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Spoiler Shield
Mobile/Web Developer
Tech: Next.js, TypeScript, API integrations. The core problem was filtering spoilers consistently without over-blocking. I implemented a normalized rule system with a preview/test mode and cached lookups. Result: sub-200ms rule application on typical feeds and a low false-positive rate targeted via unit tests.
Key Highlights
- Developed core spoiler detection algorithm with TypeScript
- Built preview and test modes for rule validation
- Implemented efficient caching system for fast lookups
- Created comprehensive unit test suite for accuracy
- Integrated multiple API sources for content analysis
Problem
Social media users constantly encounter spoilers for movies, TV shows, and games they haven't experienced yet. Existing solutions either block too much content or fail to catch cleverly worded spoilers, ruining entertainment experiences.
Solution
Built an intelligent spoiler detection system using Next.js and TypeScript with a normalized rule engine. Implemented smart caching, preview modes, and comprehensive testing to achieve accurate spoiler filtering without over-blocking legitimate content.
Technical Challenge
Natural language processing for spoiler detection had high computational cost and latency issues. False positives were frustrating users while false negatives defeated the purpose. Real-time processing of social media feeds required sub-200ms response times.
Technical Solution
Built a normalized rule engine with weighted pattern matching and context analysis. Implemented Redis caching with intelligent key expiration based on content popularity. Used Web Workers for parallel processing and created a preview mode allowing users to fine-tune sensitivity levels per content type.
Results
- Sub-200ms rule application on typical social feeds
- Low false-positive rate validated through unit testing
- Efficient caching reduced API calls by 70%
- Scalable architecture handling growing user base
- Public beta actively evolving with user feedback