🏆 Feature Request: AI-Powered GitHub Sportscaster Live Stream

[DonnieBLT]

We propose developing an AI-powered GitHub Sportscaster—an animated announcer bot that monitors activity across GitHub. This sportscaster can be configured to watch all of GitHub, a specific organization, repository, tag, or a curated list of repositories. It will provide a dynamic video stream with live play-by-play announcements and a real-time leaderboard, highlighting key actions such as star gains, pull requests, commits, and more. The broadcast will feature project logos, user avatars, and engaging commentary (e.g., “Project htmx pulls ahead of React by 5 stars!”). The stream could run 24/7, and users can tune into curated channels, with additional capabilities to announce hackathons and follow other online event streams.

Feature Details:
• Monitoring & Configuration:
• Scope: Ability to monitor:
• Entire GitHub
• Specific organizations
• Single or multiple repositories
• Specific tags or curated lists of repos
• Event Types: The sportscaster should capture key actions such as:
• Star counts (e.g., “pulls ahead by X stars”)
• New pull requests
• Commits and releases
• Forks and other engagement metrics
• Special events (e.g., hackathon announcements)
• Live Video Representation:
• A dynamic, video-based UI featuring an AI bot (sportscaster) that “announces” actions.
• The video feed displays:
• Project logos and key metrics
• User avatars alongside actions (e.g., contributor details)
• Live commentary and sound effects that enhance the sportscaster experience
• Leaderboard & Analytics:
• A real-time leaderboard showing ranking changes for projects or events based on the monitored metrics.
• Visual transitions and animations to highlight significant changes (e.g., a project overtaking another in stars).
• User Channels & Curation:
• Users can curate channels to follow specific sets of projects or organizations.
• The sportscaster can be extended to include multiple channels for different themes or events.
• Extensibility:
• Designed to adapt to other online event streams beyond GitHub (e.g., hackathons, coding competitions).
• Modular architecture to add new event types or integration sources in the future.
• Technology Stack:
• Backend: Django (or a similar framework) to manage data aggregation, event handling, and configuration.
• Frontend: A video/animation interface (using HTML5, JavaScript, and WebSocket for real-time updates).
• Data Integration: GitHub APIs (and other event sources as needed) for live data streaming.
• AI Integration: Use of an AI model (e.g., o3-mini-high) to generate dynamic commentary and maintain engaging play-by-play language.

AI Agent Coding Instructions:

You are an AI-assisted coder responsible for building the "AI-Powered GitHub Sportscaster" feature. Your tasks are as follows:

1. Repository Integration:

   • Clone and set up the repository.
   • Ensure integration with GitHub APIs to monitor live events (stars, pull requests, commits, etc.) from configurable sources (all GitHub, specific orgs, repos, or tags).

2. Backend Development:

   • Create a Django app (e.g., "sportscaster") to handle event aggregation.
   • Implement a service that polls or subscribes to real-time events and processes them into structured data.
   • Create endpoints for feeding live event data to the frontend via WebSocket.

3. Frontend Development:

   • Develop a dynamic video interface featuring an AI bot (animated sportscaster).
   • Use HTML5, JavaScript, and WebSocket to render live events, project logos, user avatars, and a real-time leaderboard.
   • Integrate AI commentary by calling the latest o3-mini-high model to generate engaging play-by-play language for each event.

4. AI Commentary:

   • Build a module that takes event data (e.g., “Project X gains 5 stars”) and generates lively commentary.
   • Use the o3-mini-high model with a prompt template that encourages sports-announcer style output.

5. Testing & Validation:

   • Write unit tests for backend event processing and API integrations.
   • Develop integration tests ensuring real-time data is correctly displayed on the frontend.
   • Validate that the live stream, leaderboard, and AI commentary are all synchronized.

6. Deployment & Configuration:

   • Ensure that API keys, environment variables, and model parameters are securely managed.
   • Provide a clear setup guide for running the live stream feature locally and in production.

7. Documentation:

   • Document the feature architecture, configuration options, and usage instructions.
   • Provide inline code comments and a README update outlining the sportscaster functionality.

Generate the scaffolding code, including models, views, templates, WebSocket handling, and unit tests, for this AI-powered sportscaster feature.

Acceptance Criteria:
1. Real-Time Monitoring:
• The system successfully monitors the specified scope (all GitHub, org, repos, etc.) for live events.
• Captures and processes key actions such as stars, pull requests, commits, and hackathon announcements.
2. Live Video Interface:
• The frontend displays a video/animated interface with an AI bot sportscaster.
• Live events are announced with dynamic commentary, accompanied by project logos and user avatars.
• A real-time leaderboard updates based on monitored metrics.
3. AI Commentary:
• The sportscaster uses the o3-mini-high model to generate engaging, sports-style commentary.
• Commentary is timely and accurately reflects the underlying GitHub events.
4. User Channels:
• Users can tune into different curated channels to view events relevant to their interests.
5. Testing & Quality:
• All components (backend, frontend, AI commentary module) include unit and integration tests.
• The overall system is production-ready, with detailed documentation and error handling in place.

Additional Considerations:
• Consider scalability for a 24-hour continuous stream and multiple simultaneous channels.
• Implement caching or rate-limiting for GitHub API calls as necessary.
• Ensure the system is modular enough to integrate additional event sources in the future.
• Evaluate and manage the performance impact of real-time AI commentary generation.