AI-native mobile — how I build and what I ship.
AI is now part of both the engineering process and the product. From AI-assisted development to privacy-first on-device intelligence, this is where mobile is going.
AI-Assisted Development
Embed AI copilots into the daily engineering loop — scaffolding, refactoring, test generation and code review — to compress boilerplate and free senior time for architecture and edge cases.
Prompt Engineering
Design reliable, reusable prompt systems and context strategies that turn LLMs into dependable engineering collaborators rather than one-off helpers.
AI Feature Integration
Ship user-facing AI into mobile products — smart search, summarization, recommendations and conversational flows — behind clean, testable capability interfaces.
On-Device AI
Run inference locally for privacy and latency using TensorFlow Lite and Core ML, with hybrid routing to the cloud only when heavier reasoning is required.
AI Toolchain
The daily stack that compresses boilerplate and elevates code quality.
Cursor AI
Primary AI IDE for architecture-aware coding
GitHub Copilot
Inline completion and refactoring
ChatGPT
Design exploration and problem solving
Claude
Long-context reasoning and code review
Gemini
Multimodal and research assistance
Conversational mobile
Every enterprise app gains a proactive, context-aware copilot as a first-class layer.
Privacy-first intelligence
On-device inference makes powerful AI possible without shipping sensitive data.
AI-native architecture
Tool-calling and agents become standard components in the mobile architecture stack.
Let's build the next generation of AI-powered mobile.
Open to Principal / Staff mobile architecture roles and AI mobile initiatives. Let's talk about what you're building.