I’m the developer of Sunset Compass, a free iOS navigation tool for photographers and hikers.
The Problem: The current market leaders for ephemeris data (Sun Surveyor, PhotoPills) are incredible engineering feats, but they suffer from two issues:
They are complex and expensive ($10-$15), targeting the top 5% of professional photographers.
They largely ignore non-English markets, leaving a massive gap for casual users globally.
The Solution: I built Sunset Compass to be the "accessible" alternative. It tracks Sun and Moon positions, Golden Hour phases, and provides haptic feedback for alignment. Crucially, I decided to compete via a "Linguistic Moat" rather than feature bloat—the app is fully localized into 50 languages to target long-tail search intent in underserved markets.
Technical Stack & Optimizations: The app is built with React Native (Expo SDK 54). The biggest engineering challenge was map performance with large datasets of "Global Vista" pins:
Viewport-Based Rendering: I implemented a filtering logic that only renders pins visible in the current viewport (plus a 20% buffer zone). This reduced initial map load time from ~1000ms to ~200ms and cut memory usage significantly.
Parallel Storage: Using Promise.all to load pin data and favorites simultaneously from AsyncStorage during initialization.
Privacy Architecture: Unlike many competitors, there is zero cloud storage. All user locations and saved pins utilize local AsyncStorage. The app uses SunCalc for on-device astronomical calculations, meaning it works offline.
Current Status: The app is free (ad-supported, no subscriptions) and available on iOS.
I’d love feedback on:
The haptic feedback implementation (we use a 5-degree tolerance zone).
The "Glassmorphic" UI approach—does it feel too heavy for a utility app?
Any experience you have with ASO for tier-2/3 languages.
I’m the developer of Sunset Compass, a free iOS navigation tool for photographers and hikers.
The Problem: The current market leaders for ephemeris data (Sun Surveyor, PhotoPills) are incredible engineering feats, but they suffer from two issues:
They are complex and expensive ($10-$15), targeting the top 5% of professional photographers.
They largely ignore non-English markets, leaving a massive gap for casual users globally.
The Solution: I built Sunset Compass to be the "accessible" alternative. It tracks Sun and Moon positions, Golden Hour phases, and provides haptic feedback for alignment. Crucially, I decided to compete via a "Linguistic Moat" rather than feature bloat—the app is fully localized into 50 languages to target long-tail search intent in underserved markets.
Technical Stack & Optimizations: The app is built with React Native (Expo SDK 54). The biggest engineering challenge was map performance with large datasets of "Global Vista" pins:
Viewport-Based Rendering: I implemented a filtering logic that only renders pins visible in the current viewport (plus a 20% buffer zone). This reduced initial map load time from ~1000ms to ~200ms and cut memory usage significantly.
Parallel Storage: Using Promise.all to load pin data and favorites simultaneously from AsyncStorage during initialization.
Privacy Architecture: Unlike many competitors, there is zero cloud storage. All user locations and saved pins utilize local AsyncStorage. The app uses SunCalc for on-device astronomical calculations, meaning it works offline.
Current Status: The app is free (ad-supported, no subscriptions) and available on iOS.
I’d love feedback on:
The haptic feedback implementation (we use a 5-degree tolerance zone).
The "Glassmorphic" UI approach—does it feel too heavy for a utility app?
Any experience you have with ASO for tier-2/3 languages.
Thanks for taking a look!