Hi HN, entityone here.
Short version of how this exists: I was pair-programming with an LLM and got tired of re-explaining the same context every morning. So I spent about ten days building it a memory. Atoms (named, versioned strings) hashed into a SHA-256 Merkle tree, an RFC 6962 consistency proof so I could verify the tree evolved honestly, and a Prediction-by-Partial-Matching model over the recall sequence so it pre-fetches the next atom before I ask. Sub-millisecond recall. I was very pleased with myself.
Then I hit the problem that is, in hindsight, extremely funny: I had built a memory system and had nothing to remember. A proof of honesty over an empty tree proves nothing. You cannot evaluate a memory substrate on a toy. It needs a real project with real bugs, real corrections, real state that changes under you and lies to you.
So I built one. The "test harness" turned into an actual SaaS — billing, provisioning, DigitalOcean orchestration, the works — written almost entirely with the same AI, using the ten-day memory engine as its long-term memory the entire time. Roughly eight months. The product became the experiment. The experiment became the product. I am aware this is backwards.
A few things I did not expect: corrections become durable (the first time I told it "ask, don't guess," it stored that as a procedure and it's loaded on every session since); bug shapes are retrievable (a three-hour crash-loop debug is now one atom it reaches for first); and the compounding is real but slow — marginal for a few weeks, then around 200–300 atoms it flips and sessions start with it already knowing what I'd have re-explained.
It's MCP-native (Claude, Cursor, Cline connect with no SDK), there's a REST API, and a 3D Merkle-tree visualiser because I wanted to watch the proofs.
Honest limitations: the 64% Markov next-atom hit rate is measured on my own sessions — yours will differ, and I'd like more external numbers. It's single-tenant by design (your own substrate), so no shared-corpus magic across customers. And the discipline is on the human: it only knows what you bother to make it remember.
Repo: github.com/wjm2202/Parametric-Memory. Happy to get torn apart in the comments — the Merkle and PPM internals are the fun part to argue about.