问HN：你们如何处理人工智能的维护？
正在进行我的第三个生产级人工智能部署。大家都在谈论“从用户反馈中学习的系统”，但在实际操作中，我看到的是：
- 用户纠正错误
- 错误被记录
- 工程师每周审查日志
- 工程师手动更新模型/提示
重复以上过程。这只是在“手动更新中增加了额外步骤”，并不是真正的循环反馈。
问题是：有没有人真正构建出一个完全自动化的学习循环，使得纠正 → 自动改进，而无需工程师介入？
还是说“自我改进的人工智能”仍然主要是营销噱头？
欢迎进行20分钟的电话交流，比较不同的方法。私信我。
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Working on my 3rd production AI deployment. Everyone talks about "systems that learn from user feedback" but in practice I'm seeing:<p>Users correct errors<p>Errors get logged<p>Engineers review logs weekly<p>Engineers manually update model/prompts -<p>Repeat This is just "manual updates with extra steps," not a real flywheel.<p>Question: Has anyone actually built a fully automated learning loop where corrections → automatic improvements without engineering?<p>Or is "self-improving AI" still mostly marketing?<p>Open to 20-min calls to compare approaches. DM me.