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Embeddings

  • What Is an Embedding?
  • Meaning Becomes Geometry
  • Learning an Embedding Space
  • Similarity Is a Decision
  • Dimensions Do Not Mean What You Think
  • Neighborhoods and Hubs
  • How Many Dimensions Does Meaning Need?
  • Feature Space: What Does a Linear Model Actually See?
  • Hard Negatives
  • Retrieval Is a Policy
  • Calibration
  • Is Similarity One-Dimensional?
  • Change the Model, Change the Universe
  • Can One Embedding Space Be Translated Into Another?
  • Retrieval Is Not Geometry
  • What Should a Translation Preserve?
  • Can a Smaller Representation Preserve a Larger One?
  • From Deltas to Operators
  • Building an Embedding Runtime
  • Preference Rankers — Learning Which Answer Is Better
  • MR.Q — Building a Neural Quality Model From Two Embeddings
  • RELATE: Searching Embeddings by Relation, Not Just Similarity
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Technical library for the AI era.

Understand what is changing. Learn how it works. Use it.

© 2026 Ernan Hughes
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