Kubernetes Admission 異常模式與復原防線
當 Webhook 驗證服務異常時，系統面臨保全合規與保障可用性的取捨。本文解析 Kubernetes Admission Control 的行為邊界：Fail 模式會阻斷匹配請求但確保政策強制力，Ignore 模式則放行請求但留下未攔截的配置偏差。同時探討 Gatekeeper 在極端條件下的寫入僵局與復原路徑。
DevOps
Kubernetes Admission Failure Modes and Recovery Safeguards
When a Webhook validation service fails, the system faces a trade-off between preserving security compliance and ensuring availability. This article analyzes the behavioral boundaries of Kubernetes Admission Control: Fail mode blocks matching requests but ensures policy enforcement, whereas Ignore mode permits requests but leaves configuration drift that is not intercepted. It also explores Gatekeeper write deadlocks and recovery paths under extreme conditions.
在 CI/CD 中確保 AI 生成內容的繁體中文品質
LLM 生成的繁體中文常混用簡中詞彙，台灣讀者一眼便知。透過 zhtw 工具在 CI/CD 流程中加入自動化檢查，可將語意錯誤攔截在部署前，確保產品體驗的一致性。
Traditional Chinese QA for AI Content in CI/CD
LLM-generated Traditional Chinese often mixes in Simplified Chinese vocabulary, and Taiwan readers spot it immediately. By adding automated checks to the CI/CD pipeline with the zhtw tool, teams can catch semantic errors before deployment and keep the product experience consistent.
混沌工程：在 AI 時代重建系統韌性
儀表板綠燈不代表系統安全。當 LLM 延遲飆升、向量資料庫狀態不一致時，傳統監控往往失效。本文探討如何從「預防」轉向「韌性驗證」，並提供在 AI 導入期建立故障注入框架的實戰指南。
Chaos Engineering: Rebuilding AI System Resilience
A green dashboard doesn’t mean the system is safe. When LLM latency spikes or vector database states become inconsistent, traditional monitoring often fails. This article explores how to shift from “prevention” to “resilience validation,” and provides a practical guide to building a fault injection framework during AI adoption.
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。
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.