Ninetail-Fox Memory Engine cuts RAM below 60MB

Hot take: this is a practical local-memory architecture post, not a breakthrough compression paper, but the tradeoff is sane for agent memory retrieval.

• –The strongest point is the combination of quantized vectors and LRU eviction; that directly attacks the RAM bottleneck instead of pretending SQLite alone solves it.
• –The hybrid 70/30 vector + BM25 design is the right hedge against int8 ranking noise, especially when the goal is top-5 recall rather than perfect nearest-neighbor ordering.
• –The author’s correction on “TurboQuant” improves credibility; the actual implementation is much simpler than the brand name suggests, but it is also more transparent.
• –The compression claim is realistic after correction: the real win is roughly 4x storage reduction, not the earlier theoretical exaggeration.
• –The architecture is clearly optimized for local desktop agents, with the tradeoff that retrieval quality depends on hybrid ranking and cache hit behavior rather than pure vector precision.