MIT-licensed feed-forward classifiers used as advisory hints by Botte Secrète, a local-first control plane for AI coding agents. These are classifiers, not language models: they do not generate text and a prediction is never proof that a task succeeded.

Current status

The authoritative source currently wires 11 micro-NN predictors. Four have reproducible training or distillation paths and guards; seven remain observation-only until label provenance, temporal evaluation, calibration, drift detection, and rollback are complete.

The model-by-model status is maintained in the grounding roadmap. Run the audit instead of inferring maturity from the presence of a weight file:

bash

git clone https://github.com/zedarvates/botte-secrete.git
cd botte-secrete
python -m skills.auto_router.checkup_belt2
python -m skills.nn_audit.cli skills/botte_nn --json
python -m skills.botte_nn.cli list

Where it is used

Inference

Python inference reads the JSON weights directly. The optional Rust binary uses the same format.

bash

python -m skills.botte_nn.cli predict \
  skills/botte_nn/models/effort_classifier.json \
  --input 0.1 0.2 0.8 0.0

Feature order and normalization are part of each model's contract. Do not pass anonymous vectors copied from an unrelated task; use the named extractors in features.py.

Intended use and limits

Use the models for cheap local comparisons and shadow routing experiments. Low-confidence predictions must abstain or escalate. Do not use them as safety, legal, licensing, publishing, or deployment gates. Bundled fixtures test regressions; they do not prove production generalization or a universal token saving percentage.

Reproducibility and licence

Source, training code, feature contracts, tests, and issues: zedarvates/botte-secrete.

Released under the MIT License.

zedgamer/botte-nano-nn

Botte Nano-NN — tiny classifiers for agent routing

Current status

Where it is used

Inference

Intended use and limits

Reproducibility and licence

zedgamer/
botte-nano-nn