A Coding Guide to TypeSafe AI Jev
A runnable Python tutorial for Jev that moves from Choice, Score and Noul through confidence math, speculative fan-out, confidence-gated routing, composite scoring, typed function calls and production error handling.
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What Jev does here
Shows how to keep policy in ordinary code while Jev supplies bounded judgments, including risk-dependent confidence thresholds and separate atomic scores that can be reweighted without another inference call.
Includes a public notebook, pinned SDK setup, timing and token ledger, Pydantic response models, asyncio fan-out, retry policies and typed errors, making it suitable for hands-on reproduction rather than conceptual reading alone.
Article and notebook reviewed on 2026-09-24. Runtime outputs and savings depend on the reader's own API run; this directory does not treat the tutorial's example measurements as universal benchmarks.