Problem
Existing movie rating apps treat every user's 4-star rating the same. They don't account for individual taste patterns. Someone who consistently rates slow-burn dramas highly has fundamentally different preferences than someone who rates action blockbusters highly, even when both give identical scores. The result is generic recommendations that don't reflect who you actually are as a viewer.
Solution
A native iOS app built entirely through AI-directed development. It tracks your movie ratings, identifies patterns in your taste across genres and performers, and builds a personalized profile over time. That profile drives AI-generated performer rankings and 30-day trending analysis, so recommendations reflect your actual preferences, not global averages.
Screenshots
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- Live on Apple App Store
- Built end-to-end through AI-directed development
- Multi-dimensional taste profiling
- 30-day trending analysis engine
Built entirely through AI-directed development (vibe coding) using Swift and SwiftUI. The app analyzes rating patterns to build multi-dimensional taste profiles, then uses these profiles to generate personalized performer rankings and trend analysis. Core Data handles local storage with CloudKit sync for cross-device continuity. The 30-day trending feature identifies emerging patterns in the user's viewing and rating behavior. The entire app, from architecture to App Store submission, was built through iterative AI-directed development, demonstrating the full production viability of that workflow.
Tech Stack
- Swift
- SwiftUI
- Core Data
- CloudKit
- AI/ML Kit