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colingauvin | on: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows
I've been building this for the last 6 months or so. I've basically got it working. The model is not the issue, the infra is. Keeping everything in context just isn't possible and LLMs, even Fable, don't mode switch well. To get around this I've built a database software that ingests as much digital information as possible, and annotates it, then creates timelines with resolution gradients (longer ago = less resolution) that it feeds to the LLM on every request.

Then you have your cheap little MoE or ternary model just running in a loop, with an escalation pathway before it reaches the big expensive models.

Currently it's doing things like reminding me to take allergy medication when I wake up because it's checked AQI or whatever, reminding me to stop at the market when I'm on my way to pick up the kids to get the cherry tomatoes I forgot, giving me heads up of what folks are expecting from me in certain meetings based on cross correlating email and calendar, etc.

It's honestly the single most productive tool I've found for my ADHD.

docjay | parent | next
I’m curious why you don’t just use them like a Meeseeks box, rather than compressing and context stuffing into one. One only checks and categorizes your emails, another one for each category of email or even subcategory, one that only handles calendar additions, a different one to check it and notify you; you can go infinite with it. Hell, I’ll have one instance find a file and read it into the context of a different one because I don’t want a bunch of grep commands mucking up the context of the analysis. The find/read one exists for a few moments, as does the analysis one, and the ‘perform’ one is entirely different. I can run them all in parallel and use a queue if needed.

I’m sure you have reasons for your setup though, so I’m curious how you landed on it.

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nateb2022 | parent
Think of an LLM as a thesaurus, but for entire trains of thought rather than words. Your initial query yields something pertinent to the task at hand. But let it endlessly recurse and... you end up with something completely useless.

People would do well to acquire at least a modest familiarity with what an LLM actually is. NLP is fascinating. So is entropy.