Summary
This tutorial demonstrates practical ways to use Jev, a new low-cost, low-latency decision-making AI model, by building three real applications inside the no-code platform Base 44. The creator shows how to build an emoji picker and a Netflix-style movie decider app, explaining how Jev’s near-zero output token cost and speed make features like real-time classification commercially viable in ways that weren’t practical with traditional LLMs.
The video also covers a more advanced use case: an ad-scanning tool that combines Jev with Gemini, using Gemini to convert images into text descriptions since Jev currently only processes text, then having Jev classify and score the results. Viewers learn how to connect Jev inside Base 44 either through built-in integration credits or via an OpenRouter API key, and how to switch models mid-build depending on the task.
For anyone experimenting with app builders or evaluating when a fast, cheap decision-making model like Jev makes sense versus a full language model, this video offers concrete, reproducible examples along with an honest discussion of Jev’s current limitation — namely, its lack of native multimodal support — and how to work around it using other AI tools in combination.
📺 Source: Jack Roberts · Published September 22, 2026
🏷️ Format: Tutorial Demo
