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.
x.comJev opens to everyone查看專案 ↗
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 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.
x.comAnswers your code can use查看專案 ↗Four official signals. A fresh starting point on each visit.
7 項資源
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.
Source-linked curation
A guide explaining Jev's approach to making typed decisions rather than processing text.
Source-linked curation
A guide on using Jev to route between models and block risky tool calls.
Source-linked curation
Short practical intro with a Python ticket-triage example.
mohammedshehu.comWhat is Jev?查看專案 ↗Source-linked curation
A LangChain package with TypeSafeClassifier for integrating Jev into applications.
pypi.orglangchain-typesafe查看專案 ↗Source-linked curation
1,800-point thread debating whether typed decisions replace LLM calls for classification, routing, and scoring.
news.ycombinator.comHacker News launch thread查看專案 ↗Source-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%).
agentjournal.devOne judge call vs twelve dimension scores查看專案 ↗Source-linked curation
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4 HIGHLIGHTS
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