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This file is maintained by the AIs. The CAPTAINS-LOGS.md file is completely human generated.
A modern full-stack social media application built with Go and React.
- ✅ Complete Authentication System: Login/signup with session management
- ✅ Responsive Design: Mobile-first UI that scales beautifully to desktop
- ✅ Modern UI/UX: Clean design with smooth animations and transitions
- ✅ Type Safety: Full TypeScript integration with strict typing
- ✅ Production Ready: Docker containerization with optimized builds
- 🚧 Social Features: Coming soon - posts, following, feeds
- Backend: Go 1.24+ with Gorilla Mux router
- Frontend: React 19+ with TypeScript, Vite, and Tailwind CSS
- Database: YugabyteDB (PostgreSQL-compatible) with GORM ORM
- Authentication: Session-based auth with HTTP-only cookies
- Deployment: Docker containers with embedded static assets
For detailed development instructions, see .github/copilot-instructions.md.
This project is a full-stack social media application using Go for the backend and React with TypeScript for the frontend. It utilizes YugabyteDB (PostgreSQL-compatible) and GORM for database interactions. Key conventions include environment variables for configuration, consistent error handling, and a security-first approach. The project structure is well-defined with backend/, frontend/, and models/ directories. Development follows modern practices with Docker for containerization and Makefile for common tasks.
Commands you need are in the Makefile.
Whenever the yugabyte service is running, it can be accessed with this command.
This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).
This project was developed through human-AI collaboration:
- Primary Developer: @samintheshell
- AI Assistant: Claude (Anthropic) - Architecture design, code generation, and development guidance
- AI Assistant: Gemini 2.5 Pro (Preview) via GitHub Copilot - Code generation, bug fixing, and project understanding
- AI Assistant: GitHub Copilot (OpenAI) - Code generation, code review, and developer productivity enhancement
The AI contributions include system architecture, coding patterns, documentation structure, and implementation of core features. All code is open source under AGPL-3.0.
