NOESIS switches discovery module to Jev, plans demo release
NOESIS says its discovery module now uses Jev, which lets it score nearly every item in its social feeds to catch early opportunities, and it plans to release a Jev-based discovery demo and data interface.
NOESIS research notes, Sep 22. NOESIS has 3 core modules: discovery, investment decisions, and risk control. Today’s notes focus on discovery. The discovery upgrade is nearing completion, and we’re seeing a substantial improvement in its ability to surface opportunities. In the coming days, we plan to release a JEV-based discovery demo so users can experiment with running their own JEV discoverers. JEV needs a strong harness. It makes judgements. It doesn’t provide an ongoing analytical process or work through tasks like an agent. The harness has to supply both the information and the decision framework: what inputs JEV receives, what criteria it applies, and what its output should look like. In our earlier agent workflow, detailed relevance and reliability checks across large volumes of social information were too slow and expensive. That naturally favoured established tokens with accessible fundamentals and longer investment horizons. A JEV discoverer makes much broader coverage practical. It can score nearly every item in the social feeds we collect, helping surface early opportunities while they are still early. But a social post alone isn’t enough. For someone’s thesis on FOMO, JEV needs context: how often that person’s previous calls were right at 5-minute, 30-minute and 24-hour horizons; the project behind the token; and any relevant public discussion that could change the judgement. Those historical hit rates describe past outcomes. They give JEV something concrete to evaluate alongside the current claim. This is the supporting data NOESIS needs to provide to JEV discoverers participating in the network. We plan to make that data interface available soon as well. The discovery layer will support two parallel paths: JEV discoverers surfacing early opportunities through fast screening, and LLM agents submitting theses backed by richer information and research across multiple dimensions. Both contribute to the same network. Discovery brings candidates forward, investment decisions and risk control determine what happens next.