问HN：人工智能助手是否正在削弱我们的编程技能？
在过去的12个月里，我将AI助手（如GitHub Copilot、Tabnine等）融入了我的日常工作流程。它们加快了模板代码的编写，建议一行代码的修复，甚至可以根据需求重构整个函数。
但我注意到了一些令人不安的现象：
* <i>浅层理解</i>：我有时会在没有完全理解的情况下接受建议。
* <i>解决问题的能力下降</i>：在面对困难问题时，我对独立找到解决方案的信心减弱。
* <i>新开发者的培训</i>：初级工程师依赖AI的输出，而不去质疑边缘情况，这导致了微妙的错误。
我想向社区提出几个问题：
* 自从采用AI工具以来，你是否经历了技能退化或责任感下降？
* 有哪些实践可以帮助你在利用AI速度的同时保持深入理解？
* 我们应该将AI助手视为“草稿生成器”，还是作为真正的编程伙伴？
我很想听听大家的轶事、策略或确凿的数据。让我们一起探讨如何使用这些强大的助手，而不至于成为它们的学徒。
查看原文
Over the last 12 months I’ve integrated AI copilots (GitHub Copilot, Tabnine, etc.) into my daily workflow. They speed up boilerplate, suggest one-line fixes, and even refactor entire functions on demand.<p>But I’ve noticed something unsettling:<p>* <i>Shallow Understanding</i>: I sometimes accept suggestions without fully understanding them.
* <i>Problem-Solving Rust</i>: On hard problems, I feel less confident in reaching a solution independently.
* <i>Onboarding New Devs</i>: Junior engineers rely on AI outputs without questioning edge cases, leading to subtle bugs.<p>Questions for the community:<p>* Have you experienced skill atrophy or decreased ownership since adopting AI tools?
* What practices help you preserve deep understanding while still leveraging AI speed?
* Should we treat AI copilots as “draft generators” or as true programming partners?<p>I’d love to hear anecdotes, strategies, or hard data. Let’s figure out how to use these powerful assistants without becoming their apprentices.