Design System 分層架構：Style × Color × Industry 的組合爆炸
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。
Insight
Miscellaneous articles including management perspectives, viewpoints, and other topics.
Design System Layered Architecture: From Preset to Platform
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.
AI 寫的 UI 為什麼總差一點？因為沒人告訴它什麼算好
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。
Why Does AI-Written UI Always Fall a Little Short? Because Nobody Told It What Counts as Good
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 導入不是選 A 或 B：三鏡頭 Workshop 設計
董事會下個月要 review，三份 AI 文件擺在桌上：Agentic AI、AI + SDLC、DevSecOps。高層想要創新，工程師想少加班，Workshop 從哪一個開始？多數團隊把它當成三選一，結果是 60% 的 GenAI 專案卡在 PoC。它們不是平行選項，是相依關係。本文示範用三幕 Workshop——痛點探索、三鏡頭亮燈、Quick Win → Core Change → Strategic Leap——讓高層與工程師在同一張地圖上排出順序。
AI Adoption Isn’t A or B: The Three-Lens Workshop
The board review is next month. Three AI documents are on the table: Agentic AI, AI + SDLC, DevSecOps. Leadership wants innovation. Engineers want fewer late nights. Where does the workshop start? Most teams treat this as a pick-one decision — and 60% of GenAI initiatives stall at PoC as a result. These aren’t parallel options; they’re dependencies. This post walks through a three-act workshop structure — Pain Discovery, Three-Lens Mapping, and Quick Win → Core Change → Strategic Leap — so leadership and engineers can sequence the work on the same map.
PR 跑出 120 個警告，為什麼只有 5 個值得看？
Junior 提交了 PR，AI review 吐出一份 40 頁的報告。120 個標記中,Senior 看了 20 分鐘，發現 80% 是風格，15% 是誤報。當警告太多，高信賴度風險就被淹沒了。PR review 有兩個特殊結構，讓它特別吃多 Agent 交叉驗證的紅利。
120 PR Flags. Why Only 5 Are Worth Reading?
A junior engineer submitted a PR, and the AI review returned a 40-page report. Among 120 flags, a senior engineer spent 20 minutes reviewing them and found that 80% were style comments and 15% were false positives. When there are too many warnings, high-confidence risks get buried. PR review has two special structural properties that make it especially benefit from multi-agent cross-validation.
91 個組件的 Skill 為什麼生不出一個訂單頁面？
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」。
Why Can’t a Skill with 91 Components Generate an Order Page?
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.”