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Ok, those are pretty decent examples, and clears up the utility a bit: speed and tokens. Some of it's still a bit iffy (e.g. `cutv 'email address' 'phone number' < examples/users.csv`, csv is already in columns), but I can see using it for some niche queries. Neat tool.

I very much appreciate your to-the-point, non-vibed README as well, ty for that.

by solaire_oa38 minutes ago|

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I still think that `churn_risk` above is incorrect and unacceptable (perhaps there are sensible fixes, but saying "no churn risk" about a refund, in a leading example on their homepage, flabbergasting).

But if that were solved, I could see giving ollaya/grev to LLMs themselves, giving LLMs their own massive token-saver.

by devttyeu44 minutes ago|

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Yeah, speed is the one, I believe the default TypeSafe API quota is 1.5-2k queries per second (batched in bigger requests).

On the readme I'm so sorry to tell you that, but it's 100% written by Opus 5.5 with zero "pretty please don't write slop" prompting, it's just how slop is going to look like from now on. I've been writing code for 15 years or sth like that and the code is also what I'd call pretty reasonable..

by solaire_oa31 minutes ago|

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It has some jargon hallmarks, which I noticed, but vibed or not, it's a massive improvement on other repos. Maybe it's because it's only a few commits so far... perhaps if you were to vibe 100 more commits it would devolve. Or maybe 5.5 really did improve (doubt it, still sounds like an asshole for me). But idk.

by spaniard892771 hours ago|

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Isn't it better to use an LLM to train modernbert or xgboost et al?

by devttyeu56 minutes ago|

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It is /possible/ to use an LLM.

But with Jev you're just paying for input (prefill) which is really fast, and in case of Jev specifically costs 50% of Deepseek V4.1 Flash (which has famously really cheap input token pricing).

I put 250MB / 1M lines of logs through Grev and it cost ~$10USD, DSv4.1 would be at least 10x that and much, much, much slower. With Jev/Grev that 1M requests took 10 mins

Edit: completely misread your question - yeah you could finetune specialized models to do that, probably based on some decent pretrained llm base, that is true for roughly any Jev-shaped problem. Do you want to bother doing that, also having to deal with having to host a zoo of specialized models?