I'm an iOS developer based in Brisbane, Australia, building Swift & SwiftUI apps across health, fitness, and productivity. I have 5+ years of experience shipping apps to the App Store and a strong interest in on-device AI and ML.
- π 2nd Place β Google MedGemma Impact Challenge (March 2026) among 850+ global teams
- π± Multiple live apps on the App Store
- π€ On-device ML with Apple MLX, CoreML, and Gemini AI
- πΊ iOS development content on YouTube @developerjosh
- π joshbourke.com
Social fitness app with real-time step leaderboards powered by HealthKit and Supabase Β· steppy.fit
- Friends compete on daily step leaderboards with live HealthKit sync
- Custom Supabase backend with RLS policies, PostgreSQL RPCs, and Edge Functions
- Group management, username validation, reporting system, and App Store-compliant account deletion
- Profile customisation with SF Symbol icons and a SwiftUI parallax hero view
Stack:
SwiftUIHealthKitSupabase@Observable
- Two-stage on-device CoreML pipeline: food/no-food classifier β food identification model - Gemini AI classifies mixed plates and maps detected items to a local nutrition database - Supabase cloud sync + Core Data local persistence with `NSFetchedResultsController` and batch inserts - RevenueCat subscriptions, Firebase Analytics, and a calorie/macro goal manager **Stack:** `SwiftUI` `CoreML` `Gemini AI` `Supabase` `Core Data` `RevenueCat` `Firebase`AI-powered whole food nutrition tracker Β· nutrify.app
Kaggle competition-winning iOS app for AI-powered home inventory Β· keeptrack.app
- π₯ Kaggle competition winner
- Gemini AI analyses photos of household items to autofill name, brand, description, location, and estimated value
- Firebase backend for cloud storage and sync; supports receipt/warranty attachments and PDF export
Stack:
SwiftUIGemini AIFirebase
2nd Place β Google MedGemma Impact Challenge Β· github.com/mrdbourke/sunny
π Recognised as 2nd place in Google's MedGemma Impact Challenge (March 2026) among 850+ global teams β featured on the Google Keyword blog.
- Built the iOS app for an open-source on-device skin health tracker
- Fine-tuned MedGemma-1.5 on 1,000+ images; deployed on-device via MLX 4-bit quantisation
- Generates structured skin reports from photos while keeping all data completely private on-device
Stack:
SwiftUIApple MLXMedGemmaCoreML



