A local acoustic control system that learns user-defined labels for the surface or location of knocks, such as desk or wall. It also counts threshold-qualified knocks within each captured window. By default, a frozen Microsoft BEATs Iter3+ AS2M encoder creates 768-D audio embeddings and an incremental classifier learns the user's labels.
Turn any open model into a classifier/jev endpoint
A very fast document classifier/splitter using Jev
A flexible, adaptive classification system for dynamic text classification
Turn your documents into a Markdown wiki you can read, that answers with citations. Works with any LLM provider, Ollama included. CROW classifier routing: 89% right note vs 62%, 2.1× faster ingest, 68% fewer tokens.
Pretrained text classifiers you can run and retrain on CPU
RL Token pipeline for the SO-101 arm: π0.5 fine-tuning, RL Token training, critical-phase classifier, online RL and deployment.