About
Reel Favorites

What is Reel Favorites? Reel Favorites is a personalized movie discovery and recommendation platform designed to help enthusiasts find their next favorite movies and shows.

The application combines movie data from TMDB, video content from YouTube, and recommendation workflows powered by OpenAI to create a watchlist experience that feels more personal than generic popularity-based suggestions.

Vintage cinema projector casting a bright beam of light

Our Mission

Reel Favorites is a project I designed and built to explore how AI can improve personalized movie discovery.

The project exists to create meaningful connections between viewers and movies and shows that might otherwise remain undiscovered in the vast ocean of content.

For Movie and TV Lovers

Reel Favorites helps you:

  • Discover movies and shows tailored to your unique taste through our recommendation engine
  • Keep a personal watchlist of movies and shows you want to watch, and add them to your favorites for easy access
  • Explore new generes, movie details, trailers, and related content with more context
  • Save time by focusing on movies and shows you're likely to enjoy rather than endless scrolling
  • Save movies and shows to your favorites to power your personalized recommendations

How It Works

For Users

Create your profile

Users create an account and begin building their taste profile by saving favorite movies and interacting with content across the app.

Add your favorites to your watchlist

The more movies and shows you add to your watchlist, the better our recommendation engine understands your taste.

Receive personalized recommendations

Our system analyzes your favorites and uses movie and show metadata and AI to generate personalized recommendations.

Track and organize

Users can maintain a personal watchlist, manage favorites, and revisit recommendations over time as their preferences evolve..

Discover and explore

The app supports exploration through detailed movie information, trailers, and recommendations to make discovery feel more engaging and less random.

Under the Hood

Reel Favorites was built as a full-stack application using modern product architecture, AI integration, and user-centered software design. The project demonstrates how external APIs, persistent user data, and LLM workflows can be combined into a polished, real-world application.

Technology Stack

Frontend Framework

Next.js with React, leveraging both client-side and server-side rendering for optimal performance and SEO

UI Design

Tailwind CSS for a responsive, customized interface with a dark-themed, modern aesthetic

External API

Integration with The Movie Database (TMDB) API for comprehensive film data

State Management

React Context API for efficient state management across components

Database

Firebase Firestore for user data persistence, favorites storage, and recommendation tracking

Authentication

Firebase Authentication for secure user account management

Media Handling

Custom media player components for trailer and featurette playback

User Experience

Responsive design with intuitive navigation between discover, detail, and library views

Recommendation Engine

The Reel Favorites recommendation system leverages user interaction data and content metadata:

  • •User Favorites Analysis: Recommendations are generated based on films users have added to their favorites
  • •Real-time Data Flow: React context providers maintain synchronized state between user interactions and recommendations
  • •Content-based Analysis: The system matches users with films based on attributes like genre, themes, and tone
  • •Firebase Integration: User preferences and recommended content are stored in Firestore for persistence across sessions
  • •Regeneration Capability: Users can manually refresh recommendations as they add more favorites
  • •Responsive Feedback: The interface provides clear loading states and feedback during recommendation generation

Key Features Implementation

  • •Movie Discovery: Browse current and popular titles through a clean discovery experience powered by TMDB data
  • •Search Functionality: Search across a wide movie and tv catalog to quickly find titles and build a stronger taste profile
  • •Detailed Movie & Show Pages: Rich media and information display including trailers, featurettes, release information, and interactive elements
  • •Personal Watchlist: Save favorites and manage a persistent watchlist tied to each user account
  • •Personalized Recommendations: Generate recommendations shaped by user-selected favorites and AI powered recommendation engine
  • •Autocomplete Search: Real-time search suggestions powered by TMDB API to help users find titles quickly and easily
  • •Authentication Flow: Secure, account-based personalization for saved content and recommendations

The Team

Reel Favorites was created by Darryl Mackas a project focused on software architecture, AI-powered product design, and modern web application development.

The project is not affiliated with TMDB, YouTube, or OpenAI.

Join Us on the Journey

Reel Favorites is also a case study in the kind of work I do for clients: designing and building software that connects product strategy, external APIs, data, and AI into useful customer experiences.

If you are exploring a new web application, internal platform, or AI-powered product, this project reflects how I approach architecture and implementation.

If you’re looking for help with software architecture, AI integration, or product development, let’s talk.

Work with Me