HNSW
HNSW at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Discover how AI finds paintings by color scheme across 48,000 masterpieces”, “Explore cost-effective vector search with on-disk ANN indexes like DiskANN”, and “Scale Your AI Agents with Postgres's Native Multi-Modal Power”. Finding a painting by its color palette has always been a slow, computationally expensive task, until now. RAM is the ceiling that most vector search budgets hit first. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every HNSW story on Beyond Market Intelligence, newest first.
Discover how AI finds paintings by color scheme across 48,000 masterpieces
Finding a painting by its color palette has always been a slow, computationally expensive task, until now. Palette Atlas reduces 48,000 masterpieces to 128-color vectors and uses a clever embedding trick to make search practical. The sliced Earth Mover's Distance approach hits 78% overlap with exact results while running in under a millisecond. That's the kind of practical innovation that makes you wonder what other data tasks are waiting for a smarter approximation.
Explore cost-effective vector search with on-disk ANN indexes like DiskANN
RAM is the ceiling that most vector search budgets hit first. When that ceiling gets too expensive, the conversation shifts to disk. The real trade-offs between in-memory HNSW and on-disk options like SPANN and DiskANN are walked through, giving a practical lens on latency versus storage cost. It's a grounded look at architecting infrastructure that scales without breaking the bank. For a deeper dive into system-level choices, our piece on adaptive recommendation systems offers a useful parallel.
Scale Your AI Agents with Postgres's Native Multi-Modal Power
Postgres has quietly become the backbone for production AI, and Gwen Shapira's session makes a compelling case for why that matters. She shows how teams are moving beyond toy demos, using Postgres's JSONB parsing and high-recall HNSW indexing to ground LLMs in both deterministic and semantic context. The 4x speedup from vector quantization is a practical win, but the real insight is managing agentic memory without losing reliability. For those exploring distributed training challenges, our guide on distributed algorithms pairs well with this foundation.