展示HN：预测性形而上学平台：数据科学与中国 astrology 相结合
你好，HN，我是*FateGuide*项目的产品经理和开发者（大量使用AI编码！）。我们的网站*suanmingzhun.com*旨在将稳健的*TypeScript (TSX)*和*数据科学*原理应用于*中国传统命理（八字/紫微斗数）*。
核心工程挑战是*精确性和一致性*。具体来说：
1. *时区问题（历书）：* 对于全球用户而言，将当地出生时间准确转换为真太阳时间（这对八字至关重要）是复杂的，因为历史上的夏令时变化和时区漂移。我们不得不构建一个专门的、经过严格测试的*TSX模块*来处理复杂的*历书*计算，确保四柱在全球范围内都是正确的。我很想听听其他工程师是如何解决类似的历史/地理空间数据挑战的。
2. *定性逻辑建模：* 如何将五行或紫微的十二宫等概念转化为定量数据结构？我们建立了一个系统，为元素的强度分配可测量的值，使得*AI算法*能够一致地解读*运势*和*职业*逻辑，避免了人类解读的主观性。
3. *未来：* 我们计划进一步开源我们*日历*和*八字*模块中非专有的部分。
我们非常欢迎对我们的TSX实现和数据建模方法的反馈！
（您可以在我们的GitHub项目上查看更详细的技术文档：[在此插入您的GitHub链接，例如，github.com/your-repo/fate-guide]）。
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Hi HN, I'm the PM and developer (using a lot of AI coding!) behind the *FateGuide* project. Our site, *suanmingzhun.com*, is an attempt to apply robust *TypeScript (TSX)* and *data science* principles to *Chinese Metaphysics (Bazi/Ziwei Doushu)*.<p>The core engineering challenge was *precision and consistency*. Specifically:<p>1. *The Time Zone Problem (The Almanac):* For global users, accurately converting local birth time to True Solar Time (essential for Bazi) is complex due to historical DST changes and time zone drift. We had to build a dedicated, heavily-tested *TSX module* to handle complex *Almanac* calculations, ensuring the four pillars are correct worldwide. I’d love to hear how other engineers have tackled similar historical/geospatial data challenges.<p>2. *Modeling Qualitative Logic:* How do you turn concepts like the Five Elements or Ziwei's 12 Palaces into quantitative data structures? We built a system to assign measurable values to elemental strengths, allowing the *AI algorithm* to interpret *Fortune* and *Career* logic consistently, avoiding the subjectivity of human interpretation.<p>3. *Future:* We aim to further open-source the non-proprietary parts of our *Calendar* and *Bazi* modules.<p>Feedback on our TSX implementation and data modeling approaches is highly appreciated!<p>(You can find a more detailed technical write-up on our GitHub project here: [Link to your GitHub URL here, e.g., github.com/your-repo/fate-guide]).
The core engineering challenge was *precision and consistency*. Specifically:<p>1. *The Time Zone Problem (The Almanac):* For global users, accurately converting local birth time to True Solar Time (essential for Bazi) is messy due to historical DST changes and time zone drift. We had to build a dedicated, heavily-tested *TSX module* to handle complex *Almanac* calculations, ensuring the four pillars are correct worldwide. I’d love to hear how other engineers have tackled similar historical/geospatial data challenges.
2. *Modeling Qualitative Logic:* How do you turn concepts like the Five Elements or Ziwei's 12 Palaces into quantitative data structures? We built a system to assign measurable values to elemental strengths, allowing the *AI algorithm* to interpret *Fortune* and *Career* logic consistently, avoiding the subjectivity of human interpretation.
3. *Future:* We aim to further open-source the non-proprietary parts of our *Calendar* and *Bazi* modules.<p>Feedback on our TSX implementation and data modeling approaches is highly appreciated!<p>(You can view the full project details and my technical write-up on GitHub [<a href="https://suanmingzhun.com/" rel="nofollow">https://suanmingzhun.com/</a>).