Spaces:
Runtime error
Runtime error
| # LinkedIn Post - Episode 6: Design System Automation v3.2 | |
| ## Main Post (Copy-Paste Ready) | |
| --- | |
| Every designer has done this: Open DevTools. Inspect element. Copy hex code. Paste to spreadsheet. Recreate in Figma. Repeat 200 times. | |
| I spent 3-5 days manually extracting design tokens from websites. Then more time recreating them in Figma as variables. | |
| So I built a 3-layer system: deterministic extraction + rule-based color classifier + 4 AI agents. | |
| **The Architecture (v3.2):** | |
| Layer 1 (FREE, <1 second): | |
| - 7-source extraction (Playwright + Firecrawl) | |
| - Normalizer: radius, shadows, colors all cleaned and named | |
| - Color Classifier (815 lines, deterministic): CSS evidence -> category -> token name | |
| Layer 2 (FREE, <1 second): | |
| - Rule Engine: WCAG contrast (actual FG/BG pairs), type scale detection, spacing grid | |
| - 113 tests passing, 100% reproducible | |
| Layer 3 (~$0.003, 4 specialized agents): | |
| - AURORA: brand color advisor (Qwen 72B) — advisory only, can't override classifier | |
| - ATLAS: benchmarks against 8 industry design systems (Llama 70B) | |
| - SENTINEL: prioritizes fixes by business impact (Qwen 72B) | |
| - NEXUS: synthesizes everything, resolves contradictions (Llama 70B) | |
| **The Pipeline:** | |
| Website URL -> 7-Source Extraction -> Color Classifier -> Rule Engine -> DTCG JSON | |
| -> Figma Plugin -> Variables + Styles + Auto-Generated Visual Spec Page | |
| -> AI Analysis -> Accept/Reject -> TO-BE JSON -> Compare in Figma | |
| **My biggest lesson building V1 -> V2 -> V3:** | |
| V1: LLMs for everything. $0.50/run. Hallucinated contrast ratios. | |
| V2: Rules + LLM split. $0.003/run. But 3 naming systems fighting in exports. | |
| V3: Rules + Classifier + Advisory LLM. $0.003/run. ONE naming authority. Clean output. | |
| The fix wasn't better AI. It was a clear authority chain: | |
| 1. Color Classifier (PRIMARY) - deterministic, covers ALL colors | |
| 2. AURORA LLM (SECONDARY) - can only suggest semantic roles | |
| 3. Normalizer (FALLBACK) - hue + numeric shade | |
| **Real results:** | |
| - 143 colors extracted, classified, and named (deterministically) | |
| - 220 FG/BG pairs checked for AA compliance | |
| - Radius: raw CSS garbage -> none/sm/md/lg/xl/full (normalized) | |
| - Shadows: unsorted -> xs/sm/md/lg/xl (5 progressive levels) | |
| - Benchmarked against Material 3, Polaris, Atlassian + 5 more | |
| - Output: W3C DTCG v1 compliant JSON with $extensions metadata | |
| - Figma: auto-generated visual spec with AA badges | |
| - Time: 3-5 days -> ~15 minutes | |
| - Cost: ~$0.003 | |
| The key? **I stay in control.** AI recommends, I decide. | |
| Full workflow + architecture: [Medium link] | |
| Try it: [HuggingFace Space link] | |
| Code: [GitHub link] | |
| Episode 6 of "AI in My Daily Work" | |
| What design workflows are you automating? | |
| #UXDesign #AIEngineering #DesignSystems #Figma #HuggingFace #Accessibility #WCAG #DesignTokens #W3CDTCG #BuildInPublic | |
| --- | |
| ## First Comment (Post Immediately After) | |
| **Resources:** | |
| Medium Article: [link] | |
| Complete architecture breakdown + V1 -> V2 -> V3 evolution + Figma integration | |
| Live Demo: [HuggingFace Space link] | |
| Try it with any website URL | |
| GitHub: [link] | |
| Open source - star it if useful! | |
| --- | |
| **The Naming Authority Problem (V3's key insight):** | |
| V2 had THREE competing systems naming colors: | |
| - Normalizer: "color.blue.light" (word-based) | |
| - Export layer: "color.blue.500" (numeric) | |
| - AURORA LLM: "brand.primary" (whatever it wanted) | |
