GitHub Copilot

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GitHub Copilot

GitHub Copilot is an AI-powered development assistant built by GitHub in partnership with OpenAI and Microsoft. Originally launched in technical preview in June 2021 and reaching general availability in June 2022, Copilot has expanded from single-line code completions into a full agentic platform that can autonomously edit multiple files, run terminal commands, and open pull requests with minimal human direction. It is now embedded across GitHub.com, major IDEs, the CLI, GitHub Mobile, and Windows Terminal.

General
GA date	21 Jun 2022
Developer	GitHub (Microsoft)
Type	AI Coding Assistant
License	Commercial SaaS
Documentation	docs.github.com/en/copilot

Core Features

Inline code completions — context-aware autocomplete in supported editors; includes next-edit predictions in VS Code, Xcode, and Eclipse.
Copilot Chat — conversational interface for code explanation, refactoring, debugging, and Q&A; available in IDEs, GitHub.com, GitHub Mobile, and Windows Terminal.
Agent Mode (IDEs) — autonomous in-IDE operation that edits multiple files, runs terminal commands, self-corrects errors, and iterates until a task is complete.
Cloud Coding Agent — an async agent assigned via a GitHub issue that researches the repo, writes an implementation plan, and opens a pull request.
Copilot Code Review — AI-generated pull request review suggestions surfaced inline on the PR diff.
PR Summaries — auto-generated descriptions of pull request changes for reviewers.
Copilot CLI — natural-language terminal assistance (GA April 2026).
MCP Server Integration — any Model Context Protocol server works as a Copilot extension, replacing the deprecated Copilot Extensions API.
Multi-model selection — choose from OpenAI models, Anthropic Claude (including Opus), Google Gemini, and others on Pro+ and above plans.
Copilot Spaces — centralizes repository context (code, docs, specs) to improve response quality.

Supported Editors and Surfaces

Surface	Notes
Visual Studio Code	Full feature support including Agent Mode
JetBrains IDEs	IntelliJ, PyCharm, WebStorm, GoLand, and others
Visual Studio	Windows-native IDE support
Xcode	Includes next-edit predictions
Eclipse	Agent Mode GA July 2025
Neovim	Plugin-based integration
Zed	Native integration
GitHub.com	Chat, code review, PR summaries, cloud agent
GitHub Mobile	Chat on iOS and Android
GitHub CLI / terminal	Copilot CLI for natural-language shell commands
Windows Terminal Canary	Chat integration

Pricing Tiers

Plan	Price	Key Inclusions
Free	$0/month	2,000 completions/month, auto model selection, Copilot CLI
Pro	$10/user/month	Unlimited completions, multiple model access, cloud agent, $10 AI credits/month
Pro+	$39/user/month	Premium models (Claude Opus, etc.), higher AI credit allowance
Max	$100/user/month	Highest individual AI credit allowance, priority access to new models
Business	$19/seat/month	Team management, policy controls, monthly AI credit pool
Enterprise	$39/seat/month	All Business features, larger credit pool, priority model access, GitHub Enterprise Cloud required

Free plans also apply to verified students, educators, and qualifying open-source maintainers (Pro-level features).

Tools and Resources

Documentation — docs.github.com/en/copilot
Plans and pricing — github.com/features/copilot/plans
REST API for Copilot management — docs.github.com/en/rest/copilot — manage seats, billing, and usage at org/enterprise level.
MCP server catalog — any MCP-compatible server can extend Copilot's context and tools.

Ecosystem and Integrations

Integrated natively into GitHub.com, enabling AI assistance directly in pull requests, issues, and discussions without leaving the browser.
Works with GitHub Actions for AI-assisted CI pipeline troubleshooting and workflow generation.
Enterprise deployments support Bring Your Own Keys (BYOK), allowing organizations to route Copilot through their own LLM provider API keys.
Available as a standalone subscription or bundled with GitHub Enterprise Cloud.

Get started at github.com/features/copilot or explore the full reference at docs.github.com/en/copilot.

