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devttyeu • today at 10:09 PM • 2 replies • view on HN

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.


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solaire_oa • today at 10:21 PM

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.

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spaniard89277 • today at 10:12 PM

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

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