Thread

1. segmondy · 1790969354 · focus · HN ↗

   duh, this is not news. (general, fast and cheap) before decision models, you could pick only 2.

   LLM as judges - generalized, but too slow. If you had to make millions of classifications a day, this will be the wrong approach. you won't/shouldn't use LLM to classify spam/no spam. hot dog/or something.

   traditional classifiers, very specific 1 trick pony, super fast and cheap once built. If you need to make tons and tons of classifications, this would be the approach. but if you wanted a classifier right now for a novel problem, you need an expert to curate data, train and deploy.

   decision models/jev - are generic, you can throw them at most generic classification problems, and they are good enough. it's a fine balance between general, fast and cheap. you get all 3

   1. xfalcox · 1791195806 · focus · HN ↗

      Doesn't the article covers the speed part by showing that Qwen 3.6 35A3B has lower latency and same accuracy?

      1. jvanderbot · 1791203288 · focus · HN ↗

         Laya, a local Jev alternative, is a 400Million parameter model. It can play doom.

         Find me a another class of 0.4 B model that can handle structured output decision problems with the same latency and accuracy as Qwen 3.6.

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