After 18+ years in engineering, from COM and MFC to SwiftUI and CoreML, I’ve learned that deep technical foundations don’t expire—they evolve. Here’s how I transitioned from legacy code to AI-powered mobile apps, and why I believe now is the most exciting time to be a developer.
Engineering Principles Don’t Expire
I started my journey writing C++ in Visual Studio, debugging MFC windows, and working with ATL COM and DirectShow. Back then, performance and memory efficiency weren’t just best practices—they were survival skills.
Those same skills now guide how I build modern apps with machine learning.
Whether I’m optimizing a CoreML model on iOS or embedding TensorFlow Lite on Android, I fall back on the same principles: minimize latency, handle edge cases, and test like it’s production.
Mobile Development: From Xamarin to SwiftUI and Beyond
Over the years, I’ve built and shipped apps using:
- Xamarin
- Objective-C & Swift
- Kotlin
- C#.NET
- Adobe RMSDK (PDF rendering)
- Apple Watch integrations
The shift came when I began adding AI-enhanced features—from gesture detection to personalized coaching using sensor data and on-device ML.
In one project, pairing Apple Watch motion data with a CoreML model led to a 30% increase in retention. That wasn’t a coincidence—intelligent UX keeps users coming back.
AI Isn’t a Buzzword. It’s the New Runtime.
As an engineer, I initially approached AI like any SDK: learn the docs, integrate, test. But AI requires a different mindset.
You're not just coding logic—you're shaping behavior with data.
I've worked with:
- CoreML & CreateML (iOS on-device ML)
- TensorFlow Lite (Android inference)
- Firebase ML Kit (OCR, detection)
- OpenAI APIs (chat, summarization)
And I’ve realized: the best developers of the future won’t just know how to code—they’ll know how to design AI-first systems.
What Hasn’t Changed
Despite all the changes, some truths remain:
- Performance still matters. Mobile AI can’t lag.
- User experience leads. Intelligence must enhance UX, not complicate it.
- Testing is critical. Edge cases are still your biggest enemy.
AI adds layers of abstraction, but it doesn’t replace engineering discipline.
Why I'm Writing This
I’m now focused on sharing everything I’ve learned—from debugging COM objects to building real-time AI features on mobile.
This isn’t a side project. Writing is part of my career now, and hopefully a path to sustaining it financially through value-based content.
If you're a systems developer moving into AI, or a mobile engineer curious about integrating machine learning, follow me—I’ll be sharing hands-on insights, architecture decisions, and real-world strategies.
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Let's build the future of intelligent mobile apps—together.**
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