Thread

1. Oras · 1789820747 · focus · HN ↗

   I played around with Jev last night and did it for classification tasks that I used Gemini 2.5 flash lite with.

   It’s a bit faster and bit cheaper, but this is compared to LLM. The consistency was nice to see, BUT, as someone who trained NLP models prior to LLMs, it’s just BERT with more data. I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough. And I believe many labs will replicate it in no time and might have it as part of their harness.

   I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.

   1. tchalla · 1789823087 · focus · HN ↗

      Anyone who has worked in ML for 10+ years would already know that the usage of LLMs for everything is lazy, wasteful and a high degree of marketing on it.

      1. ketzu · 1789830052 · focus · HN ↗

         I thought one core result that led to LLMs was the realization that a specialized model is not necessarily better at a task than a general one.

         1. jmalicki · 1789832021 · focus · HN ↗

            That goes all the way back to at least to Stein's Paradox in 1955, sadly too few people get educated about Statistics and keep thinking specialized models will necessarily be better. If you want to estimate the batting averages of 3 MLB baseball players from samples, you are better off building a model to predict all of their batting averages than computing the mean from a sample of each one separately.

            <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Stein%27s_example" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Stein%27s_example

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