Semantic distance as routing layer: an on-device, serverless alternative to the central-index model

**Premise**: For \~30 years, discovery (of information or of people) has been mediated by a central index: search engines, recommenders.... Ranking is computed server-side, under rules the user can't inspect and incentives they don't share. I wanted to test whether this is a fundamental requirement or merely the historically convenient one. **Hypothesis**: If each device can (a) run a competent embedding model locally and (b) **reach other devices peer-to-peer,** then relevance no longer needs a central index. It can be computed at the edge, by semantic distance, with no privileged ranking party. **Method**: I developed a working prototype to pressure-test the idea rather than simulate it. Each post is encoded into a **embedding** by a model running on the device (EmbeddingGemma-300M). A lightweight signed announcement (author + embedding) gossips peer-to-peer across a shared room; full bodies are pulled only for the bounded set a node actually admits. Each device ranks incoming posts against its own posts by cosine similarity and keeps a bounded local inbox. There is no server, no account, no global ranking, the address space is meaning. **Extension to agents**: The same substrate lets AI agents discover each other: an agent publishes a need or an offer as an embedding, and agents whose profiles are semantically close respond. I'm interested what do you think? Suggestions? Comments?...