| Microsoft office professional Plus 2016 product key | |
| Mysoftwarekeys.com | Mysoftwarekeys.com | Mysoftwarekeys.com | |
| NBCRJ-YJ94Q-T73WV-9PR4F-9W3VC | |
| KNJPJ-YBFTR-42K6M-Y6FMX-BKM3P | |
| QQ34F-N3THK-CWTFJ-HD66X-8QK7C |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
Windows 2012 R2 Essentials: http://download.microsoft.com/download/8/F/7/8F7024D2-AB2A-4BE2-8406-1E3AC49C5C1F/9600.16384.WINBLUE_RTM.130821-1623_X64FRE_SERVER_SOLUTION_EN-US-IRM_SSSO_X64FRE_EN-US_DV5.ISO
Windows 2016: https://software-download.microsoft.com/download/pr/Windows_Server_2016_Datacenter_EVAL_en-us_14393_refresh.ISO
Windows 2019 Essentials: https://software-download.microsoft.com/download/pr/17763.737.190906-2324.rs5_release_svc_refresh_SERVERESSENTIALS_OEM_x64FRE_en-us_1.iso
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.
| from flask import Flask | |
| app = Flask(__name__) | |
| @app.route("/") | |
| def home(): | |
| return "AI Grocery Server is running!" | |
| <!DOCTYPE html><html data-stencil-build="xa2n4ld3" class="hydrated"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><style sty-id="sc-discord-messages">@import url('https://fonts.bunny.net/css?family=roboto:400,500,700');@font-face{font-family:'Whitney';src:url('https://cdn.jsdelivr.net/gh/ItzDerock/discord-components@master/assets/fonts/Book.woff') format('woff');font-weight:400;font-display:swap}@font-face{font-family:'Whitney';src:url('https://cdn.jsdelivr.net/gh/ItzDerock/discord-components@master/assets/fonts/Medium.woff') format('woff');font-weight:500;font-display:swap}@font-face{font-family:'Whitney';src:url('https://cdn.jsdelivr.net/gh/ItzDerock/discord-components@master/assets/fonts/Semibold.woff') format('woff');font-weight:600;font-display:swap}@font-face{font-family:'Whitney';src:url('https://cdn.jsdelivr.net/gh/ItzDerock/discord-components@master/assets/fonts/Bold.woff') format('woff');font-weight:700;font-display:swap}.discord-messages{color:#fff; |
| id | station | stationid | value | unit | obstime | date | |
|---|---|---|---|---|---|---|---|
| 0 | 流浮山 | RF001 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 1 | 湿地公园 | RF002 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 2 | 水边围 | N12 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 3 | 石岗 | RF003 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 4 | 大美督 | RF004 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 5 | 大埔墟 | RF005 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 6 | 北潭涌 | RF006 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 7 | 滘西洲 | RF007 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 | |
| 8 | 西贡 | N15 | 0 | mm | 2026-08-24T10:00:00+08:00 | 2026-08-24 |
This document lists comment styles and languages (and dialects) that use it. The list is based on the one used in NERD commenter.
Comment types are grouped by the types of symbols used. Each example is organised as a comment block followed by a list of languages.
Note1: the number of languages is quite large, and much of this