Edit on GitHub

Github Github Copilot AI technology Hackathon projects

Discover innovative solutions crafted with Github Github Copilot AI technology, developed by our community members during our engaging hackathons.

AI Classroom Edge Intelligence

AI Classroom Edge Intelligence is a privacy-first classroom AI platform built for schools that need useful AI without sending every piece of student information to the cloud. The platform evaluates each task by privacy level, connectivity, and complexity, then routes it to Offline Edge Mode, a Local Classroom Server, or Fireworks AI Cloud Assist. Sensitive or restricted information stays local. Eligible anonymized, high-complexity tasks are sent through a secure Express backend to Fireworks Serverless using Qwen3.7 Plus. The project includes an AMD Model Router, Edge Runtime Monitor, Rural Connectivity Simulator, Privacy and Local Data Ownership Console, Classroom Digital Twin, and teacher approval workflow. Live results display the provider, model, route, privacy classification, latency, safety note, and AI response. API keys remain server-side, and the privacy guard blocks sensitive requests from cloud inference. The project was inspired by rural schools where connectivity can be unreliable and student privacy is critical. Instead of acting as a simple chatbot, it serves as an intelligent routing and decision-support system. Teachers review, edit, approve, or reject recommendations before instructional actions are recorded. The current implementation includes a working browser interface, real backend routing, live Fireworks Serverless integration, server-side key protection, and Docker containerization. Local AMD AI PC inference, GPU/NPU acceleration, device telemetry, and production synchronization are clearly identified as future work. The long-term vision is a school-owned AI platform combining local intelligence, optional cloud reasoning, persistent classroom evidence, and teacher oversight for rural and underserved communities.

Verity — Fact-Checking for the Rest of Us

Most people believe first and verify never , there simply isn't time to check every headline, post, or forwarded message before deciding whether to trust it. That gap is where misinformation lives and spreads. Verity closes it. You give Verity a claim — a headline, a statistic, a line from an article — and it separates what's actually checkable from what's just opinion, then searches for independent evidence across 40+ public authoritative sources. Instead of trusting a single model's best guess, Verity requires independent checks to agree before returning a verdict: true, false, or somewhere in between, with a source trail you can click through and verify yourself. Every verification runs live on AMD Instinct hardware via the Fireworks AI API, using Gemma and other open-source models routed through a 5-pass consensus architecture — nothing pre-computed, nothing faked. The models are swappable and inspectable by design, because a trust tool that asks you to trust it blindly defeats its own purpose. Verity is built for the moment right before someone shares something they haven't checked , turning fact-checking from a newsroom luxury into something anyone can do in seconds.

Contxt

Every AI grew a memory in 2026 — ChatGPT, Claude, Gemini, Copilot, Grok — but they're five walled gardens that never talk to each other, so you re-introduce yourself every time you switch. And every memory product (Mem0, Supermemory, Letta) is cloud-first and built for developers, not for you. Contxt is the consumer piece nobody built: one portable, privacy-first context layer that works across every AI, where your most sensitive data never leaves your device. A browser extension connects your Gmail, Calendar and Notion, then classifies every item on-device through a Crown-Jewels Gateway — Gemma 3 270M running in WebGPU plus a deterministic-rules safety floor. PRIVATE "crown jewels" stay local and end-to-end encrypted (the cloud is a blind relay of ciphertext, with keys transferred device-to-device by QR). SHARED context is distilled into reusable cards that any AI can read over MCP, and injected live into the composer on Claude, ChatGPT and Gemini. A companion web dashboard mirrors the extension in real time. One context layer. Every AI. Your data stays yours.

