從 Context 流失到自我修復 CI：Anthropic 的工程實戰觀察
Anthropic 工程師在臺北 Meetup 分享：如何透過 hooks 與自動化機制，把 CI 的回饋迴路產品化，減少等待與人工重工。
DevOps
From Context Loss to Self-Healing CI: Observations from Anthropic’s Engineering Practice
Anthropic engineers shared at a Taipei Meetup how hooks and automation can productize CI’s feedback loop, reducing waiting and manual rework.
DevOps 沒有取代 SDLC，AI 也不會
PR merge 速度翻倍，rollback 次數也跟著翻倍。AI 加速的是寫程式碼那一段,但 review 與測試的反饋網沒跟著加密。本文拆解 SDLC、DevOps、CI/CD 三層架構,看 AI 該被擺進哪一層。
DevOps Didn’t Replace SDLC. Neither Will AI.
PR merge count doubled — and so did production rollbacks. After adopting AI tools, one team watched weekly merges climb from 32 to 71, while monthly rollbacks jumped from 2 to 5. Every rolled-back PR had passed CI. AI accelerated the coding part, but the feedback net of review and testing didn’t become denser to match. This post breaks down the three-layer architecture of SDLC, DevOps, and CI/CD, and looks at which layer AI should be placed in.
CI Broke Again Over a Lint Error—Can AI Just Fix It?
Most CI failures are lint errors, typos, and formatting issues—anyone can fix them, but each round costs 10 minutes of waiting. Anthropic’s internal YOLO Push concept lets Claude auto-fix these mechanical failures, with a complete GitHub Action YAML example and safety boundary design.
CI 失敗→複製錯誤→手動修→再等 10 分鐘——這個循環可以自動化嗎？
CI 失敗最常見的原因是 lint error、typo、格式問題——任何人都能修，卻要等 10 分鐘。Anthropic 內部的 YOLO Push 概念讓 Claude 自動修復這類機械性失敗，含官方 GitHub Action YAML 範例和安全邊界設計。
2025 AI Development Tools Landscape: From Coding to Presentations, What’s Worth Your Investment?
84% of developers use AI tools, but research shows actual efficiency may drop by 19%. A complete comparison of the 2025 AI development tool ecosystem: Claude Code, Cursor, Windsurf, Figma AI, v0.dev, and Gamma—helping you decide what’s worth investing in.
2025 AI 開發工具全景圖：從寫程式到做簡報，哪些值得投資？
84% 開發者使用 AI 工具，但研究顯示實際效率可能下降 19%。完整比較 2025 年 AI 開發工具生態：Claude Code、Cursor、Windsurf、Figma AI、v0.dev、Gamma，幫你判斷哪些值得投資。
5 Pitfalls You’ll Hit in Your First Month Building RAG
Assigned to build a RAG PoC? This is not about choosing vector DBs. It is about the 5 pitfalls you will definitely hit: chunking strategy, data quality, success metrics, maintenance costs, and user expectations. Lessons paid for in production outages.
RAG 專案的第一個月，你會踩的 5 個坑
被指派做 RAG PoC？這篇講的不是怎麼選 vector DB，而是你第一個月一定會踩的 5 個坑：chunking 策略、資料品質、成功定義、維運成本、用戶期望。都是花錢買來的教訓。