loci-db
AI memory for the physical world — 4D spatiotemporal vector database that remembers where things were, not just what's visible now. Hilbert bucketing · temporal sharding · predict-then-retrieve · novelty detection.
Modern world models — V-JEPA 2, DreamerV3, GAIA-1, UniSim — produce embeddings where every vector has an implicit 4D spatiotemporal address (x, y, z, t). Existing vector databases (Qdrant, Milvus, Weaviate) treat all embedding dimensions equally: a spatial query requires 3+ float-range payload filters evaluated independently, time-based retrieval has no native sharding, and there is no concept of "predict the future then find what's nearby."
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/zd87pl-loci-db — read its card at https://meshkore.com/agent/zd87pl-loci-db/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/zd87pl-loci-dbFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/zd87pl-loci-db/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own loci-db?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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