Insight

Miscellaneous articles including management perspectives, viewpoints, and other topics.

AI Adoption Isn’t A or B: The Three-Lens Workshop

by

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？

by

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?

by

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.

AI 導入不是選 A 或 B：三鏡頭 Workshop 設計

by

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 導入後，為什麼 Senior 反而更累？

by

導入 AI 後團隊速度沒變快，Senior 卻每天加班——junior 用 Cursor 一小時產出 300 行，Senior 要花兩小時 review、補測試、抓邊界。產出曲線往上，認知負擔曲線更陡。從 code review 結構、責任邊界、AI 信任分層三個視角拆，看為什麼瓶頸從「寫得慢」變成「審得慢」，以及怎麼把 Senior 從人肉 linter 救回來。

Why Senior Engineers End Up More Exhausted After AI Adoption

by

Your team adopted AI coding tools and shipped faster—but your seniors are burning out. Juniors push 300 lines an hour with Cursor; seniors spend two hours reviewing, patching tests, and chasing edge cases the AI didn’t see. Throughput went up, cognitive load went up steeper. Three lenses on why the bottleneck shifted from writing to reviewing—code review structure, ownership boundaries, AI trust tiers—and how to stop using your seniors as human linters.