Reproducible calibration and selective-risk benchmarks for Jev/TypeSafe decisions in DSPy workflows
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Reproducible calibration and selective-risk benchmarks for Jev/TypeSafe decisions in DSPy workflows
Jev returns a structured decision for the local program; consult the source for the exact decision policy.
Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.
Based on repository metadata and README with rule-based classification; pending human review, with no independent runtime or performance verification.
Jev is TypeSafe AI's System One decision model: it returns typed scores and choices (not free text), so agents can route, filter and gate at low latency and cost.
Source: jmanhype/jev-dspy-lab — 3 stars, MIT. Listed by Awesome Jev.
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