When Design System client needs start overlapping, a flat Preset architecture can collapse into exploding maintenance costs. This post explores how a three-layer decoupled architecture of Style, Color, and Industry turns maintenance costs from exponential growth into modular work, enabling Design Systems to scale.
當 Design System 的客戶需求開始交叉，扁平的 Preset 架構會陷入維護成本爆炸的困境。本文探討如何透過 Style、Color 與 Industry 三層解耦架構，將維護成本從指數增長轉為模組化，實現設計系統的規模化。
PR merge count doubled — and so did production rollbacks. After adopting AI tools, one team watched weekly merges climb from 32 to 71, while monthly rollbacks jumped from 2 to 5. Every rolled-back PR had passed CI. AI accelerated the coding part, but the feedback net of review and testing didn’t become denser to match. This post breaks down the three-layer architecture of SDLC, DevOps, and CI/CD, and looks at which layer AI should be placed in.
PR merge 速度翻倍，rollback 次數也跟著翻倍。AI 加速的是寫程式碼那一段,但 review 與測試的反饋網沒跟著加密。本文拆解 SDLC、DevOps、CI/CD 三層架構,看 AI 該被擺進哪一層。
AI-generated UI quality is inconsistent because it lacks concrete constraints. UI Skills turns vague best practices into verifiable rules. This post analyzes its design patterns and shows you how to build a team-specific Skill constraint system.
AI 生成的 UI 品質參差不齊，因為缺乏具體約束。UI Skills 將模糊的最佳實踐轉化為可驗證的規則。解析其設計模式，教你建立團隊專屬的 Skill 約束系統。
A PM drops a Figma link, and an engineer replies with an API spec. Pick the wrong starting point, and later refactor costs may double. This post breaks down the tradeoffs among Design-First, API-First, and Code-First, and offers a practical hybrid framework for the AI era.
PM 丟 Figma，工程師回 API spec。起點選錯，後期重構成本可能翻倍。拆解 Design-First、API-First、Code-First 的取捨，並提供 AI 時代的混合實戰框架。
Developers often set a Skill’s responsibility scope too broadly, leading to overcomplicated architectures. This post breaks down the five levels of Skill capability boundaries and points out that its nature is a “knowledge base,” not an “automation engine.”
開發者常把 Skill 責任範圍設得過大，導致設計出過度複雜的架構。本文拆解 Skill 的 5 層能力邊界,指出其本質是「知識庫」而非「自動化引擎」。