专注 AI 知识库与 Agent 工作流。这里记录我真实的搭建、踩坑、纠错和进化过程。 Focused on AI knowledge bases and agent workflows. This is where I log the real building, the dead ends, the corrections, and the progress.
本科软件工程，会 Python。我把设计、内容和系统接在一起。 Software engineering degree. Python. I connect design, content, and systems.
从效果图反推物料，把「好看但不能生产」翻译成尺寸、路径、线宽、色差、节点和分层规则。 Reverse-engineering production assets from renders — turning "looks good but can't be produced" into sizes, paths, stroke widths, colour tolerances, nodes, and layer rules.
不靠空泛人设和漂亮口号，而是验证「为什么只有这个人、在这个条件下，能持续提供这项价值」。 No vague personas or slogans. I validate why this specific person, under these specific conditions, can keep delivering this specific value.
把重要判断、模板、SOP 和验收标准留在自己的系统里，而不是依赖某个模型。 Keeping the important judgments, templates, SOPs, and acceptance criteria inside my own system — not inside a model.
把学习、内容和真实项目接进同一套知识库，让复用不再依赖记忆。 Wiring learning, content, and real projects into one knowledge base so reuse no longer depends on memory.
把重复判断拆成可执行流程，让一组 AI 分工完成一件事。 Breaking repeated judgment into executable steps so a set of agents can divide up one job.
把验证过的方法固化成技能和管道，减少每次重新解释。 Hardening proven methods into skills and pipelines so nothing has to be re-explained.
让搭建过程本身成为内容，公开踩坑与纠错。 Making the build process itself the content — dead ends and corrections included.
更多内容整理中。More being organized.
把重复判断拆成可执行流程，让一组 AI 分工完成一件事。 Turn repeated judgment into executable steps and let a set of agents share one job.
从效果图到可生产文件——尺寸、路径、线宽、色差、分层全部落到规则。 From render to producible files — sizes, paths, stroke widths, colour, and layers all reduced to rules.
用留存和转化数据找出真正的问题，而不是先改内容。 Use retention and conversion data to find the real problem before rewriting anything.
也可以在这些地方找到我Or find me here