Sunny Side

Through user research, we discovered something interesting: people don't check weather apps just to know the temperature. They're making decisions: • "Should I bring an umbrella?" • "Can I hang my laundry outside?" • "Do I need sunscreen today?" • "What should I wear?" Existing weather apps gave you data, but you had to interpret it yourself. We wanted to build something smarter - an app that gave you actionable recommendations, not just numbers. The challenge was: how do you turn weather data into practical advice without being annoying or obvious?

I built a recommendation engine that analyzed weather data and provided contextual advice: Temperature + UV Index → "Apply sunscreen, UV is high today" Precipitation probability → "60% chance of rain, bring an umbrella" Humidity + Temperature → "Good day for outdoor laundry" Temperature range → "Layer up, temperature drops 10°C this evening" The technical implementation: • WeatherKit for real-time forecasts • Custom algorithm to analyze multiple weather factors • Push notifications for important changes • Location pinning for daily routine checks For the architecture, I applied SOLID principles: • Single Responsibility: Each view model handles one concern • Dependency Injection: Easy to test and swap components • Interface Segregation: Clean protocol definitions

This was my first time using WeatherKit, Apple's official weather framework. The learning curve was interesting: Challenges: • WeatherKit requires API keys and proper entitlements • Rate limiting (500 calls per day on free tier) • Handling offline scenarios gracefully • Parsing complex weather data structures Solutions: • Implemented smart caching to reduce API calls • Built offline mode with last-known data • Created clear error handling with user-friendly messages • Used Combine for reactive data updates The MVVM pattern helped a lot here. All weather logic lived in the ViewModel, making it easy to test without needing actual API calls. I could mock the weather data and verify the recommendation logic worked correctly.

After launch, the feedback validated our approach. Users loved the recommendations: "Finally, a weather app that tells me what I actually need to know!" "The laundry timing feature saved my clothes from getting soaked!" "I use the pinned locations for my morning routine - so convenient" The location pinning feature became surprisingly popular. Users would pin: • Home (morning routine check) • Office (commute planning) • Kids' school (pick-up timing) • Weekend spots (outdoor activity planning) This project taught me that sometimes the best innovations aren't about new technology - they're about understanding what users actually need and delivering it in a simple, intuitive way.