A developer shares building a calorie tracking app with Jev, highlighting its instant experience.
i made a calorie tracking app with jev. it's INSTANT 🤯
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.
The author shares moving the decision step of healthcare intake triage from an LLM to Jev (@typesafeai): 25 typed questions, 0.29s, $0.00015, with code walking the workflow, plus method, numbers, and limits.
