Building a Community Intelligence Tracker with Jev

If you are building at the cutting edge of AI models and infrastructure, you’ve probably come across TypeSafe’s new model, Jev. Instead of asking a model to generate more text, you can ask Jev to decide between explicit choices — what matters, how important it is, who should own it, and what should happen next. I wanted to try Jev on a problem where the output could directly change what a team does next. Community conversations felt like a good test: bugs, feature requests, support questions and interesting use cases are already there, but they arrive mixed into hundreds of ordinary messages. So, I built a small tracker that turns that stream into a queue of requests that needs attention: Start with the decision you needMy first instinct was to classify messages: bug, feature request, docs question, general chat. That was useful, but still left most of the work to a human. What I really wanted to know was: Does this need attention? Who should look at it? What should happen next? So…