A look at full-stack delivery at Ford, where Angular, Java Spring Boot, backend APIs, and CI/CD meet the constraints of a large organization. The focus is on clarifying ambiguous requirements, understanding dependencies, and getting maintainable software through review and release while keeping company-specific details private.
Writing
Notes on building products, working with AI, and learning by doing.
How a consumer social app for K-drama fans grew from a product hypothesis into a mobile MVP. The story follows discovery around what fans do after finishing a show and the shift from structured show information toward casual conversation, reactions, and community.
How I use AI for exploration, implementation, debugging, and review while keeping responsibility for the product and technical decisions. The piece describes a practical loop of defining the outcome, inspecting the system, delegating bounded work, and verifying what was built.
An exploration of the infrastructure decisions that come with owning a side project, including AWS and cloud architecture, deployment, DNS, environments, and operations. It considers how understandable systems and deliberate tradeoffs can keep product iteration practical without overbuilding.
A working definition of product engineering as helping turn uncertainty into shipped learning. The piece explores why understanding the customer, questioning the problem, building across the stack, and learning after release belong together.
A reflection on side projects as a way to practice product ownership when nobody else defines the problem. It asks how to keep a project focused on a user's need, make a useful first version, and recognize when a prototype has taught you enough.
A practical note on using AI to move faster without skipping the work of understanding a product or its code. Its central rule is that an engineer still owns the architecture, behavior, risks, and user value of a change, even when an agent helped produce it.