Inspiration

As a kid, I loved collecting sticker albums: the thrill of finding a new one, the satisfaction of completing a page, the joy of sharing them with friends. Years later, I noticed cats had their own special corner of the internet, from memes to entire communities dedicated to them. I wanted to capture that same sense of playful discovery and delight: spotting a cat in the wild, collecting it like a sticker, and making the experience both educational and shareable.

What I Learned

Building Catsnap taught me a lot, both technically and creatively:

  • How to integrate computer vision + AI for breed identification.
  • Designing interfaces that feel delightful and gamified, rather than purely functional.
  • The importance of community and shareability: people love sending their cat stickers to friends.
  • Balancing fun (stickers, collection mechanics) with usefulness (cat breed encyclopedia + care tips).

Challenges

Every project has hurdles. Here were the main ones I faced:

  • Accuracy vs. delight: I didn’t want Catsnap to feel like a dry “identifier app,” so I leaned into playful sticker visuals to balance the AI’s limitations.
  • Sticker design: Making stickers feel as fun as the childhood albums that inspired me took more iteration than expected.
  • Scoping features: It was tempting to add everything (social feeds, achievements, etc.), but I had to stay focused on a minimum delightful product.

How I Built It

Catsnap was built with a stack designed for fast iteration:

  • 📱 Frontend: SwiftUI for a smooth, native iOS experience
  • 🧠 AI/ML: VisionKit + OpenAI
  • ☁️ Backend: AIProxy for key protection
  • 💸 Monetization: RevenueCat, of course

Built With

  • aiproxy
  • openai
  • swiftdata
  • swiftui
Share this project:

Updates