Challenge
How can complex software ideas become working prototypes quickly without sacrificing engineering discipline?
Rapid prototyping often starts with code before the requirements, architecture, or operational constraints are understood. AI coding tools can accelerate that process, but without clear direction, they can also introduce inconsistent decisions and unnecessary rework. I wanted to explore a more deliberate approach using a complex, sensor-driven crew readiness system as a realistic engineering exercise.
Solution
Define the system first, then use AI coding agents to execute a phased implementation plan.
I started with an executive summary, business and functional requirements, technical specifications, a RACI matrix, and an implementation roadmap. Using Codex in VS Code, I directed the coding agent to first establish its own project tooling, skills, MCP integrations, and repository instructions. From there, it followed the documented phases to build a Next.js and Supabase prototype with simulated sensor data, readiness dashboards, and AI-assisted summaries.
Outcome
A functional prototype completed in two days with a documented, repeatable development process.
The result was a working Crew Readiness Platform demonstrating simulated physiological monitoring, anomaly detection, individual readiness insights, and AI-assisted analysis. I completed the initial prototype over a weekend, with phase-by-phase commits showing how it evolved. While the system uses simulated, unvalidated data, it demonstrates how upfront planning and experienced engineering oversight can make AI-assisted development faster and more intentional.