VectorY is growing PIONEER-ALS from 12 to 36 participants to test VTx-002, an antibody therapy aimed at the protein TDP-43. #longevity# #geroscience# #vectory# #pioneerals# #vtx002#

Vector search that works at 10M vectors shouldn’t need a redesign at 10B. LanceDB hit 18.05ms p50 / 21.61ms p99 across 10B vectors, while scaling indexing and letting each query tune precision. New deep dive from @Yah01_

ditched hosted cloud vector APIs for local SQLite with sqlite-vec. lookups dropped to 4ms with zero remote network calls and no monthly cloud bill. There are no connection timeouts or silent context drops.

💾 Billion-scale vector search without keeping everything in memory. 📰 Title: HFresh: Memory-Efficient Vector Search 🔗 URL: Weaviate introduces HFresh, a new disk-based vector index built to handle billion-vector datasets on constrained memory budgets. Highlights 🧩 Partition-based architecture Instead of connecting every vector in one giant graph like HNSW, HFresh splits vectors into small regions called postings. An in-memory centroid HNSW narrows down the right region, then only the relevant postings are read from disk. 📦 Two-stage quantization Centroids use RQ8, cutting memory about 4x while preserving routing accuracy. Postings use RQ1, compressing up to 32x versus 32-bit floats to minimize disk I/O and storage cost. 🔄 Rebuild-free background maintenance Split, Merge, and Reassign, based on the LIRE protocol, keep the index fresh continuously with no full rebuilds required. On the 1M-vector DBpedia dataset, HFresh used just 239MB of heap versus 6.67GB for uncompressed HNSW, about 28x less. It has also been proven at 1 billion 256-dim vectors, making it a strong fit for memory-constrained, large-scale deployments. #VectorSearch# #Weaviate#

Dozens of vector quantization papers come out every year. When we surveyed them, we noticed that most quantizers are not new algorithms. They are the same handful of primitive operations, strung together in a different order. So we built VQ-bench: an open-source library of those primitives, composable into pipelines. E-RaBitQ, one of the strongest methods we tested, is four primitives in a list. Swap one and you have a new quantizer, evaluated exactly the way every other method is evaluated. We used it to benchmark 14 popular quantizers on recall, reconstruction error, and encode time. Two takeaways so far: PQ and OPQ have the lowest reconstruction error, and EDEN matches E-RaBitQ on recall while encoding much faster. This is a first iteration. We want your feedback, corrections, and contributions, and we will keep adding quantizers over time. Website: Blog: Repo: Paper (VecDB@VLDB 2026):

LangSmith for Startups Spotlight: Vector Legal Vector Legal is the premier AI-native law firm for startups, venture capital, and private equity. VectorOS, the one stop shop legal operating system, powers day-to-day legal work like formation, trademarks, contract management, company records, and e-signatures, while expert attorneys are on call when the stakes go up: fundraising, complex contract review, equity management, and bet-the-company matters. Trusted by companies including Corgi, Browser Use, and Sela AI, Vector Legal recently raised a seed round led by Base10 Partners with participation from Y Combinator. They use LangSmith for agent deployment, tracing, and fleet management across the AI agents powering their legal workflows. Building something big, or backing those who are? Vector Legal would love to help: