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Surprise Me Feature - Documentation Index

Welcome to the Surprise Me feature documentation! This page helps you navigate all the documentation for this feature.

🎯 What You're Looking For

I want to understand the feature quickly

👉 Quick Summary - 5-minute read with key concepts and benefits

I want to implement the feature

👉 Quick Start Guide - Step-by-step implementation (2-3 hours)

I need the complete design specification

👉 Complete Design Document - Full technical specification

I want to see code examples

👉 Implementation Examples - Ready-to-use code snippets

I need visual diagrams

👉 Architecture Diagrams - System architecture and flows

📚 All Documentation

Document Purpose Audience Time to Read
Quick Summary Feature overview Everyone 5 min
Quick Start Guide Implementation steps Developers 10 min
Complete Design Full specification Developers, Architects 30 min
Architecture Diagrams Visual system design Developers, Architects 15 min
Implementation Examples Code snippets Developers 20 min

🎓 Learning Path

For Product Owners / Managers

  1. Read Quick Summary
  2. Review "Key Concept" and "Benefits" sections
  3. Check "Success Metrics" section
  4. Done! ✅

For Developers (New to Project)

  1. Read Quick Summary
  2. Study Architecture Diagrams
  3. Read Complete Design
  4. Follow Quick Start Guide
  5. Reference Implementation Examples as needed

For Developers (Ready to Code)

  1. Skim Quick Summary
  2. Jump to Quick Start Guide
  3. Use Implementation Examples for reference
  4. Refer to Complete Design for details

For System Architects

  1. Read Complete Design
  2. Study Architecture Diagrams
  3. Review database schema and API design
  4. Evaluate scalability and performance considerations

For QA Engineers

  1. Read Quick Summary
  2. Review "User Experience Flow" in Complete Design
  3. Use "Testing Checklist" in Quick Start Guide
  4. Reference Implementation Examples for test cases

🔍 Quick Reference

Key Concepts

  • 7 Recommendation Algorithms: Smart Mix, Weighted Random, Forgotten Gems, Similar to Favorites, Time-Based, Pure Random, Unwatched First
  • Lightweight Design: No ML, just smart SQL queries
  • Personal Focus: Single-user video rediscovery
  • Sidebar Navigation: Standalone page, not a tab

Technical Stack

  • Frontend: Next.js, React, TailwindCSS
  • Backend: Next.js API Routes
  • Database: SQLite with new media_access table
  • Service Layer: RecommendationService class

Main Components

  • /app/surprise-me/page.tsx - Main page
  • /app/api/videos/recommendations/route.ts - API endpoint
  • /app/api/media-access/[id]/route.ts - Access tracking
  • /lib/recommendation-service.ts - Algorithm logic

Database Schema

CREATE TABLE media_access (
  id INTEGER PRIMARY KEY,
  media_id INTEGER,
  access_type TEXT CHECK (access_type IN ('view', 'play', 'bookmark', 'rate')),
  created_at DATETIME
);

📖 Document Details

Quick Summary

  • Lines: ~220
  • Topics: Overview, algorithms, benefits, scenarios
  • Best for: Understanding the "why" and "what"

Quick Start Guide

  • Lines: ~570
  • Topics: Step-by-step implementation, testing, troubleshooting
  • Best for: Actually building the feature

Complete Design

  • Lines: ~580
  • Topics: Architecture, algorithms, database, API, UI, performance
  • Best for: Deep technical understanding

Architecture Diagrams

  • Lines: ~390
  • Topics: Mermaid diagrams for system, data flow, algorithms
  • Best for: Visual learners and system design

Implementation Examples

  • Lines: ~910
  • Topics: Complete code examples, tests, utilities
  • Best for: Copy-paste ready code

🎯 Implementation Phases

Phase 1: MVP (Week 1)

Phase 2: Enhanced (Week 2)

Phase 3: Optimized (Week 3)

🤔 Frequently Asked Questions

Where do I start?

Answer: If you're implementing, go to Quick Start Guide. If you're learning, start with Quick Summary.

How long does implementation take?

Answer: Basic MVP takes 2-3 hours. Full feature with all algorithms takes 1-2 weeks.

Do I need machine learning?

Answer: No! The design specifically avoids ML for simplicity and lightweight operation.

Will this work with 100,000 videos?

Answer: Yes, with proper database indexes (included in design). See performance section in Complete Design.

Can I customize the algorithms?

Answer: Absolutely! See Implementation Examples for how to add new algorithms.

Is this production-ready?

Answer: The design is production-ready. Implementation quality depends on following the guides.

🔗 Related Features

This feature integrates with:

  • Video Player: Uses existing UnifiedVideoPlayer component
  • Bookmarks: Leverages bookmark data for Forgotten Gems algorithm
  • Star Ratings: Uses rating data for recommendations
  • Media Database: Extends existing media table with access tracking

📝 Contributing to Documentation

Found an issue or want to improve the docs?

  1. Check which document needs updating using this index
  2. Follow the style of existing documentation
  3. Update the relevant document
  4. Update this index if you add new content
  5. Update the main docs README

🆘 Need Help?

Can't find something?

  • Use browser's find function (Ctrl/Cmd + F) to search this page
  • Check the main docs README
  • Search within individual documents

Found a bug in documentation?

  • Create an issue describing the problem
  • Reference the specific document and section
  • Suggest a fix if possible

Want to suggest improvements?

  • Share your ideas in project discussions
  • Submit a pull request with improvements
  • Update this index to reflect changes

📊 Version Information

Document Version Last Updated Status
Quick Summary 1.0 2025-10-12 ✅ Complete
Quick Start Guide 1.0 2025-10-12 ✅ Complete
Complete Design 1.0 2025-10-12 ✅ Complete
Architecture Diagrams 1.0 2025-10-12 ✅ Complete
Implementation Examples 1.0 2025-10-12 ✅ Complete
This Index 1.0 2025-10-12 ✅ Complete

Next Steps:

  1. Choose your learning path above
  2. Start reading the recommended documents
  3. Begin implementation using the Quick Start Guide

Happy Coding! 🎉