AI Coding

I work extensively with Claude Code to accelerate development, from scaffolding components to refactoring entire modules. It runs alongside my editor as a collaborator that understands the codebase, follows conventions, and executes the tedious parts so I can focus on architecture and intent.

Beyond generation, I use AI as a critical reviewer: auditing code for accessibility gaps, inconsistent naming conventions, code conventions, design token usage, and optimization opportunities. The value isn't in replacing thinking — it's in getting honest, tireless feedback that improves the output.

AI output is only as good as the rails around it. I lean on Design System tokens, component contracts, and TypeScript to keep generated code on-brand and on-spec. That combination turns AI from a source of UI drift into a tool that actively reinforces consistency across teams.

Large refactors — API renames, token migrations, component version bumps — are where AI pays off most. I use it to apply repetitive changes across the codebase predictably, while I review diffs, validate tests, and catch the edge cases that need a human call.

Prompts, project context files, and clear conventions make AI dependable. I set up repo-level instructions, automated checks, and review steps so AI-assisted work ships with the same quality bar as anything else — type-checked, linted, tested, and consistent with the Design System.