Claude Code

Over the past year, the phrase “I wrote it with AI” has gone from “worth mentioning” to “weird if you didn’t” in pull request descriptions. Anthropic internally merged a single PR of 22,000 lines — largely produced by Claude, landing in their production reinforcement-learning codebase. When that story spread, most engineers didn’t react with awe; they reacted with anxiety: if they can pull that off, what’s stopping the wave of “PMs opening Claude Code and writing production code” from hitting our team?

You can’t stop it. But you can manage it. This post is the SDLC guideline I wrote for my own team after working through Erik Schluntz’s talk Vibe coding in prod | Code w/ Claude (Erik is a coding-agent researcher at Anthropic and co-author of Building Effective Agents). The whole thesis is one sentence: enjoy the AI speedup without sacrificing code quality, maintainability, system reliability, or security.

過去一年，「我用 AI 寫的」這句話在 Pull Request 描述裡出現的頻率，已經從「值得一提」變成「沒寫才奇怪」。Anthropic 內部曾合併過一個 22,000 行、由 Claude 大量產出、最後跑進正式環境強化學習程式碼庫的 PR——這件事傳出來的時候，多數工程師的第一反應不是讚嘆，而是焦慮：如果他們做得到，那我們團隊憑什麼擋得住「PM 直接開 Claude Code 寫 production code」的浪潮？

擋不住。但可以管好。這篇文章是我整理 Erik Schluntz（Anthropic 編程智能體研究員、《Building Effective Agents》共同作者）演講「Vibe coding in prod | Code w/ Claude」之後，為自己團隊寫的一份 SDLC 規範。核心命題只有一句：在享受 AI 加速的同時，不犧牲程式品質、可維護性、系統穩定性與資安。

As a developer who maintains multiple open-source projects on GitHub, my daily work goes far beyond writing code. I also deal with a constant stream of issues, PR reviews, releases, and other tasks that are tedious but critical. As projects grow, this workload quickly exceeds what one person can handle efficiently.

Over the past few months, I’ve integrated Claude Code and GitHub Copilot Review into my daily development workflow, and the results have been remarkable — tasks that used to take half a day now often get done in 1-2 hours. This post shares my complete workflow and explains why I believe the developer’s own technical ability is the key to using AI tools effectively.

身為一個在 GitHub 上長期維護多個開源專案的開發者，我每天面對的不只是寫程式，還有大量的 Issue 處理、PR Review、版本發佈等瑣碎但重要的工作。隨著專案規模成長，這些工作量已經遠超一個人能高效處理的範圍。

過去一段時間，我開始將 Claude Code 和 GitHub Copilot Review 整合進我的日常開發流程，結果讓我非常驚艷——原本需要半天的工作，現在經常在 1-2 小時內就能完成。這篇文章將分享我的完整流程，以及為什麼我認為開發者本身的技術能力，才是善用 AI 工具的關鍵。