This talk shows how to run your design system on Figma Variables as a living graph—not just a table of values. We’ll explore collections, modes, and aliases as first-class nodes you can trace, validate, and debug visually before anything ships. You’ll see how variables become a new contract with engineering and why this model is deterministic by design and ready for AI. We’ll demo Variable Visualiser (VV) and its AI assistant: ask for what you need, then watch it design, bind, and refactor variables—discover relationships, create collections & modes, bind attributes, rename at scale, or generate OKLCH color scales that match a reference. Every action is explicit, reviewable, and reversible. Finally, we’ll push the same graph straight to code: export VV as JSON, keep the bindings, and have build outputs match the Figma file 1:1—no translation layer, no divergence, no surprises.

What you’ll learn

• How to model variables as a graph: collections, modes, aliases, and dependencies.

• Visual debugging: trace impacts, catch missing bindings, validate ranges, and spot collisions.

• Using VV’s AI assistant to create/rename/bind at scale and auto-generate color scales from a target.

• Design-to-dev in one move: export JSON from VV and consume it in build pipelines.

• No drift strategies for multi-brand, multi-mode systems—deterministic by contract.

Who this is for
Design system architects, advanced Figma users, and front-end engineers who want a scalable, AI-friendly pipeline with zero hand-off drama. Session outline

1. Variables as a Living Graph (collections, modes, aliases, semantics)

2. Visual Validation & Debugging (dependency tracing, guardrails)

3. VV AI Assistant (prompt → structured variable ops, reviewable diffs)

4. Design→Dev Export (VV JSON, binding parity in code)

5. No Drift Playbook (governance, reviews, CI checks)

6. Q&A

Live demos

• Build a brandable Button/Card across light/dark with variable bindings only.

• AI-generated color scale to match a reference, then propagate via aliases.

• Export VV JSON and render the same UI in code—values match by definition.

Key takeaways

• Treat variables as your single source of truth and review them like code.

• Determinism beats documentation: if it’s bound, it can’t drift.

• AI is finally useful in DS when it operates on a well-structured variable graph.