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Lesson 10: int8 quantization of the Stage C deciders, CPU latency and size, a stdlib server speaking pankhllm's decision shape, and a real run through pankhllm's own binary. The default int8 broke Qwen; the small decider got smaller and slower. And the contract, applied honestly: neither lane earns its place yet.
Lesson 09: a teacher model writes 420 realistic questions and labels them, 406 kept, for 49 cents; 183 join 20,000 templates. Three small models learn from them side by side: the from-scratch decider, BERT-mini with LoRA, and Qwen2.5-0.5B with LoRA. Coverage on teacher-written questions, before and after, with the precision that came with it.
Lesson 08: the tiny decider learns to write the whole request as JSON, operation and parameters, and a grammar built from the catalog constrains every token. Validity goes to 100%. The number that matters is the one a grammar cannot fix: answers that are valid and wrong.
Lesson 07: the Lesson 01 GPT, shrunk to 0.83M parameters, writes one token after a question: the operation that answers it. Beside it, pankhllm's hashed logistic regression. On phrasings neither saw, a threshold alone holds 98% precision for neither; with pankhllm's known-words guard, the transformer does, at 41.2% coverage.