Discover gists
管理员身份运行cmd,依次输入下面命令
slmgr.vbs -upk
slmgr.vbs -ipk W269N-WFGWX-YVC9B-4J6C9-T83GX #注意!!不同系统版本这里的激活码不一样,请往下翻寻找表格!
slmgr.vbs -skms kms.cangshui.net
| { | |
| "collectionChartDataList": [ | |
| { | |
| "collectionName": "collection1", | |
| "collectionSalePrice": [ | |
| { | |
| "price": 58.25, | |
| "cvtDatetime": "2023-03-26T08:00:00" | |
| }, | |
| { |
| .editor-group-watermark > .letterpress{ | |
| background-image: url("https://raw.githubusercontent.com/Aikoyori/ProgrammingVTuberLogos/main/VSCode/VSCode-Thick.png") !important; | |
| opacity: .75; | |
| aspect-ratio: 3/2 !important; | |
| } |
Your ultimate resource for macOS Office installers, serializers, and troubleshooting.
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.
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.
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title></title> | |
| <meta charset="utf-8" /> | |
| <script src="Scripts/jquery-1.9.1.min.js"></script> | |
| <link href="Content/bootstrap.min.css" rel="stylesheet" /> | |
| <script src="Scripts/isRockFx.js"></script> | |
| <script> | |
| $(function () { |
| #include <X11/Xlib.h> | |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| int main() | |
| { | |
| Display *display; | |
| Window window; | |
| XEvent event; | |
| int s; |
