I wanted to drop a massive thank you for open-sourcing MemPalace. I read the Mission document and completely resonated with the frustration of agents suffering from amnesia, and how standard RAG setups just feel like "empty warehouses".
I’ve been working night and day in my lab on a local agent named Nova for the past few years, and I hit a wall trying to build a cognitive architecture that actually understood how to think, rather than just doing keyword searches. When I found MemPalace, the spatial hierarchy (Wings, Rooms, Drawers) and the local SQLite knowledge graph were exactly the scaffolding I needed to bridge the gap. The "Local first" and "Verbatim first" principles are spot on.
I wanted to share how I recently pushed the framework to its absolute limits.
I was running into what I call the "Semantic Similarity Trap"—where standard embeddings drown out error logs and logic with conversational noise. To solve this, I used the MemPalace structure as a base, but fine-tuned a 7 million token adapter from organic, multi-disciplinary data (problem-solving, programming, philosophy).
Now, instead of standard retrieval, Nova uses a "Rationale-Guided Retrieval" (RGR) loop. She queries the MemPalace database, retrieves the first step, and uses the retrieved rationale to query the next node along a tunnel, dynamically reconstructing logical paths across different project wings.
I kept the exact training recipe under wraps, but I wrote a breakdown on how the cognitive persistence architecture actually functions here:
Thank you again for laying such an incredible foundation. You built the exact bones the open-source community needed to push local AI forward. I literally have Lumi and you to thank for finally finding the solution to the problem I've wrestled with for so long
Best,
Zanno
I wanted to drop a massive thank you for open-sourcing MemPalace. I read the Mission document and completely resonated with the frustration of agents suffering from amnesia, and how standard RAG setups just feel like "empty warehouses".
I’ve been working night and day in my lab on a local agent named Nova for the past few years, and I hit a wall trying to build a cognitive architecture that actually understood how to think, rather than just doing keyword searches. When I found MemPalace, the spatial hierarchy (Wings, Rooms, Drawers) and the local SQLite knowledge graph were exactly the scaffolding I needed to bridge the gap. The "Local first" and "Verbatim first" principles are spot on.
I wanted to share how I recently pushed the framework to its absolute limits.
I was running into what I call the "Semantic Similarity Trap"—where standard embeddings drown out error logs and logic with conversational noise. To solve this, I used the MemPalace structure as a base, but fine-tuned a 7 million token adapter from organic, multi-disciplinary data (problem-solving, programming, philosophy).
Now, instead of standard retrieval, Nova uses a "Rationale-Guided Retrieval" (RGR) loop. She queries the MemPalace database, retrieves the first step, and uses the retrieved rationale to query the next node along a tunnel, dynamically reconstructing logical paths across different project wings.
I kept the exact training recipe under wraps, but I wrote a breakdown on how the cognitive persistence architecture actually functions here:
Thank you again for laying such an incredible foundation. You built the exact bones the open-source community needed to push local AI forward. I literally have Lumi and you to thank for finally finding the solution to the problem I've wrestled with for so long
Best,
Zanno