Build Log
Building my first MVP with AI
The biggest advantage was not faster code. It was the ability to move repeatedly between questions, prototypes and decisions.
Before building, I assumed AI would mainly help me write code. In practice, its more important role was helping me keep the whole problem visible while I moved through unfamiliar technical decisions.
The process became less linear. Research led to a prototype, the prototype exposed a missing assumption, and that assumption changed the product structure. AI helped me move through that loop without treating every revision as a restart.
Speed only helps when the question is clear
AI can produce a large amount of code quickly, but speed amplifies ambiguity. When the product boundary was unclear, the output became generic, overbuilt or difficult to maintain.
The most productive moments came from narrowing the decision first: what must this version prove, what can remain manual, and what data cannot be exposed in the front end?
The useful unit of progress was not code written. It was uncertainty removed.
A better loop for future projects
My current workflow is simple: define the question, inspect the constraints, build the smallest testable path, review what changed and document the decision.
That loop turns AI from a code generator into a project partner. It still requires judgment, but it shortens the distance between not knowing and having something concrete to evaluate.