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。
🌏 閱讀中文版本 By: rajatim Abstract: We celebrate “agile” and “failing fast,” but we seldom talk about the cost of failure. This article explores an ongoing paradigm shift in product development: moving from “Hypothesis-Driven Development” to a “Continuously Evolving Intelligence” formed by the synergy of DevOps, PM, and AI. This is not just a process optimization; … Read more
本文探討一個產品開發的範式轉移：從被動驗證假說，演進為由 DevOps、PM 與 AI 構成的「持續進化智慧」體。文章將闡述這個新範式如何重塑技術人員的價值，並提供一個可執行的架構藍圖，將團隊從功能實現者，升級為自我進化系統的建築師。
From 4 hours of manual LAMP setup to 30 minutes with Bitnami. Deep dive into how pre-packaged stacks reduce operational costs by 60-75%. Covers application ecosystem, team sizing, cost-benefit analysis, and graduation strategies. For SA, DevOps, and Backend engineers in 1-5 person teams.
從手動架設 LAMP 的 4 小時到 Bitnami 的 30 分鐘，深入解析預打包堆疊如何降低維運成本 60-75%。涵蓋應用生態、團隊規模選型、成本效益分析與畢業策略。適合 1-5 人技術團隊的 SA、DevOps、Backend 工程師。