Mar 7 17:54 🧪 add segment classifier, protocol processor test harness, and full chain tests - Part 4 blog: segment classifier synopsis with processor + chain test results
- Segment classifier: training script, eval harness, 50 labeled mixed dictations
- Protocol processor: updated with needs-llm detector, test harness (92 unit + 100 eval)
- Full chain test: 12 cases through classifier → model → reassembly
- Model weights: segment-classifier-model.json, needs-llm-model.json
- Eval data: 100 dictation-to-command pairs across 31 categories Mar 5 16:29 📝 add Part 3 outline: the 40-millisecond classifier gate Trained embedding classifier (logistic regression on NLEmbedding)
decides whether dictation needs LLM normalization. 100% accuracy
on held-out data, trained in 40ms on 120 examples using BLAS.
Covers: hand-crafted vs trained classifier, Accelerate/BLAS speedup,
fan-out architecture, and the routing gate concept. Mar 4 23:13 🧪 initial commit — voice-to-syntax training lab Processor, pipeline, eval data, training infrastructure, and blog drafts
from the voice dictation → programming syntax experiments.
Architecture: LLM normalizer → procedural processor (split approach).
Zero-training pipeline achieves 47% on mixed-difficulty eval (2x baseline). 
