THE JEV GUIDE · CURATED EDITION
Launch news, explanations, demos, and community projects around TypeSafe Jev, organized by topic. Every entry links back to its X source.
Curated selection · Not exhaustive · Updated manually
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The post mentions a leaked JEV neural core, remarking that reading brain scans no longer requires waiting for a neuroradiologist’s report, and lists axial, sagittal, and coronal imaging planes, suggesting relevance to brain scan interpretation.
A developer shares a first draft of a skincare checker tool that quickly checks if skincare products are pregnancy-safe. It is built with SAM3, Gemini Flash, TypeSafe JEV, and Muse.
Thread listing 10 ways Jev-like models are already used in medicine, including in the operating room.
The author lost their voice due to illness and quickly built an app with TTS, frequent phrases, and AI/Jev corrections to communicate fast—showing Jev in a real-world use case.
The author announces a series of experiments with jev that have real business value, starting with radiology, highlighting the global shortage of radiologists and increasing scan volumes.
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In 669 synthetic clinical cases, Perplexity scored 96.1%, GPT-6 Luna 95.2%, and Jev 93.9%. These are preliminary results from Clinical System One, not clinical validation. Link
At the "What would jev Do" hackathon hosted by @adalagent, we worked on a runtime safety layer for AI companions. The proof of concept focuses on mental health support, where both signs of distress and inappropriate AI replies need careful handling.
Perplexity's new model pplx-decider v1.1 got 643 of 669 medical test calls right, outperforming the paid Jev model while costing about 43% less to run. It's free to download.
An online seminar by Igaku-Shoin introducing generative AI basics for healthcare workers, covering OpenAI's dots and TypeSafe AI's Jev.
pplx-decider v1.1 scored 643/669 on clinical decision tests, outperforming Jev's 628, with 42% lower cost per decision and similar speed. Weights are downloadable for on-premise hospital deployment.
Mr Vu shares a sneak peek of a cross-country medicine search powered by JEV for search planning and EmbeddingGemma 2 for embeddings.
This post shares cost and speed data for Jev and other models on clinical decision tasks, comparing total cost across 9 APIs and noting the fastest 3GB local model responds in 284 ms.
