awesome-jev (kraayenjon)
A curated list of Jev use cases, projects, SDKs, and resources. Jev is TypeSafe AI's System One model for fast, typed decisions in software — Choice, Score, and Noul with calibrated probabilities.
How it uses Jev
A curated list of Jev use cases, projects, SDKs, tools, and learning resources. Jev is the first System One model from TypeSafe AI — an AI model that returns typed decisions (Choice, Score, Noul) with calibrated probabilities instead of generated text.
Use cases
- Support inbox triage - Fan out intent, urgency, severity, and frustration questions in one call; act on the confident answers, route the rest.
- RAG passage filtering - Score relevance, contradiction, and injection risk per passage before the answering model sees it.
- LLM guardrails - Screen prompts, replies, and tool calls with hazard Nouls and a harm Score; policy passes, reviews, or blocks.
- Confidence-gated actions - Lower thresholds for reversible read-only actions, higher ones for risky operations, humans for the rest.
- Model routing - Let a fast typed decision choose between deterministic code, a cheap LLM, a frontier LLM, or a person.
- Structured extraction cascades - A small model extracts candidate fields; Jev verifies each value; only failures escalate to a reasoning model.
Getting started
Get an API key from the TypeSafe console, then:
Source: community catalog. Details and metrics reflect the published community snapshot and are not a performance endorsement. Documentation details extracted from GitHub · the README · the project website.