Automating the design system setup (50+ screens, code, docs) in &10 hours

Source: Dev.to

We’ve been experimenting with automating the “first month” of a web project—the tedious phase of setting up repos, configuring Tailwind, building basic components, and arguing over spacing. By generating the design assets and the code simultaneously from a shared requirement set, we managed to condense a typical 8‑12 week setup foundation into roughly 3‑10 hours.

The Core Problem: Translation Drift

Usually a designer hands off a static Figma file and a developer translates it into dynamic code. They drift apart almost immediately. Most AI tools only generate snippets or single screens, leaving you with a “Frankenstein” UI—​inconsistent tokens, varied padding, and no shared architecture.

We realized the issue was trying to sync two different sources of truth. So we tried a System‑First approach.

System‑First Pipeline

Instead of Design → Code, we built a pipeline (TheSSS AI) that works like this:

1. Requirements →
2. Context‑Aware Interpretation →
3. Governance

The AI generates the rules first (tokens, accessibility standards, spacing scales) in JSON. Then it spawns the artifacts in parallel from that single truth:

• Design Tokens (JSON)
• UI Mockups (visual assets)
• Component Library (React/Vue + Storybook)
• Documentation (Markdown)

Because the React button component and the Figma mockup are generated from the same parent data at the same time, there is no translation error—they just match.

Results

We can generate a full “Foundation‑Ready” MVP—​auth flows, dashboard layouts, settings pages, basic CRUD views—in about a working day. The system handles the mundane stuff nobody on our team likes doing:

• Accessibility annotations
• Responsive variants
• Documentation files

Trade‑offs

• The “SaaS Look” – Designs are clean and accessible but resemble a standard Bootstrap/SaaS style. If you need a unique, award‑winning brand identity, this approach feels too rigid. It automates the “boring” layer of design, not the creative layer.
• The Logic Gap – UI and state‑management boilerplate are generated perfectly, but deep business logic isn’t understood. Complex validation rules must be explicitly prompted for, which can take longer than hand‑coding them.
• The “Eject” Problem – Regenerating the entire foundation is easy, but once a human developer modifies the code, re‑running the AI risks overwriting those changes. We’re still figuring out a reconciliation strategy.

Open Questions

• Are others experimenting with this “simultaneous generation” approach?
• Are we moving toward a world where the initial codebase is disposable and regenerated when requirements change?
• Does automating the setup phase make it obsolete, or does it simply encourage code bloat?