You inherit a 4000-line function. No comments, no docs, original author left. This is not a technical problem—it is a psychological one. This article covers how to use AI to build cognitive maps, write tests, plan refactoring, and convince your manager to give you time to address technical debt.
接手一個 4000 行的 function，沒註解、沒文件、原作者離職。這不是技術問題，是心理壓力問題。這篇講怎麼用 AI 建立認知地圖、寫測試、拆分重構，以及怎麼說服主管給你時間處理技術債。
New project, README says npm install and you are good. Half a day later, still debugging the environment. Then I started using Claude Code. Same task, 10 minutes. This article covers exactly how to do it, and when to let AI take over completely.
接手新專案，README 說「npm install 就能跑」，結果花了半天還在 debug 環境。後來我開始用 Claude Code，同樣的事 10 分鐘搞定。這篇講具體怎麼做，以及什麼時候該讓 AI 完全接手。
AI wrote 200 tests for me. All passed. We deployed. Then a bug hit production. The problem wasn’t that AI wrote bad tests—it tested the wrong things. This article shares a simple framework: What stays with humans, How goes to AI.
AI 幫我寫了 200 個測試，全部通過，上線還是出 bug。問題不是 AI 寫錯，是它測的東西根本不是該測的。這篇文章分享一個簡單的框架：What 留給人，How 交給 AI。
AI excels at executing tests, but struggles with judging ‘what to test’ and ‘whether results are correct.’ QA’s core value is shifting from execution to acceptance—validating AI’s validation. This article analyzes the four most important QA skills in the AI era and the short, medium, and long-term impacts on teams.
AI 擅長執行測試，但不擅長判斷「該測什麼」和「結果對不對」。QA 的核心價值正在從執行轉向驗收——驗證 AI 的驗證。本文分析 AI 時代 QA 最重要的四種能力排序，以及對團隊的短中長期影響。
After adopting AI, your boss asks about the results—and you can’t give a straight answer. It’s not because AI doesn’t work. It’s because no one defined what ‘success’ looks like from the start. This article provides a practical acceptance framework: four metrics you can start tracking today, and how to establish a baseline when you have no historical data.
導入 AI 後，老闆問成效如何，你答不出來。不是因為 AI 沒用，而是從一開始就沒有定義「成功」長什麼樣。這篇文章提供一個務實的驗收框架：四個今天就能開始追蹤的指標，以及如何在沒有歷史數據的情況下建立比較基準。