AI-powered "vibe coding" is reversing the design workflow. Instead of starting in Figma, designers now build functional prototypes directly with code-generating tools. Figma has shifted from being the first step (exploration) to the last step (fine-tuning the final 20% of pixel-perfect details).

For a solo founder moving fast, a comprehensive Figma UI kit is often a waste of time. Instead, use Figma at two extremes: for very rough structural exploration (even wireframing with screenshots) and for creating specific graphic assets (gradients, icons). Build the details directly in code.

When iterating on a Gemini 3.0-generated app, the host uses the annotation feature to draw directly on the preview to request changes. This visual feedback loop allows for more precise and context-specific design adjustments compared to relying solely on ambiguous text descriptions.

The handoff between AI generation and manual refinement is a major friction point. Tools like Subframe solve this by allowing users to seamlessly switch between an 'Ask AI' mode for generative tasks and a 'Design' mode for manual, Figma-like adjustments on the same canvas.

Instead of creating static mockups in Figma, Cursor's design team prototypes directly in their AI code editor. This allows them to interact with the "life states of the app" and get a more realistic feel for the product, bridging the gap between design and engineering.

Visual "vibe coding" platforms, intended to simplify development, can add unnecessary complexity and scope creep to simple projects. When this happens, it's cheap and effective to abandon the tool and start from scratch in a code editor like Cursor to maintain simplicity.

While "vibe coding" tools are excellent for sparking interest and building initial prototypes, transitioning a project into a maintainable product requires learning the underlying code. AI code editors like Cursor act as the next step, helping users bridge the gap from prompt-based generation to hands-on software engineering.

A practical AI workflow for product teams is to screenshot their current application and prompt an AI to clone it with modifications. This allows for rapid visualization of new features and UI changes, creating an efficient feedback loop for product development.

The panel suggests a best practice for AI prototyping tools: focus on pinpointed interactions or small, specific user flows. Once a prototype grows to encompass the entire product, it's more efficient to move directly into the codebase, as you're past the point of exploration.