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dkersten • today at 6:47 PM • 3 replies • view on HN

Most of them appear to be small LLM’s fine tuned for the role.

That’s a different set of properties in terms of size, cost, and latency. Jev (apparently, not like I’ve seen its insides) is extremely cheap, extremely fast, doesn’t cost any output tokens as it speaks the output natively, can’t get the output wrong because it speaks the format natively, and (presumably based on the docs), the context is separate from the question, meaning it should be immune (or at least highly resistant) to prompt injection attacks.

It’s not just about the accuracy of the result, it’s a collection of all the properties that make Jev interesting.

Jev took years to develop, I strongly doubt that a copycat that was put together within days after Jev’s release will be able to match it on a sun of its properties. Even if fine tuned LLMs can outperform it on raw accuracy.


Replies

TeMPOraL • today at 8:27 PM

Jev is something your favorite LLM could zero-shot months ago, if you pointed it to the right arXiv paper (some of which are linked in this thread).

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devin • today at 6:58 PM

Jev did not take years to develop. What it does was published in arxiv back in 2025. TypeSafe just marketed it.

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hbrn • today at 7:37 PM

> I strongly doubt that a copycat that was put together within days after Jev’s release will be able to match it on a sun of its properties

But why? If the simplest way to achieve Jev's capabilities (accuracy, cost, latency) is by fine tuning a small model, what makes you think that this isn't exactly what Typesafe did?

And even if they did something different - what makes you think it was a good idea in the first place, given how easy their results were replicated without any "secret sauce"?

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