My counter, qualified: deterministic consolidation is cheap and reproducible in a way LLM-in-the-loop consolidation isn't, at least today. Every think() invocation is free (cosine + entity matching + SQL). If I put an LLM in the loop the cost is O(N²) LLM calls per consolidation pass — for a 10k-memory database, that's thousands of dollars of inference per tick. So for v1 I'm trading off "better merge decisions" against "actually runs every 5 minutes without burning a budget."
On 1M-context-windows: I think they push the "vector DB break point" out but don't remove it. Context stuffing still has recall-precision problems at scale (lost-in-the-middle, attention dilution on unrelated facts), and 1M tokens ≠ unbounded memory. At 10M memories no context window saves you.
wvf is interesting — just read through. The "append everything, let the model retrieve" approach is the complement of what I'm doing: you lean fully into LLM semantics, I try to do the lookup deterministically. Probably both are right for different workloads. Yours wins when you have unbounded compute + a small corpus; mine wins when you have bounded compute + a large corpus that needs grooming.
Starring wvf now. Curious if you're seeing meaningful quality differences between your approach and traditional retrieval at scale.