The problem
What was broken before AI
Design teams often have to choose between fast static mockups and slower working prototypes. Figma can communicate layout and flow, but it does not always capture the feel of real interaction, data, loading states, edge cases, or implementation constraints. Engineers may not see the full intent until later, and designers may not discover interaction problems until the work has already moved downstream.
What changed
What the use case made possible
AI coding tools let designers push further into the prototype stage. A Figma idea can become a working artifact sooner, and custom Claude Code skills can make repeated development tasks easier to run. Instead of a one-off handoff, the design process can include more tangible artifacts: small apps, interactive states, reusable instructions, and prototypes that reveal whether the idea actually works.
Why this matters
Why this use case is worth studying
Brian’s workflow is valuable because it gives designers a more direct way to test their own ideas. The prototype does not have to be production code to be useful. It just has to be real enough for someone to click, feel, critique, and improve. AI lowers the cost of getting to that moment, which makes design feedback sharper and earlier.
Use this when
When this pattern applies
Use this pattern when a design idea needs to be clicked, tested, or felt before the team can judge it. It works especially well for interaction-heavy flows, complex states, design-system components, and moments where static Figma screens are not enough.
