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Show HN: verbatimeter - check how grounded your LLM / RAG agent is in real-time

https://pypi.org/project/verbatimeter/

I developed this tool and tried to keep it minimalist, lightweight, portable and easy to use. Hoping some people will find it useful. I think it's especially valuable for RAG agent but could be used more broadly as well.https://github.com/pierreolivierbonin/verbatimeter

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Re:Show HN: 18 Words on Saturday July 11, 2026 @08:49AM (#48871612)
Very cool game, well done!

Any plan to have multilingual support? Would love a French version.

Re:How do you use Vim in the era of AI? on Friday July 10, 2026 @08:59PM (#48867409)
I started using Neovim - AstroNvim specifically is pretty good - to keep my hands on the keyboard at all times, of course, but also to scroll through Claude Code's output in a terminal using vim motions, read the code it's outputting and the oftentimes overly verbose explanations it's giving.

Most of all, Neovim is helping me keep an eye on the code I develop at all times, and helping me keep writing code manually as well. It think it's especially important to be mindful about what we're doing these days because we're at risk of all becoming vibe coders as the AI assistants improve.

In essence, this is how: hjkl, G, gg, $, 0, w, b, e, i, y, p, <leader>e, <leader>o, <leader>ff, <leader>fw, /, dd, dw, grr, ]b, [b, :w, :q, :wq, etc... :)

Re:Show HN:Getting GLM 5.2 running on my slow computer on Friday July 10, 2026 @08:09PM (#48867002)
I must say, this is incredible, nice work! I've been tinkering with local LLMs for a while now and experimenting with different tech, and I'd never thought it'd be possible to run such a large model on consumer-grade compute. It still feels like these days, you need to buy a $10K DGX Spark or something crazy expensive just to get to that level of performance but with experiments like yours, it looks like there's still a lot of room for improvement and innovation.

What I'd like to see personally is Small Language Models giving similar performance eventually (because do we really need that many hundred billion parameters?). Researchers have shown that a lot of attention heads in the LLMs we're using these days are barely even active, so the MoE architecture seems like a step in the right direction. I still wonder how far we can push things back towards a 1B or 3B parameters model while trying to reach similar performances as double-digit or triple-digit B params models.