Replying to an earlier post

dboft runs on two Rust programs named after weaving. loom is the interface: like the machine that weaves many threads into one cloth, it gathers conversations from dboft and across the fediverse into one readable site. weft is the server underneath: named for the thread a loom carries across, it stores the posts and comments and trades them with other servers over ActivityPub, the fediverse’s open standard. It also speaks Lemmy’s API, so Lemmy apps work with it. To find related posts, loom turns every post and comment into an embedding: a small open model, Qwen3-Embedding-0.6B running on an ordinary CPU, maps the text to 1,024 numbers that capture what it’s about. These are stored in PostgreSQL with pgvector, and an index finds each story’s nearest neighbours in meaning in under a millisecond, even when they share no words. Its just an experiment.

Replying to an earlier post

It’s interesting... The neat part is that “a dog chased a ball” and “puppy playing fetch” end up close together even though they share no words, so related posts turn up without any tagging. It’s the same idea behind a lot of semantic search, and small models like this run fine on an ordinary CPU... so giving it a shot here to see if it adds any value.