I'm a backend developer with 7 years of experience building scalable systems for fintech and e-commerce companies. What started as curiosity about Rust's performance promises turned into a deep dive into WebAssembly-and eventually, this book.

Like many developers, I was frustrated with AI inference costs and privacy concerns. After spending months experimenting with running AI models directly in browsers using Rust and WebAssembly, I realized this approach could solve real problems for real teams.

My Journey to Browser AI

My background is rooted in traditional backend development-APIs, databases, and distributed systems. But when I first saw a neural network running at near-native speed in a browser tab, I knew something fundamental was shifting in how we think about AI deployment.

I've spent the last two years refining techniques for client-side AI inference, contributing to open-source WASM projects, and helping teams transition from expensive cloud AI services to privacy-first browser solutions.

Why I Wrote This Book

Too many AI tutorials are either too theoretical or assume extensive ML backgrounds. I wanted to create something different-a practical guide that gets working results fast, written by someone who learned these technologies the hard way.

This book contains everything I wish I had when I started: the gotchas, the shortcuts, and the real-world patterns that actually work in production.

Beyond This Book

When I'm not writing Rust or optimizing WASM modules, you'll find me contributing to the Rust AI ecosystem or speaking at local meetups about the future of edge computing.

I believe the next wave of AI applications will run where users are-in their browsers, on their devices, with their data staying private. This book is my contribution to making that future accessible to every developer.