Hi, I'm Christopher Kilo.

My path into software engineering hasn't followed a traditional four-year computer science degree program. It began with formal career and technical education and continued through independent learning driven by real projects.

Through Job Corps and Davis Technical College, I built an early foundation in technology, IT, web development, and design. From there, the projects I wanted to build began demanding skills I hadn't learned yet.

So I learned them.

Modern frontend frameworks led into backend systems. Backend systems led into authentication, databases, and APIs. Deployments led into cloud infrastructure and CI/CD. Larger applications introduced testing, accessibility, concurrency, distributed workflows, system architecture, and production debugging.

Much of that knowledge was developed independently through documentation, experimentation, debugging, and building systems complex enough to expose gaps in my understanding.

I learn best when technology has a purpose: understand the problem, learn what the system requires, build it, test it, break it, understand why it broke, and improve it.

AI has become part of how I learn and build, but not as a replacement for understanding the work.

I use AI to explore unfamiliar concepts, challenge architectural decisions, develop implementation plans, review code, identify failure modes, create test strategies, and translate broad requirements into concrete engineering tasks.

Over time, that evolved into a structured workflow built around context, constraints, acceptance criteria, regression protection, and validation.

The goal isn't simply to make an AI generate more code. It's to reduce ambiguity, understand the decisions being made, and verify that the resulting system actually works.