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  1. Cheating at search with jev

    Kicking the tires of jev for a very basic query understanding task

  2. What you should be thinking of as you consider search + retrieval roles

  3. My biased search expert perspective on where new AI teams run into expensive and unfortunate mistakes

  4. For vector search buyers, pay attention to how graphs get updated.

  5. A useful pattern for LLM classification at scale is to let it hallucinate plausible, fake entities.

  6. Sure its not fancy, but you might save operational headaches by just brute-forcing vector search for a small enough corpus

  7. A week of wector wevents - vector retrieval, hybrid search, combining the dense and lexical.

  8. Let's walk through basics of using PCA to reduce dimensionality of embeddings