A lot of use cases for agents require long running sessions. And you're likely to have many agent sessions running in parallel. These aren't going to be running on your personal devices

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brabel 5 hours ago | parent | next [-]

I agree. I currently do run a bunch of dev containers on my local Docker but that is not good enough, they neeed to be able to work while I am on the run and close my laptop. So we are trying having personal containers on a managed Kibernetes cluster for every developer. We’re still working on making this work but it seems promising, the hardest thing being just permissions to let the agent use MCP servers, http APIs etc. If anyone has done this, how did you manage that?? I know there are products like OpenCLI that help with the problem but we want something we can build ourselves since AI makes it so much easier to build things!

	▲	devrwoody 4 hours ago \| parent [-]
		Use ai to build it a poc and read the code after, learn, rebuild until you find something that works. I host all of my custom applications on a machine in my home with Tailscale, k8, infrastructure as code It’s wired, always on, has access to my gpu running on another machine or falls back to cloud inference Self hosted GitHub action runners on each different platform for build/deploy of various projects: tauri v2, apis, web uis, native macOS apps, Unity 3d None of this existed a month ago and it’s been a blast pretending to run my own platform

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ctolsen 4 hours ago | parent | prev | next [-]

Durability and parallelism aren't that hard, and you won't be needing frontier models for much of this stuff. The parameter efficiency of models is increasing rapidly. I don't see why a (near-)future device wouldn't be capable of running a perfectly capable personal assistant.

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spwa4 5 hours ago | parent | prev [-]

Why not? Why wouldn't your phone be able to do this?

The other advantage long running sessions have is that very low tok/s are perfectly acceptable. In fact, hardly noticeable.

(aside, of course, from Google and Apple both having incentives to prevent this from happening, as both depend on selling centralized services to you and advertisers, and the local model breaks both)