{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreih2bylnoyzmmyldwfsvfczcg4sw2ljubd4k43m424imkhviwzoxoe",
    "uri": "at://did:plc:pgryn3ephfd2xgft23qokfzt/app.bsky.feed.post/3mjj5vhs5r2i2"
  },
  "path": "/t/i-developed-an-experimental-graph-native-artificial-brain-engine/175266#post_1",
  "publishedAt": "2026-04-15T03:58:21.000Z",
  "site": "https://discuss.huggingface.co",
  "textContent": "I am currently developing a small experimental system that explores a different approach to artificial intelligence.This prototype is built as a graph-based resonance architecture rather than a traditional transformer-based language model. It does not use decoders, matrix multiplications, vector embeddings, or attention mechanisms.Current status:Trained using only 5 Wikipedia articles\nReasoning is performed through resonance and gestalt proximity between meaning cells\nEvery response includes a transparent cognitive route showing the synaptic path taken\nNo black-box processing — all steps are visible and editable\n\nExample response (synthesis time: 157 ms, 5 synapses):“Physicist. In this context, the theory of relativity predicts that a sufficiently dense mass could bend spacetime, forming a black hole.”\nThe system is designed to learn in a pedagogical and ontological way — much like teaching a child — by building meaningful associations step by step.This is a very early-stage personal experiment.\n\nMy goal is to explore whether a more structured, transparent, and brain-inspired architecture can emerge from simple resonance mechanisms instead of large-scale statistical training.I would appreciate any thoughts or feedback from the community.",
  "title": "I developed an experimental Graph-Native Artificial Brain engine"
}