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. andy99 · 1789824513 · focus · HN ↗

         I have, LLMs are less fragile, that’s why I like them. The ability to generalize isn’t just about being general purpose, it’s super robust, and so assuming the budget is there (I agree they are inefficient) end up performing better on many classical tasks that have ood inputs. Before LLMs / foundation models we all struggled with generalization and at least in the work I was doing people were independently converging to using bigger more general models for tasks anyway as compute got cheaper. LLMs are just the most popular version of this.

         1. stefan_ · 1789827120 · focus · HN ↗

            That's part of the irony here I guess. In specialized fields, think computer vision, there were lots of teams whose innovative state of the art model was essentially just a function of the limitless compute they could throw at the problem. Now there are just people with even bigger sticks.

            There are lots of scenarios where specialized models still are the only option for real time, power efficiency, and so on. And transformers and other tech behind LLMs can equally produce better specialized models. But no sympathy for those who confused compute with innovation.

         2. tchalla · 1789834693 · focus · HN ↗

            > The ability to generalize isn’t just about being general purpose, it’s super robust

            I work with LLMs daily. 5 of my specialized tasks are outperformed by a custom model than a general purpose frontier model. The performance of my custom models not only beat them but are orders of magnitude low in costs and thus are able to be used by more customers.

            1. verdverm · 1789836185 · focus · HN ↗

               I suspect you and GP are talking at different layers. I think you are using robust on specific tasks with measurable confusion matrix. I think GP is talking about robust in more complex and diverse workflows, with the ability to self correct over turns.

               Either, please correct me if I'm misinterpreting

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