| Result in Figma: blue.300, blue.dark, blue.light, blue.base — ALL in the same export. | |
| V3 fix: ONE authority. Color Classifier (deterministic) is PRIMARY. AURORA is advisory only — it can suggest "this blue should be brand.primary" but can't rename palette colors. | |
| `filter_aurora_naming_map()` enforces the boundary. Clean Figma output, every time. | |
| --- | |
| **What's Next — Episode 7: Automated Component Generation** | |
| Researched 30+ tools. Found a genuine market gap: | |
| No production tool takes DTCG JSON and outputs Figma components with variants. | |
| Building it. Button (60 variants), TextInput, Card, Toast, Checkbox/Radio. | |
| Figma Plugin API supports everything: createComponent(), combineAsVariants(), setBoundVariable(). | |
| Same tokens in = same components out. Fully deterministic. | |
| --- | |
| **Previous Episodes:** | |
| - Episode 5: UX Friction Analysis (7 agents + Databricks) | |
| - Episode 4: UI Regression Testing | |
| - Episode 3: Review Intelligence System | |
| What should I build for Episode 7? Drop ideas below | |
| --- | |
| ## Alternative Version (Shorter, Story-Driven) | |
| --- | |
| "Can you audit their design system and document it in Figma?" | |
| 3-5 days of DevTools, spreadsheets, and manual Figma work. | |
| I built something different. Three versions, actually. | |
| V1: Used LLMs for everything. $0.50/run. They hallucinate math. | |
| V2: Split into rules + AI. $0.003/run. But three systems fought over color names. Figma output was chaos. | |
| V3: Clear authority chain. One color classifier (deterministic, 815 lines). LLMs are advisory only. W3C DTCG-compliant JSON. Auto-generated visual spec in Figma. | |
| What it does now: | |
| - 7-source extraction from any website | |
| - Rule-based color classification (brand/text/bg/border/feedback) | |
| - WCAG AA check on 220 actual FG/BG pairs | |
| - 4 AI agents for brand analysis, benchmarking, audit, synthesis | |
| - W3C standard JSON output | |
| - Figma plugin: variables + styles + visual spec page | |
| 15 minutes. $0.003. I stay in control. | |
| Full architecture: [Medium link] | |
| Demo: [HuggingFace link] | |
| Episode 6 of "AI in My Daily Work" | |
| #DesignSystems #AIAgents #UXDesign #Figma #Automation #HuggingFace #WCAG #W3CDTCG | |
| --- | |
| ## Image Suggestions | |
| 1. **Hero:** V1 vs V2 vs V3 comparison table showing the evolution | |
| 2. **Architecture:** 3-layer diagram (Extraction -> Classification+Rules -> 4 Agents) | |
| 3. **Naming Authority:** Before/after showing Figma chaos vs clean output | |
| 4. **Figma Visual Spec:** Screenshot of auto-generated spec page | |
| 5. **Agent Output:** NEXUS synthesis with scores + top 3 actions | |
| --- | |
| ## Hashtags (Optimized) | |
| Primary (always include): | |
| #UXDesign #AIEngineering #DesignSystems #Figma #HuggingFace | |
| Secondary (mix based on audience): | |
| #DesignTokens #W3CDTCG #Accessibility #WCAG #BuildInPublic #Automation #MultiAgent | |
| --- | |
| ## Posting Strategy | |
| **Best time:** Tuesday-Thursday, 8-10 AM your timezone | |
| **Key messages for V3:** | |
| 1. V1 -> V2 -> V3 evolution story (naming authority problem) | |
| 2. Color Classifier (815 lines, deterministic) as key innovation | |
| 3. W3C DTCG v1 compliance — standards over proprietary formats | |
| 4. Figma visual spec auto-generation | |
| 5. Component generation gap (Episode 7 teaser) | |
| **Differentiation from Episode 5:** | |
| - Episode 5 = UX friction analysis (GA4 + Clarity + Databricks) | |
| - Episode 6 = Design system extraction (Playwright + Classifier + Figma + HuggingFace) | |
| - Same philosophy: deterministic code for certainty, LLMs for ambiguity | |