Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
Welcome to the comprehensive, community-driven resource repository for GATE Computer Science & Information Technology (CS & IT) 2027 aspirants. Securing a top rank in the Graduate Aptitude Test in Engineering (GATE) unlocks direct admissions to premier postgraduate programs (M.Tech/Ph.D.) at IITs, NITs, and IISc, alongside prestigious technical roles within leading PSUs.
This repository serves as a centralized documentation hub compiled from authoritative open-source archives, topper strategies, and verified learning pathways.
In this tutorial I'll show you how to create a Github webhook that will post updates from your Github account to a channel in Discord. The steps are simple so follow along!
First you need to create a webhook in a text channel. We're assuming you have both Manage Channel and Manage Webhooks permissions!
Commit format: <emoji_type> <commit_type>(<scope>): <subject>. <issue_reference>
:sparkles: feat(Component): Add a new feature. Closes: #
^--------^ ^--^ ^-------^ ^---------------^ ^------^
| | | | |
| | | | +--> (Optional) Issue reference: if the commit closes or fixes an issue
Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.
Last updated: May 24, 2026.
This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.
Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.
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.
Docker Compose is a tool for defining and running multi-container Docker applications. With a docker-compose.yml file, you can configure your application’s services, networks, and volumes. Below is a comprehensive list of commonly used keys/sections along with a brief explanation and example.
| Key/Section | Description | Example |
|---|
| Replicate Codex Micro Layer 1 on Layer 2 | |
| This guide is written so it can be given directly to an agent. Its goal is to make Layer 2 behave exactly like the protected Codex Layer 1. Afterward, Layer 2 remains editable in Work Louder Input, so individual keys can be replaced normally. | |
| This is an unsupported, version-sensitive technique. Back up the configuration and follow the validation steps before writing anything to the device. | |
| ## Expected result | |
| Layer 2 retains its own name, color, and lighting settings, but gains Layer 1's: |