This post shows a clinical workflow where GLiNER finds metformin, DeBERTa catches the contradiction, Jev returns block_conflict, and the code stops the update for human review—three model families with distinct jobs in a fail-closed flow.
i finally turned yesterday’s bar joke into a clinical workflow 😂 GLiNER finds metformin. DeBERTa catches the contradiction. Jev returns `block_conflict`. code stops the update and sends it to human review. 3 model families. 3 different jobs. one fail-closed workflow.
During live clinical consultations, an ambient scribe periodically feeds transcripts to Jev, which traverses a medical ontology to classify symptoms, update the differential diagnosis, suggest treatments, and flag red flags in real time.
The author demonstrates how to triage a patient to the right doctor using only TypeSafe AI's Jev model and some question-asking code, noting that no LLM is used during the conversation.
A developer shares building a calorie tracking app with Jev, highlighting its instant experience.
