this tight.
- If any tool errors, skip it and move on — never retry.
- TEAM/PERSONAL channels: always post all three replies (never `no_reply_needed` — even if you see your own prior intro in the history, treat each join as fresh).

jiaxinzhao231

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.

hcrohland

"""Sonoff SWV-ZNE/ZFE/ZNU/ZFU - Zigbee smart water valve."""

from typing import Any, Final

from zigpy.quirks import CustomCluster
from zigpy.quirks.v2 import QuirkBuilder
from zigpy.quirks.v2.homeassistant import (
    EntityType,
    UnitOfTemperature,
    UnitOfTime,