How I work.
Every project I take on moves through the same disciplined pipeline. It keeps scope honest, surfaces risk early, and makes sure what ships actually solves the problem it was built for.
I start by understanding the real problem, not just the requested feature — who the users are, what's currently broken, and what success actually looks like.
Turning goals into concrete, testable requirements. I separate must-haves from nice-to-haves early so scope stays realistic.
Mapping out approaches and trade-offs before writing code — build vs. integrate, sync vs. async, simple vs. flexible.
Designing the data model, service boundaries, and integrations so the system stays maintainable as it grows, not just functional on day one.
Building in small, working increments with clear commits, so progress is visible and direction can shift without losing momentum.
Verifying behavior against real scenarios and edge cases — not just the happy path — before anything reaches production.
Shipping with a rollback plan and clear versioning, so releases are routine events rather than high-stakes ones.
Watching how the system behaves under real usage — performance, errors, and edge cases that only show up in production.
Treating launch as a starting point. I keep refining based on real feedback and usage patterns rather than considering the project "done."
For how this process holds up specifically on AI-heavy projects, read How I Structure Large AI Projects.