Original Summary

Billions over billions are burned in expensive GPU-processes designed to structure unstructured data and information.<p>I’m not a technical engineer; I’m a logic person.<p>And I like the library example, where billions of books lie on the floor, never structured on bookshelves, always there, but one above the other. Now to find the right book – a horrible searching scenario.<p>And I asked myself: why not rebuild everything from zero? First determine a structure, thus organizing the library with a clearly structured searching method, and then build a new wonderful library with beautiful bookshelves.<p>Possible.<p>Horrible work too – but possible.<p>I thought: it could be possible to start with the consumer market. The consumer is searching for a 4-star hotel and the structured library presents all 4-star hotels related to some consumer-made indicators.<p>I think many platforms already work that way – don’t they? But still one question disturbed me: if the consumer market works that easily (demand question – supply answer = fit), why then do we really need those expensive GPU processes (mostly implemented in hybrid systems – however very expensive and economically – bleeding)?<p>Why?<p>Because: the entire market is built upon fragments.<p>Fragments in consumer footprints and thus fragments in consumer understanding. Fragments in supply information and data and thus fragments in supply understanding. AI should fill the gap – with expensive statistical and probabilistic processes. What could be interesting? What did others do? What could be a fit?<p>Sorry – so much money for probabilism.<p>Bleeding again – from the perspective of an economist.<p>And this is why I build SEPPY.<p>The crux is: SEPPY instantly understands Demand without AI, Third-Party Data or historical Data. It puts Demand in the same numerical structure as Supply and makes it thus instantly comparable and matchable.<p>But numbers would not be relevant if they were not explainable. And the explanation must be comprehensive.<p>SEPPY is:<p>→ Harmonizing the market with demand and supply twins all of the same mathematical structure<p>→ Demand twins modeled from zero, in near real time, with few user inputs in that numerical structure – a CPU-native, deterministic, O(1) transformation process.<p>→ Supply in the same numerical structure put in the common numerical state space for instant comparison and a trustworthy match<p>→ The numerical twins instantly explained through properly architected, patent-pending ontologies<p>→ Demand Twins instantly decouplable from any real user input data – thus fully GDPR-by-design anonymous, yet hyper-personalized<p>→ Never overwritten – but growing and scaling with the SEPPY patent-pending approach<p>→ Thus, explainable development and evolvement and relationships of every singular twin<p>→ 31 billion twins can be modelled<p>This is SEPPY – built by a non-technical person to prove that mathematical comparison and explainability are the counterpart of GPU-expensive approaches.

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Hacker News 新项目
  • 发布时间：2026/10/20 16:15:12