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  "path": "/t/offline-autonomous-ai-engineer-phase-1-2-complete-local-llm-memory-eval-loop-architecture-inside/158142#post_2",
  "publishedAt": "2026-03-14T19:26:23.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "GitHub - remete618/widemem-ai: Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization · GitHub"
  ],
  "textContent": "Cool project. The offline-first approach is the right call for something like this.\n\nOne thing I’d think about early is how you handle memory conflicts as the task history grows. Once you have hundreds of retained facts, you’ll start getting contradictions (especially if the agent revises its own decisions). If you just append everything, retrieval quality degrades fast.\n\nWhat worked for me was batching new facts against related existing ones and letting the LLM decide per-fact whether to add, update, delete, or skip. One call instead of N, and the memory stays clean over time.\n\nAlso curious how you’re scoring relevance during retrieval. Pure vector similarity, or do you weight by recency/importance too? For an autonomous agent that runs long sessions, recency weighting makes a big difference since older task context can drown out recent decisions.\n\nBuilt a memory library focused on exactly these problems: GitHub - remete618/widemem-ai: Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization · GitHub – fully local with Ollama, might be useful as a component.",
  "title": "Offline Autonomous AI Engineer: Phase 1–2 Complete — Local LLM + Memory + Eval Loop (Architecture Inside)"
}