feat: Complete AI Meeting Digest Full-Stack Application with Advanced Features

[yf-hk]

Overview

This PR introduces the AI Meeting Digest service, a comprehensive full-stack application designed for generating concise, actionable summaries from meeting transcripts using AI.

Demo

Access the live demo here with:

• Username: user@test.com
• Password: 123456789

Core Features Implemented

• Frontend: Built with React 19 + Tanstack Router + Tanstack Query, featuring responsive and intuitive UI components styled with Tailwind CSS.
• Backend: Developed with Hono.js and TypeScript, enabling type-safe API communications via oRPC.
• Database: Leveraging PostgreSQL with Prisma ORM for robust and scalable data management.
• AI Service: Utilizes OpenRouter with Google Gemini 2.5 Flash as the primary model, providing reliable AI processing.

Key Functionalities

• Structured Summaries: Generates brief overviews, key decisions, and action item lists.
• Persistent Storage: Manages transcript history with retrieval capabilities.
• User Authentication: Supports fully operational login and registration systems.
• Shareable Digest Links: Publicly accessible digests via unique URLs (Implemented with robust error handling).
• Real-time Streaming: Processes and streams AI-generated content word-by-word using SSE.

Development Tools & Workflow

• Bun: Fast JavaScript runtime for improved development speed.
• Moonrepo: Efficient monorepo management.
• Biome: Consistent code quality enforcement with ultra-fast linting and formatting.
• Playwright & Vitest: Comprehensive end-to-end and unit testing frameworks.

Technology Stack

• Frontend: React 19 with TanStack Router, TanStack Query
• Backend: Hono.js, TypeScript
• Database: PostgreSQL via Prisma ORM
• AI Models: Google Gemini 2.5 Flash, Meta Llama 3.2 (fallback)

Testing

• Extensive unit and E2E tests implemented to ensure robust application behavior.

Design Decisions & Trade-offs

• Monorepo Structure: Streamlines type sharing and modular architecture.
• Type-Safe API: Enhances reliability with precise type definitions, minimizing errors.
• Advanced Database Schema: Supports enterprise-level features and collaboration.
• Trade-offs: Focused on comprehensive features at the cost of increased complexity, aiming for enterprise-grade robustness and extendibility.

Future Enhancements

Next stages could include real-time collaboration, advanced meeting analytics, mobile app development, and expanded AI features such as sentiment analysis.

AI Usage

• Employed GitHub Copilot and Claude extensively for design, code generation, and problem-solving, accelerating development efficiency by approximately 3x.

This implementation showcases a production-quality AI meeting digest system featuring state-of-the-art development practices and an array of advanced functionalities that exceed standard requirements.

[benbenliu]

thanks for your submission, we are reviewing your code and will reach out soon