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.
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」。
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.”
85% of developers use AI coding tools, yet 43% of enterprises abandon AI projects due to ‘lack of technical maturity.’ The problem isn’t the tool—it’s that your codebase isn’t ready. Here’s a 5-level engineering foundation checklist.
85% 開發者在用 AI 工具,但 43% 企業因「技術成熟度不足」放棄 AI 專案。問題不在工具,而是 codebase 還沒準備好。5 層工程地基檢查,讓你知道團隊卡在哪一層。
As Claude Code, Copilot CLI, and other AI tools become part of our daily workflow, the criteria for evaluating terminals have fundamentally changed. This guide compares Ghostty, WezTerm, Tabby, Termius, and Royal TSX from an AI workflow perspective.
當 Claude Code、Copilot CLI 成為日常,終端機的評估標準也跟著改變。這篇指南從 AI 工作流的角度,深度比較 Ghostty、WezTerm、Tabby、Termius、Royal TSX 五款終端機,幫助你做出最適合的選擇。
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。