SecondChance AI

SecondChance AI is an AI-powered relationship recovery and meaningful connection platform designed for people rebuilding their lives after difficult relationships, heartbreak, divorce, or emotional setbacks. Instead of focusing only on dating, our platform prioritizes emotional healing, confidence building, safety, and meaningful human connections through artificial intelligence. The platform combines intelligent profile analysis, AI-assisted compatibility insights, personalized relationship coaching, guided onboarding, secure matching, and a clean user experience to help users confidently begin a new chapter in life. Every recommendation is designed to encourage healthy communication, emotional well-being, and long-term compatibility rather than superficial interactions. Our AI Coach acts as a supportive companion that provides personalized guidance, emotional encouragement, reflective conversations, and practical relationship advice. The matching system analyzes interests, goals, personality traits, and compatibility signals to recommend meaningful connections. Additional features such as profile management, secure messaging, privacy-focused settings, and guided onboarding create a complete recovery-to-relationship journey. SecondChance AI is built with scalability and privacy in mind using modern technologies including React Native, Expo, Firebase, TypeScript, and AI-driven recommendation logic. Alongside the mobile application, we developed a premium landing website showcasing the vision, product experience, roadmap, and future plans for the platform. Our long-term vision is to evolve SecondChance AI into a trusted global relationship wellness ecosystem where artificial intelligence not only helps people discover compatible partners but also supports emotional healing, relationship education, communication improvement, and healthier human connections. We believe everyone deserves a second chance to heal, grow, and build meaningful relationships with confidence.

ReCodeX

ReCodeX is an AI-powered enterprise platform developed for the AMD Developer Hackathon: ACT II (Track 3 - Unicorn Track) designed to tackle the multi-billion dollar challenge of technical debt and legacy migration. Instead of a basic chatbot conversational layout, ReCodeX provides a complete engineering dashboard that automates the analysis, documentation, translation, and verification of aging infrastructure like legacy Java, COBOL, or unoptimized C++. The platform delivers comprehensive developer features, including: - Side-by-Side Modernization: Allows users to upload a legacy source file and view the original code side-by-side with refactored, modern, and fully test-covered code. - Automated Documentation: Provides deep semantic understanding of complex code logic to break down precisely what legacy components do. - Risk & Security Audits: Automatically generates a comprehensive Risk Report highlighting structural changes, security optimizations, and cloud-readiness metrics. The AMD Advantage & Tech Architecture: - To process heavy enterprise code demands reliably, ReCodeX relies entirely on high-performance cloud infrastructure backed by AMD hardware. - Core LLM orchestration is routed via the Fireworks AI API, utilizing high-performance AMD hardware endpoints running Google DeepMind's open-source Gemma models. - The compute layer is hosted on the AMD Developer Cloud, leveraging powerful cloud-based AMD GPUs to manage parallelized analytics, code scanning, and workflow pipeline tasks at sub-second latencies. - The entire system is packaged inside a Docker container for standardized deployment, with the frontend application built using Streamlit and React. Ultimately, ReCodeX targets tangible enterprise metrics for the Unicorn Track by reducing ongoing maintenance overhead, ensuring regulatory auditability through precise change logging, and accelerating cloud migration frictionlessly.

Curio

Paste this at the end of the text you already have: Development Philosophy During the development of Curio, our primary focus was on "Time-to-Value." We understood that in a fast-paced hackathon environment, the ability to iterate quickly and show tangible results is paramount. By leveraging the latest Google GenAI SDK, we were able to abstract away complex model interactions and focus on creating a user experience that feels premium and responsive. The decision to integrate marked.js was born out of a necessity to present AI-generated content in a human-readable format, bridging the gap between raw data and actionable information. Our development process involved rigorous testing of the frontend-backend communication, ensuring that even under high load or potential API timeout scenarios, the application remains stable and visually consistent. Future Roadmap While Curio is already a functional intelligence platform, we envision several paths for future development to enhance its impact: Multi-Modal Integration: Expanding beyond text to provide video summaries and audio-briefing capabilities for users on the go. Granular Preference Learning: Implementing an automated feedback loop where the AI learns from the articles a user clicks on, refining its filtering logic over time to become increasingly accurate. Enterprise Customization: Creating a B2B version of Curio that allows companies to ingest internal company news and market reports, providing employees with a personalized feed of internal announcements alongside industry trends. Global Language Support: Expanding the linguistic capabilities of the Gemini model integration to serve non-English speaking markets, ensuring that personalized news is accessible regardless of the user's primary language.