| package but.info.s101.membre2 | |
| import org.junit.jupiter.api.Assertions.* | |
| import org.junit.jupiter.api.Test | |
| class CryptoAtbashTest { | |
| /* | |
| Test decalerLettre(c:Char, decalage:Int) | |
| */ |
| #!name=Talkatone | |
| #!desc=Talkatone 日常使用模块 | |
| # 本模块主要为 Talkatone 进行策略分流，解决使用某些节点时无法直接使用 Talkatone 的问题，并附带 Talkatone 去广告规则，可去除 APP 内的部分第三方广告 | |
| # 本模块仅适用于 Talkatone 登陆后使用，不适用于软件注册，若尝试登录时失败可以将 tktn.at 的策略改为 DIRECT 后再试，成功后改回本来策略或重新更新本模块 | |
| # 以下为该软件部分去广告规则 | |
| DOMAIN-SUFFIX,aax.amazon-adsystem.com,REJECT,DROP | |
| DOMAIN-SUFFIX,aax-fe.amazon-adsystem.com,REJECT,DROP | |
| DOMAIN-SUFFIX,aax-fe-sin.amazon-adsystem.com,REJECT,DROP | |
| DOMAIN-SUFFIX,ads.inmobi.com,REJECT,DROP |
A complete guide to running a Windows 11 VM with GPU passthrough and Looking Glass on an Arch-based Linux system with a Wayland compositor. This setup allows you to run Windows-only software like Fusion 360 in a native-feeling window on your Linux desktop, with near-zero latency and no compression artifacts.
This guide was written from a real setup session, including every issue encountered and how it was resolved. If you're running into problems, there's a good chance the answer is in here.
webpack's core repository describes hundreds of APIs, hooks, and plugin interfaces in types.d.ts, but almost none of that reaches readers in a structured or searchable form. The existing documentation site itself had drifted behind the project it documents, both in design and in how quickly it reflects changes.
webpack-doc-kit is the toolchain built to close that gap, and rebuilding it was the subject of webpack's GSoC 2026 project. The pipeline runs end to end: TypeDoc extracts structured data from webpack's type definitions, a custom typedoc-plugin-markdown theme reshapes it into doc-kit-compatible Markdown, and @node-core/doc-kit - the generator behind Node.js's official API documentation - renders the finished site.
The project spanned five milestones and three mentees. My work co
| // ==UserScript== | |
| // @name Instagram Keyboard Shortcuts for Power User | |
| // @namespace http://tampermonkey.net/ | |
| // @version 2.4.13 | |
| // @description Scroll through posts with J/K, like with L, save with O, play/stop with P, fullscreen with F. Consistent with Gmail, Twitter, Tumblr, etc. | |
| // @author Eduardo Clemens (adapted from French Bond) | |
| // @match https://www.instagram.com/* | |
| // @icon https://www.instagram.com/static/images/ico/favicon.ico/36b3ee2d91ed.ico | |
| // @grant none | |
| // @require https://code.jquery.com/jquery-3.7.1.min.js |
| import os | |
| import subprocess | |
| import time | |
| import pyautogui | |
| # Directorul cu fisierele PDF | |
| pdf_dir = r'g:\1' | |
| # Calea catre executabilul ABBYY FineReader | |
| abbyy_path = r'"g:\Kituri\ABBYY FineReader PDF Corporate 16.0.14\FineReader OCR Editor.exe"' |
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.
Who this is for: an AI agent (or engineer) helping someone build an illustrated, animated floorplan dashboard for Home Assistant from scratch, with their own house, their own illustrations, their own sensors.
How to read it: it is written as questions and answers. Find the question the user is really asking and answer from that section. The numbers, room names and entity IDs here are placeholders — every one of them will be different in a new build. What transfers is the method: the canvas rule, the file naming scheme,
| #!/bin/bash | |
| ############################################################################### | |
| # Author: Abhishek Veeramalla | |
| # Version: v0.0.1 | |
| # Script to automate the process of listing all the resources in an AWS account | |
| # | |
| # Below are the services that are supported by this script: | |
| # 1. EC2 |
