> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbusbci.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Wrap any frozen encoder, personalize online with a Bayesian head, and ship apps on BrainState — the nimbus-bci product wedge.
# Personalizer overview
Nimbus middleware is the **product wedge**: a Bayesian personalization head for **frozen neural / foundation embeddings**. Encoders stay external. Apps consume thin `BrainState` outputs.
**One-liner:** anything that can `encode(X) → Z` plugs in; Nimbus owns personalization, uncertainty gating, and cheap online updates — not the trunk.
*One Personalizer API across trunks — from the Universal Personalizer manuscript (Paper 1b).*
Classical CSP / bandpower / Riemann features still work. Use `Personalizer(encoder=None)` or the standalone heads (`NimbusLDA`, …) documented under [Models](/model-specification).
## Start Here
`wrap` → Personalizer → save/load rules.
App-facing intents, presets, and bridges.
Cost bake-off, FT comparison, transfer map.
Fit a Personalizer in a few minutes.
## Minimal example
```python theme={null}
from nimbus_bci import Personalizer, wrap
enc = wrap(model.encode, model_id="partner-encoder", embedding_dim=64)
adapter = Personalizer(
encoder=enc,
head="lda",
classes=["left", "right"],
preset="research",
)
adapter.fit(X_cal, y_cal)
states = adapter.predict(X_test) # list[BrainState]
```
## Integrator checklist
1. **When to adapt** — factory works → little lift; gated stress → head adaptation; cost bake-off favors head before full FT. See [Evidence](/personalizer/evidence).
2. **Gate decisions** — `preset="research"` / `"consumer"`; inspect `BrainState.rejected`, `need_more_data`, `uncertainty`. See [BrainState](/personalizer/brain-state).
3. **Head stability** — after online updates, use `calibration_sufficient` on an unlabeled pool.
4. **Encoder swap** — freeze trunk; match `embedding_dim`; never pass logits as embeddings; keep `model_id` consistent across `save` / `load`. See [Encoder contract](/personalizer/encoder-contract).
## Next Read