job b4121971-28e4-4c9d-854c-f4b7cae6db60

2026-06-03 06:18:55 UTC · POST /memorize (async)

extracted facts 0 rows

Every ontological statement the LLM produced from the memorized text. Each one becomes a real donto_statement row in the substrate.

{
  "async": null,
  "extract": true,
  "facts": null,
  "holder": "agent:omega-bot",
  "images": [],
  "modality": "descriptive",
  "mode": "opencode",
  "passes": 1,
  "queue_id": null,
  "session_id": "discord:1349727923434815519:1349967527178145852",
  "text": "ajaxdavis in #resources: but then i'd argue with myself and say that if they did something like this for a while, with slightly worse results, but then post trained on the hydrated sessions in question, then it might actually work pretty welll"
}

{
  "aperture_yields": [],
  "dedup_collisions": 0,
  "elapsed_ms": 57,
  "episodic_record_id": "56254002-24e8-455f-b99f-316a115d3e53",
  "episodic_record_iri": "ctx:memory/episodic/59805e92-5227-42bd-bddb-1b2dee86baed",
  "extract_mode": "opencode",
  "extracted": true,
  "facts": [],
  "facts_extracted": 0,
  "facts_ingested": 0,
  "holder": "agent:omega-bot",
  "model": "upstream:opencode",
  "queue_id": "20cf1b23-3d5f-4e58-9d8f-11add1f83bfb",
  "semantic_record_ids": [],
  "session_id": "discord:1349727923434815519:1349967527178145852",
  "usage": null,
  "warnings": [
    "opencode mode: no facts supplied (agent yielded none); episodic stored only"
  ]
}