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Recent activityA FIELD GUIDE TO JEV / VOL. 01
The best of Jev. Real projects, practical guides and ideas from across the internet.
THE OFFICIAL SIGNAL 4 HIGHLIGHTS
Diogo Almeida@CompleteSkepticLAUNCH POSTDiogo Almeida introduces Jev, TypeSafe’s structured decision model. Watch the launch film and explore the original thread for the model’s approach, examples and author-reported performance.
TypeSafe AI@typesafeaiOFFICIALTypeSafe’s official launch announcement introduces the lab and points builders to Jev. It includes Diogo Almeida’s launch film and original introduction.
TypeSafe AI@typesafeaiOFFICIALTypeSafe announces public access to Jev without a waiting list. The official console is the starting point for trying typed decisions in your own application.
JEVLAB ARTTypeSafe AI@typesafeaiOFFICIALTypeSafe highlights Jev’s typed-output approach: applications provide state and questions, then act on probabilities, choices or scores. The linked announcement also covers the Venice API integration.
JEVLAB ARTStart with the Launch Post. Explore four official signals.
7 resources
AI-assisted summaries and translations. Check original sources for context and performance claims.
Jev is utilized for decision-making processes on Cloudflare, including support routing and risk management.
JEVLAB ART
EnglishSource-linked curation
A guide explaining Jev's approach to making typed decisions rather than processing text.
JEVLAB ARTEnglishSource-linked curation
A guide on using Jev to route between models and block risky tool calls.
JEVLAB ARTEnglishSource-linked curation
Short practical intro with a Python ticket-triage example.
JEVLAB ARTEnglishSource-linked curation
A LangChain package with TypeSafeClassifier for integrating Jev into applications.
JEVLAB ARTEnglishSource-linked curation
1,800-point thread debating whether typed decisions replace LLM calls for classification, routing, and scoring.
JEVLAB ARTEnglishSource-linked curation
One direct Jev question per row against 12–14 Jev-scored dimensions with locally fitted weights on three classification tasks: 5,477 test rows, 25,174 Jev calls, $1.43. Decomposition wins on Japanese NLI (0.9076 vs 0.8373) but flags about 25× more hard benign rows as attacks (37.2% vs 1.5%).
JEVLAB ARTEnglishSource-linked curation
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4 HIGHLIGHTS
Decisions & routing
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