Maggie Appleton 写设计、人类学和智能体界面时,习惯先画清楚隐喻,再谈工具。把「Statistically, When Will My Baby Be Born?」整理成可阅读的中文笔记:问题在哪、界面默认会把人带去哪、落地时该改什么。原站导航、评论回响和广告已去掉。

问题怎么被看见

Babies aren’t very good at adhering to schedules. Especially unborn babies. Once you get to 38+ weeks pregnant, based off a very rough estimated “due date,” Due dates now seem a bit absurd to me after learning how much uncertainty there is in determining them. There’s uncertainty around ovulation timing, implantation time of the fertilised egg, accuracy of ultrasound measurements, and genetic variation between women. you are now in a limbo land of waiting.

I’m currently in that limbo waiting phase, and spending it reading all the stats I can find on when babies tend to be born. There are surprisingly few available datasets or papers on this, but the best I could find (and get access to) was this 2001 study that looked at when 1,514 pregnant women gave birth. The data was gathered over a ten year period from 1985 and 1995 in the UK, and only includes births that happened spontaneously (e.g. not including indu

默认界面在强化什么

Taking key metrics from that, I made a tiny tool that shows a probability distribution graph of spontaneous labour starting on each day of your pregnancy. The dataset only covers 37-43 weeks of pregnancy, so it can’t give you predictions earlier than that. I’ve found this helped ground my expectations of when babies normally tend to arrive, and how long I can wait until I need to start worrying about induction. You can enter your own due date, and whether

and this is my first child is not my first child You are — weeks and — days pregnant, and — . You have a — chance of giving birth by tomorrow.

可以怎么改

The daily birth probability is the chance of giving birth on that specific day. The cumulative birth probability is the chance of having given birth by that day.

This is based on statistical data extrapolated from the Smith 2001 paper, but don’t hold me to it too firmly. It’s only one study, I am not a data analyst, and it’s a fuzzy estimate at best.

落到产品里的动作

If you’re also waiting around for a baby to arrive, hopefully you’ll find this helpful and reassuring. The TLDR is it’s very normal for it to go over 40 weeks! Risks for all the bad stuff don’t significantly increase until the end of 41 weeks. This piece on due dates and induction from Evidence Based Birth has more helpful information.

落地时建议先做的 5 件事

  1. 先写清这个工具在强化思考还是在替人思考。
  2. 默认交互不要只会谄媚:该追问、该给反例、该要求证据。
  3. 智能体界面要露出推理步骤,而不是只给一个光滑答案。
  4. 知识发布用可生长的笔记,而不是一次性营销长文。
  5. 改产品前先画隐喻:用户以为自己在做什么,系统实际在做什么。

和智能体产品怎么接

龙虾PRO做 OpenClaw 落地时,最该从这类笔记里拿走的是「别把聊天框当唯一界面」。数字员工要能追问、能验证、能把过程摊开,而不是只负责说好话。

本文侧重全链路风控方法论。落地时请用自身业务单据做回放验证,不要把示例阈值直接当生产策略。 相关:风控体检 · 方案资源

常见问题 FAQ

什么是AI智能系统?

「AI智能系统」可概括为:Babies aren’t very good at adhering to schedules. Especially unborn babies. Once you get to 38+ weeks pregnant, based off a very rough estimated “due date,” Due dates now seem a bi 本文从定义、方法与实践要点展开说明。

为什么要关注AI智能系统?

关注AI智能系统,是因为它直接影响效率、风险与可复制性。文中指出:Babies aren’t very good at adhering to schedules. Especially unborn babies. Once you get to 38+ weeks pregnant, based off a very rough estimated “due date,” Due dates now seem a bit absurd to me after learning how much uncertainty there is in det…

如何落地AI智能系统?有哪些关键步骤?

建议按以下路径推进AI智能系统:1) 先写清这个工具在强化思考还是在替人思考。;2) 默认交互不要只会谄媚:该追问、该给反例、该要求证据。;3) 智能体界面要露出推理步骤,而不是只给一个光滑答案。;4) 知识发布用可生长的笔记,而不是一次性营销长文。;5) 改产品前先画隐喻:用户以为自己在做什么,系统实际在做什么。。细节见正文对应章节。

AI智能系统适合哪些人或团队?

AI智能系统更适合:产品/技术负责人、运营与增长团队、需要落地智能体或自动化的中小团队、关注「AI智能系统」方向的读者。若你只需要单次聊天式问答,可先读概念;若要上生产,请重点看步骤、权限与风控相关段落。

关于「问题怎么被看见」,本文给出了什么结论?

在「问题怎么被看见」部分,要点是:now seem a bit absurd to me after learning how much uncertainty there is in determining them. There’s uncertainty around ovulation timing, implantation time of the fertilised egg, accuracy of ultrasound measurements, and

关于「默认界面在强化什么」,本文给出了什么结论?

在「默认界面在强化什么」部分,要点是:of pregnancy, so it can’t give you predictions earlier than that. I’ve found this helped ground my expectations of when babies normally tend to arrive, and how long I can wait until I need to start worrying about inducti