Three Projects.
50,000+ Lines of Code.
One Developer.

These three projects were built by a single developer, a medical student and military officer with no formal CS degree, using AI as a force multiplier. Claude Code's autonomous agents researched APIs, generated components, debugged edge cases, and iterated on designs in real-time. The result is a collection of production-grade applications that would traditionally require a team of specialists. All three were started after March 15th, 2026, built in free time after clinical rotations and in between study sessions. That alone says more about where the tools are heading than anything about me individually.

The motivation behind each project is personal. The NCAAT Bracket and Masters Tournament Hub exist because I love sports. March Madness and the Masters are events I follow every year with genuine passion, and building interactive tools around them was a natural way to test what AI-augmented development could produce. The Step 2 CK Study Engine exists because I needed it. No commercial platform offered the adaptive, data-dense study architecture I wanted for my medical education, so I built one. Claude Code's /teach-me skill became a built-in tutor, letting me learn new patterns in context as I developed the study platform itself.

Beyond writing code, the AI ecosystem extends into thinking and decision-making. I use /council to pressure-test ideas from multiple angles before committing to an architecture. /brainstorm for structured ideation when I am stuck. Auto-agents to delegate research, refactoring, and testing to parallel workers while I focus on design. These are not gimmicks. They are workflows that change how a solo builder operates.

As a military officer, I also care about understanding how emerging technology reshapes the cyber landscape. I have started incorporating tools like /threat-model, /security-review, and /after-action into that learning process. Knowing how AI systems work, how they can be directed, and where they are vulnerable is no longer optional for anyone responsible for operational security. Building with these tools is one of the best ways to develop that intuition firsthand.

But the deeper thesis is about what comes next. We are approaching a point where AI capabilities compound faster than any individual can fully track. GitHub Copilot normalized AI in the editor. OpenAI's Codex proved that natural language could generate working code. Model Context Protocol plugins are connecting AI to every tool and API imaginable. These capabilities will not stay behind developer consoles forever. They will leak into every profession, every workflow, every industry. The professionals who thrive in that environment will not be the ones who resist AI or passively consume it. They will be the ones who learned to operate fluently in a dual ecosystem, human and machine working in concert. The bottleneck is no longer technical skill. It is imagination, domain knowledge, and the willingness to build.

These projects are my small proof of that alignment. Evidence that someone who understands the problem space and has learned to work alongside AI can ship real software that solves real problems, in days rather than months. I am still learning, still building, and still a long way from where I want to be. But I believe this is the direction the world is heading, and I would rather be early than late.