what I would love to see along those lines is something that can answer "what packages do I have installed to do task $foo"; I keep installing things that I use for one thing and then forget about when I need to do the same task some months or years later.
Alpha3031
Very interesting project, I like it. Just wanted to clarify though the sentiment analysis is just the count of stripped words and used to tag things with the emoji? I was initially expecting it to be a part of the actual command construction process (even though I couldn't figure out how that would be relevant) given how it was listed.
publlus_enigma
This is a cool project; I think it's wonderful that traditional NLP methods are being used, rather than reaching straight for an LLM.
One significant advantage of not using a local LLM is the significantly simplified dependency stack.
If you could get Termy to code, you'd be a rich man
dmos62
It would make sense to have this integrate with a self-learning routine for an agent: e.g. at night it looks through what it did and writes NPC-Forge recipes. Tomorrow it can answer queries (which he turned added to NPC-Forge) without an LLM. Of course this implies a branching where a query is either processed by NPC-Forge or an LLM, depending on some measure of confidence that NPC-Forge can answer it well.
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gioscarab
Hi, I am the creator, feel free to ask any questions :)
What do you think about it?
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mbil
It's kind of antithetical to the tool's deterministic positioning, but have you considered making TERMy leverage an LLM for unseen or low-confidence queries, and then generate the config and update itself to make future similar queries deterministic?
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indigodaddy
So is this kind of like a super-powered tealdeer ?
what I would love to see along those lines is something that can answer "what packages do I have installed to do task $foo"; I keep installing things that I use for one thing and then forget about when I need to do the same task some months or years later.
Very interesting project, I like it. Just wanted to clarify though the sentiment analysis is just the count of stripped words and used to tag things with the emoji? I was initially expecting it to be a part of the actual command construction process (even though I couldn't figure out how that would be relevant) given how it was listed.
This is a cool project; I think it's wonderful that traditional NLP methods are being used, rather than reaching straight for an LLM.
One significant advantage of not using a local LLM is the significantly simplified dependency stack.
cf. https://github.com/TellinaTool/nl2bash
paper: https://arxiv.org/abs/1802.08979
If you could get Termy to code, you'd be a rich man
It would make sense to have this integrate with a self-learning routine for an agent: e.g. at night it looks through what it did and writes NPC-Forge recipes. Tomorrow it can answer queries (which he turned added to NPC-Forge) without an LLM. Of course this implies a branching where a query is either processed by NPC-Forge or an LLM, depending on some measure of confidence that NPC-Forge can answer it well.
Hi, I am the creator, feel free to ask any questions :)
What do you think about it?
It's kind of antithetical to the tool's deterministic positioning, but have you considered making TERMy leverage an LLM for unseen or low-confidence queries, and then generate the config and update itself to make future similar queries deterministic?
So is this kind of like a super-powered tealdeer ?