Alexander Doria @Dorialexander
— quoting Han Xiao (VP, AI @ Elastic)
Alexander Do... @Dorialexand... · Jan 23
wtf i finally get quality content on the corposlop network.
[Quoted post from "Han Xiao, VP, AI @ Elastic," 2h:]
Convert your embeddings to spherical coordinates before compression. This simple trick cuts embedding storage from 240 GB to 160 GB, and 25% better than the best lossless baseline.
Here's why it works: embeddings lie on a hypersphere, so d-1 angles can replace d Cartesian coordinates. In high dimensions, those angles concentrate around pi/2, causing IEEE 754 exponents to collapse to a single value. This makes the byte stream highly compressible.
Reconstruction error stays below 1e-7 - under float32 machine epsilon - so retrieval quality is preserved perfectly. Works across text, image, and multi-vector embeddings. No training, no codebooks.
Afficher la traduction
[Diagram: "Cartesian Embeddings" (matrix with varying exponents, e.g. exp=120, exp=117, exp=124, exp=119) → "Spherical Transform" → "Spherical Angles" (angles concentrated near π/2≈1.57, nearly all exponent=127) → "Compression Pipeline" (Transpose → Byte Shuffle → Zstd) → "Low entropy exponents → high compression"]
Note from Claude Sonnet 5
A technical tweet describing a lossless embedding-compression trick (spherical coordinate transform exploiting IEEE 754 float exponent structure) that cuts storage ~33% with negligible reconstruction error. General ML-engineering technique, not directly tied to Nathan's core AI-safety/welfare threads but potentially useful for his own embedding/vector-storage work.
embeddingscompressionmachine learning engineeringvector searchtwitter