alt Hacker News> Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation.
The next iteration in LLM products is a 24/7 thinking loop where the claude-code like thing gets input continuously from your wearable, notifications, and newsfeeds and is constantly preparing things for you.
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This is is already possible with Claude Code. I use a setup where I have one instance monitoring a local queue, I have a web app for receiving webhooks from various sources and pushing them to the queue. Plus email for things that don't have webhooks. That instance then decides what to do with each input, sometimes it can spawn additional agent to investigate/prepare, sometimes it creates a ticket assigned to me and then waits for me input. All of that just uses the monitoring tools built into CC. The dispatcher loop doesn't need to be extremely smart, so I might experiment replacing it with a local model like this.
Maybe it's my lack of imagination, but what do you imagine you'd be doing where you'd want to keep a computer busy overnight?
It seems like the purpose of humans isn't to keep machines busy. When our phone or laptop is idle, it's fine if it sleeps. And when we do want something, we'd rather not wait.
(Also, this new model seems to be designed to keep latency down, which is useful for interactive tasks.)
Help convince Firefox of this: https://news.ycombinator.com/item?id=46294238 Rather than develop its own AI, Firefox should develop a system to pipe your html rendered browsing history in real time so external local services can process it: https://connect.mozilla.org/t5/ideas/archive-your-browser-hi.... Firefox could be the only browser that does this.
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.