Using Jev for bullish/bearish headline classification in trading
Using trading automation as the example, the author says Jev's approach of taking context plus fixed options and returning probabilities beats text generation for bullish/bearish calls, while questioning the zero-hallucination claim and calling for independent speed and cost tests.
I think we’ve been using the wrong kind of AI for some jobs. Why pay a model to write paragraphs when all I need is a decision? Take trading automation. If I want to know whether a headline is bullish or bearish, I don’t need an LLM to explain its reasoning in 500 words. I need a fast, reliable classification. That’s what makes Jev interesting to me. It takes context and a fixed set of possible answers, then returns probabilities instead of generating text. But I’m not buying the “zero hallucinations” claim without qualification. A model can still confidently pick the wrong answer from the options you give it. And the speed and cost advantages need independent testing. Still, I like the direction. Code for rules. Small decision models for fast judgments. Big LLMs when I actually need reasoning and language. Maybe the future isn’t one model doing everything. It’s using the right kind of intelligence for each job.