alt Hacker NewsI appreciate the effort you put into mapping semantics so language constructs can be incorporated into this. You’re probably already seeing that the amount of terminology, how those terms interact with each other, and the way you need to model it have ballooned into a fairly complex system.
The fundamental breakthrough with LLMs is that they handle semantic mapping for you and can (albeit non-deterministically) interpret the meaning and relationships between concepts with a pretty high degree of accuracy, in context.
It just makes me wonder if you could dramatically simplify the schema and data modeling by incorporating more of these learnings.
I have a simple experiment along these lines that’s especially relevant given the advent of one-million-token context windows, although I don’t consider it a scientifically backed or production-ready concept, just an exploration: https://github.com/tcdent/wvf
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.