Thanks for the careful read — the "schema is ballooning" observation is real and I've felt it building this. You're pointing at a genuine design tension.

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.

Appreciate the thoughtful reply.

Absolutely agree the deterministic performance-oriented mindset is still essential for large workloads. Are you expecting that this supplements a traditional vector/semantic store or that it superceeds it?

My focus has absolutely been on relatively small corpii, and which is supported by forcing a subset of data to be included by design. There are intentionally no conventions for things like "we talked about how AI is transforming computing at 1AM" and instead it attempts to focus on "user believes AI is transforming computing", so hopefully there's less of the context poisoning that happens with current memory.

Haven't deployed WVF at any scale yet; just a casual experiment among many others.