A notebook bolted to a frozen model
Hebb is a writable memory layer that sits on a model you already run. It learns after deployment. The model's weights never move.
How a write works
- Allocate. A cue is compared against every page key by cosine similarity. The best free page is claimed. No labels, no schema.
- Write. Gradient descent runs for roughly twenty passes with everything frozen except that one page's contents. The same mathematics as training, clamped to a few kilobytes.
- Read. The model cross-attends to the addressed page at every layer, for every token — not pasted into the prompt, wired into the forward pass.
What it is not
Not retrieval
RAG stores text and puts it in the prompt. A page holds learned values that need not correspond to any sentence, and never enters the context window.
Not fine-tuning
No weight in the base model changes. Nothing is merged, redeployed, or version-pinned to a training run.
Not a cache
A KV cache is discarded when generation ends. A page persists, and is still correct a hundred tasks later.
Where it fits
The case for it is write frequency. Adapters can hold per-tenant state, but updating that state means a retrain cycle. If a correction has to survive into the next request rather than the next release, a retrain cadence is not slow — it is the wrong shape.
Deployment
Self-hosted, in your own environment, on your own model. Requests never leave your perimeter, and the inference runs on hardware you already pay for. The API is four endpoints.
Status: research prototype, pre-customer. The addressing layer is the live constraint and is reported honestly on the benchmarks page. If you are evaluating this for production today, start there rather than here.