Product Experiments
Exploring AI-assisted development and modern product workflows
Points
An idle game about credit card rewards, built in a weekend
Points is a credit card rewards life simulator that started as an AI game competition entry. Players optimize spending, juggle card ecosystems, and chase dream vacations. The game came together quickly because credit card rewards are a real-world system with well-defined rules, and real-world data makes for a great spec when building with agentic AI tools.
Game Design:
- Interconnected systems: career, city, airline hubs, card selection, and vacation planning
- Life progression that starts simple and escalates into complex multi-card optimization
- Strategic card management with signup bonuses, category spend, and transfer partners
Key Learnings:
- Real-world domains with structured data are ideal candidates for rapid agentic AI development
- Game jam time pressure forces ruthless scope decisions that mirror real product prioritization
- Ongoing development from weekend prototype into a more polished experience
Health Sender
iOS utility for AI-friendly health data export
Built a lightweight iOS app that exports Apple Health data in formats optimized for AI analysis. The project served as hands-on exploration of AI-assisted development workflows and the complete iOS app lifecycle.
Technical Approach:
- Native iOS development using AI-assisted workflows (Cursor, Claude Code)
- HealthKit integration for secure data access
- Optimized data formatting for LLM consumption
- App Store deployment and user feedback integration
Key Learnings:
- AI-assisted development dramatically accelerates learning new platforms
- Apple's health data policies significantly shape product possibilities
- One-time purchase model vs. subscription economics in mobile apps
Development Philosophy
These projects share a common approach:
Domain-driven development
Learn a domain deeply, then let that knowledge drive the product
Complete product cycles
From concept through deployment to understand full development challenges
AI as force multiplier
Leverage AI assistance for rapid prototyping and validation
Platform-native design
Work within platform constraints rather than against them
Each experiment teaches valuable lessons about modern product development, AI integration, and market dynamics while demonstrating practical technical capabilities.