Method
The repeatable process behind every case study, and the calls I make the same way regardless of the codebase.
Architect → Build → Harden → Measure
Every case study runs through the same four stages, and each hands the next a concrete output — so the process is repeatable, not improvised.
I model the domains first — what owns what state, where the API surface sits, which pieces are genuinely shared. Feature slices with SOLID boundaries, so a new domain is a new folder instead of a refactor. This is the cheapest decision in the project and the most expensive one to reverse.
Output — ADR · feature-slice map · component boundaries
A screen should be assembled out of components that already exist and are already documented. TypeScript carries the contract between slices, Storybook holds the primitives, and server state stays separate from client state so data-fetching bugs don't turn into rendering bugs.
Output — Storybook library · typed API layer · state model
Tests are what make frequent releases safe rather than merely frequent. Test-first with Jest where the assertion shapes the design, Cypress on the paths a client would notice, both wired into CI/CD as a gate — plus cross-browser validation, because 'works on my machine' is a bug report waiting to be filed.
Output — Cypress E2E · CI quality gates · BrowserStack matrix
Every load-time win I can point to started from a measurement, not an instinct. Bundle analysis, code-splitting, lazy loading, caching — then measure again and report the before/after against a defined baseline rather than claiming causality.
Output — Bundle audit · Core Web Vitals · before/after read
Four calls I make the same way on every codebase — the reason the numbers below them hold up.
Where you draw the boundaries is the cheapest decision in a project and the most expensive one to reverse. Feature slices with SOLID edges, so a new domain is a new folder rather than a refactor.
A screen should be composed, not authored. The Storybook library at Kodez is what turned screens into composition, and I judge it cut frontend delivery time by roughly a third — but only after a pattern had proven itself twice. Premature generalization is just debt with better manners.
Profile first, optimize second. Every load-time win I can point to — ~40% at Kodez, ~15% at Axinom — started from a measurement, not an instinct. And never read the average alone; it hides the users having the worst time.
Release throughput isn't a function of moving fast, it's a function of how cheaply you can prove you didn't break anything. ~90% test coverage in CI wasn't overhead on 40+ releases — it's why there were 40+ releases.