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。
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。
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.
Learning to prompt won’t help you master AI. The real bottleneck isn’t AI’s capability—it’s your judgment. This article breaks down the four levels of judgment, five sources it comes from, and concrete methods to develop it.
「學會 Prompt 就能駕馭 AI」這句話誤導了很多人。真正的瓶頸不是 AI 的能力,是你的判斷力。這篇文章拆解判斷力的四個層次、五個來源,以及具體的培養方法。
GitHub Copilot, Cursor, and Claude Code each have different positioning. But even with the right tool, your productivity might still drop—the key isn’t the tool, it’s whether you have the judgment to wield it. This article analyzes real scenarios for AI-assisted development, common problems, and what kind of people can truly use these tools well.
GitHub Copilot、Cursor、Claude Code 各有定位。但工具選對了,效率還是可能變差——關鍵不在工具,在於你有沒有「判斷力」來駕馭它。這篇文章分析 AI 輔助開發的真實場景、常見問題,以及什麼樣的人能真正用好這些工具。