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AI 编程可能加速战术执行,却掩盖架构与技术选型上的战略性错误。
One thing that scares me about coding with AI is that it's hard to feel your strategic mistakes I.e. I STILL remember mistakes from my early career, in ~2018: - using a complex form library instead of browser built-ins - using a downright dumb webpack + PHP combo These mistakes cost us valuable time. And when we rectified them, the tasks around them got faster. But often, we didn't even see them as mistakes! We just thought "this is the way we do it". We stuck to the tactical side of our job, and didn't consider high-level alternatives. Too many things to ship, too much pressure, too little interest in fixing it. I see the same attitude in agents. If you tell them to do a task, they will do that task. They aren't RL'd to think outside the box, or propose strategic alternatives. So the human needs to be on the lookout for these strategic opportunities. But how do you do it, if you don't feel the pain of working in them? I'm planning on building this into my /retro skill (still in-progress). That'll at least give the agent an opportunity to identify refactor opportunities. We'll see how it goes, but I'm still nervous about it.
是什么 这条推文讨论 AI 编程中的一个隐蔽风险:Agent 能高效完成被明确描述的任务,却未必会质疑任务背后的技术路径。作者用早期职业经历举例,指出复杂表单库、Webpack 与 PHP 的组合曾经拖慢开发,但团队当时把它们当成“做事方式”,直到改正后才发现整体任务明显变快。
核心洞察 Agent 擅长局部优化,人的价值则更多在于发现问题是否值得这样解决、是否存在更简单的替代方案,以及当前实现会不会制造长期维护成本。由于人类不一定亲自承受每次低效,AI 加速反而可能让错误方向更快落地。因此,回顾机制不应只检查任务是否完成,还应追问选型、边界、重复劳动和架构摩擦。
实践意义 将这些问题纳入 retro skill,是把“战略反思”变成固定流程的尝试。更有效的回顾可以让 Agent 对照原生能力、复杂度、重复代码和未来变更成本提出疑问,但最终判断仍需要人结合业务、团队能力与交付压力作出。
边界与风险 回顾机制可能产生泛化建议、事后合理化或无休止重构。它应输出可验证的替代方案与收益假设,而不是默认所有复杂实现都需要删除。