🧵 I've just released Sentence Transformers v6.2.0! Featuring model merging with @arcee_ai mergekit, load models faster, & evaluators share work across GPUs automatically. Plus lower-memory losses, multi-vector improvements & plenty of fixes. Thread with highlights 👇

There are basically three ways to build a software factory right now: 1. Build: build your own factory infrastructure around coding agents 2. Buy: use a factory platform like Warp or Factory or Port to name a few 3. Delegate: skip the factory infrastructure and use an always-on agent like @bot or a dot as the factory The third is by far the easiest to set up and has many of the properties of a factory out of the box. It can coordinate work, update your ticketing system, ask for clarification, etc. But it’s missing a defining feature of software factories, a system you can measure, iterate on, and continuously improve.

Most vector databases don't support hyperbolic space out of the box. Turns out, they don’t need to. 💡 When you work with hierarchical data—like e-commerce catalogs, file systems, or taxonomy trees—flat Euclidean vectors run out of room quickly. A 5-dimensional hyperbolic embedding can outrank a 200-dimensional flat embedding for tree-structured retrieval. Why? 𝗙𝗹𝗮𝘁 𝘀𝗽𝗮𝗰𝗲 measures similarity. 𝗛𝘆𝗽𝗲𝗿𝗯𝗼𝗹𝗶𝗰 𝘀𝗽𝗮𝗰𝗲 measures similarity and specificity. Broad concepts (like "vehicle") sit near the center, while specific concepts ("a red car on a city street") sit higher up the curved surface. So, how do you search for hyperbolic vectors in Zilliz Cloud or Milvus without waiting for dedicated index metrics? 𝗙𝗹𝗶𝗽 𝗼𝗻𝗲 𝘀𝗶𝗴𝗻 𝗮𝘁 𝗾𝘂𝗲𝗿𝘆 𝘁𝗶𝗺𝗲. Because of how Lorentz geometry works, the hyperbolic inner product is just a regular inner product with the height coordinate subtracted instead of added. • Ingest your vectors as-is. • Multiply the query’s first number by -𝟭. • Run a standard Inner Product (IP) search. That’s it. You get exact hyperbolic ranking using the exact ANN indexes (HNSW, IVF, etc.) already built into Zilliz. Read the full breakdown by Simon Hearne on our engineering blog: 👇 milvus.io/blog/your-vector-d…

Your vector collection outgrew RAM. What now? Qdrant 1.19 lets you choose pinned, cached, or cold memory tiers per component. Keep original vectors on disk and compressed vectors in RAM to balance memory use and search latency. qdrant.tech/documentation/pr…

The OneLake Iceberg bench now keeps a chDB lessons log. Two worth copying: size the filesystem cache to the disk, not the data (too big is ENOSPC mid-query, not eviction), and cap max_memory_usage so a heavy query fails cleanly, not the runner. github.com/djouallah/lakehou…

Today we introduced a novel system one model, Clairic, based on regression instead of classification (i.e. decision models like Clef). This enables estimation of continuous values without having to exhaustively list choices upfront. It also enables very efficient extraction of structured data from unstructured inputs. Open weights coming soon!

Before reaching for embeddings, try giving your AI agent a good index. My LLM wiki uses Markdown indexes containing page titles and descriptions. The agent reads the index, chooses relevant pages, and follows links when it needs more information. No vector search required.