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I have a lot of semi-practical examples of how you can use this model wrapped in unix-ish tools - https://github.com/aurorainfra/grev (readme links to docs of each tool with some more or less practical examples)

Really I think "smart grep" is a pretty good one ('look for an error looking vaguely like this'). Also I think sql-based shell history + decision model is quite good to make the last 'which one of those choices is best fit given users past few commands' etc.

by solaire_oa14 minutes ago|

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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_oa28 seconds 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).

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

by devttyeu6 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 was writing code for 15 years or sth like that and the code is also what I'd call pretty reasonable..

by spaniard8927723 minutes ago|

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

by devttyeu18 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?

by motoboi23 minutes ago|

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Is for when you want an AI to make a decision. If you have been using gpt or claude or open source models for that, than it’s a way cheaper alternative.

And if you have not been, it’s for when you have to extract the context from text. When you have numbers or fixed options, it’s just a matter of code.

So if you find yourself having to decide if a given user comment is a refund_request, that’s for that.

It’s not perfect, you still have to fine-tune (or calibrate) using examples you have (and keep those examples updated over time). But it’s way better than trying to parse text with regexes.

by itsalwaysgood3 minutes ago|

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It's about what goes into, and out of, the decision, and response time. With current AI services, they're very chatty because they're thinking in language and input and output is language.

But what if you just want the branches in those decisions instead? Do this, or Do that. Go here, go there, move this, move that, etc. You don't need all the language, you just need the reaction or the branch of the decision. It potentially reduces your response time.

Think of standing and balancing: there are a lot of decisions that fire and go to the muscles to keep you upright.

by 21 minutes ago|

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