Jev as an agent safety monitor

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%).

Guillermo Rauch: Jev reviews every fx command, faster and more accurate than a chat model.

Two decision channels on ViZDoom, navigation at 5 Hz and combat at 12 Hz, with an 18-kill test run.

Typed decisions with TypeSafe's Jev, the first System One model

The most complete gallery of what people build with Jev, TypeSafe's System One model: 3,400+ projects, demos, and write-ups by scenario, each with its original link, image, and description.

📡 全网最全 · The world's most comprehensive tracker of the Jev (TypeSafe AI System One) ecosystem — 220+ documented cases · 108 confidence-graded entries · verified & rescanned every 3 hours · API access guide included

Reproducible recipes: parallel questions, reranking, guardrails, citation checks, extraction, hierarchical classification.

Company homepage, waitlist, and product overview.

Introduction, primitives, patterns, cookbooks, HTTP API, and SDK references.