People keep asking me If I use Generative AI tools for coding and what I think of them, so this is my effort to put my thoughts in writing, so that I...
“Generative AI coding tools and agents do not work for me” is a thoughtful, experience-driven essay that critically examines the current wave of AI coding assistants. Written by veteran engineer Miguel Grinberg and featured on Hacker News, the article goes beyond hype to explain why many developers feel less productive, less in control, and more frustrated when trying to integrate generative AI into their daily workflow. Instead of offering yet another tool or framework, this piece functions as a reality check and a practical guide to thinking clearly about AI-assisted programming. It covers real-world issues such as inaccurate code suggestions, hidden maintenance costs, diminished understanding of codebases, and the risk of outsourcing too much design thinking to opaque models. Grinberg explains how these factors impact debugging, long-term code quality, and a developer’s ability to reason about complex systems. This resource is especially valuable for engineers, tech leads, and engineering managers who are evaluating whether, when, and how to adopt AI coding tools. It can help teams set realistic expectations, design better workflows, and avoid common productivity traps. If you’re feeling pressure to “just use AI” for coding but your experience doesn’t match the marketing promises, this article gives you a clear vocabulary and framework to articulate those concerns and make more grounded decisions about AI in software development.

工程管理者在評估是否在全團隊推廣 AI 編碼助手時,用本文梳理潛在風險與收益,作為決策依據。
資深開發者在和業務或管理層討論是否上 AI 工具時,藉助文章中的論點,避免只被宣傳話術左右。
個人程序員使用 AI 工具後發現效率不升反降,希望弄清原因並反思自己開發流程時,將本文作為參考。
技術負責人在制定團隊編碼規範時,思考如何有節制地使用 AI,同時守住代碼質量和理解深度。
高校教師或技術培訓講師在講授現代軟件工程實踐時,結合作品引導學生理性看待 AI 編碼助手。

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