Thread

1. prometheus1992 · 1789830629 · focus · HN ↗

   I think the main gripe that people had with Jev and Typesafe was the language used when they launched. To me personally it seemed like a parody/con/shady at first.

   "Breakthrough", "our research went in another direction" , "Two years in stealth", "System One thinking model", "Jev can't hallucinate", "RLCD","We are doing very cool stuff, but we will have to hire you to tell you", - these are some of the things that they said on their website on the launch blog.

   I had used versions of bert to achieve the same functionality years ago. But to me it seems like they were able to trick the VCs with "can't hallucinate" etc.

   To the above author, kudos for sharing your work and making it open. Something like this shouldn't be closed in the first place when it has been available for so many years

   1. yojo · 1789842876 · focus · HN ↗

      Is this equivalent though? The Laya article ends with “ Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.”

      I have a dozen different things at work that are currently using LLMs as classifiers for different questions. I don’t have the time, data, or resources to fine tune a model for each of them.

      I haven’t had a chance to plug in Jev yet (waiting on approvals), but if it has the general intelligence claimed in the press release, then Laya is in no way comparable for my use case, and whatever TypeSafe has done is a substantial innovation over the Laya paper.

      1. dwa3592 · 1789843347 · focus · HN ↗

         hey, you might wanna try this? - <a href="https:&#x2F;&#x2F;github.com&#x2F;deepanwadhwa&#x2F;OpenDecision" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;deepanwadhwa&#x2F;OpenDecision

         it&#x27;s very similar to jev&#x27;s api and runs locally - if you like it, you can try jev for your actual usecases.

      2. vasco · 1789843364 · focus · HN ↗

         In a world of agents, doing a BERT run takes about 2 hours from having an empty folder. Just a thought you could consider. Once you&#x27;ve done the first you can do the rest of them before the end of the work day.

         1. adastra22 · 1789843538 · focus · HN ↗

            BERT run on what? You would need training data, no? The things would use Jev for have no training data. Not that kind of problem.

            1. hamandcheese · 1789856511 · focus · HN ↗

               Presumably, if you are positioned to plug in Jev (or an LLM classifier), then you are also positioned to collect training data.

               1. yojo · 1789862949 · focus · HN ↗

                  The domain is code analysis, all languages and frameworks. It’s b2b SaaS, so total volume is not incredibly high. And many customers have contract clauses that we don’t train on their data.

                  I’m not convinced we could train easily here, or that it’s worth the investment compared to (previously) spending fractional cents on Luna, or now paying even less on Jev. Especially given that these numbers are not meaningful to our margins.

      3. calebkaiser · 1789864297 · focus · HN ↗

         Jev seems pretty cool! I just got access and have only gotten to do minimal experiments, but I love this general area of research and it fills a very real need.

         I agree with you. I think the OPs pushback is emblematic of a larger reaction I&#x27;ve seen that is, at the very least, misinformed.

         There are a lot of approaches that use a self-attention backbone for classifier-style outputs. You have structured generation libraries like SGLang and Outlines, but those basically give you guided generation on an autoregressive model. You also have a bunch of models that are non-autoregressive that try something similar. Older NLP stuff applies here, and there&#x27;s newer stuff using diffusion transformers for this purpose.

         But I don&#x27;t think the Jev author has ever said that he&#x27;s the sole human, alone in a vast sea of misguided researchers, who is interested in schema-guided classification? I think he said he found a novel way to train a model for this task that has much higher general intelligence at much lower cost than other approaches. Which is an exciting result with lots of applications if it bears out.

         I think some people are just reflexively skeptical of anything that gets a lot of hype. Maybe that&#x27;s fair. Things that are wildly successful and high impact also tend to get a lot of hype though, so it seems like a poor filter.

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