Classifying 80k emails with Jev before handing off to an LLM
The author argues Jev is a classifier rather than an LLM, outputting probabilities, scores, legends and confidence, and used it to classify over 80k emails before letting an LLM sort and clean them 100x more efficiently.
Unpopular take: it's a skill issue. Most people posting are vibe coders who can't tell the difference from an autoregressive large language model and a cat. JEV is neither, is a classifier, and should be treated as such. It's built to give you probabilities, score, legend and confidence. I had JEV classify all my emails (+80k) and now I'm using that data together with my LLM to make the sorting, and cleaning 100x more efficient. I do agree with Ole to some degree, that there are a lot of grifters, who seemingly just jump on the next hype train, shill whatever they think they have solved. But loads of the examples that have been floating around have extremely interesting, and when other try to replicate or use, they have no clue HOW to use JEV. The all show up thinking its an LLM, the expect it to work as an LLM because thats what they have gotten used to. So my take is that JEV helps sifting out the grifters from the actual builders. Below I'll add some actual real good examples of JEV in action. Most of it, above my paygrade.
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