The other problem is bandwidth. Even if you can run Kimi K3 locally, AI usage patterns tend to be very bursty. Having the scalability of cloud compute to run multiple agents and sessions at once makes it much more usable.
My current workflow is passing a human written spec to an agent to implement with strict coding guidelines, architectural decisions, etc. The agent isn’t making any decisions about the abstractions to use, it’s just creating the objects and test suites. So I don’t mind the slower bandwidth because I’m running the heavy agentic lifting over night with no need for human supervision.
But I fully appreciate that my workflow isn’t the norm. In fact my workflow it’s the exact opposite the AI grifters like Sam Altman are selling because it still involves a human with knowledge of the systems making different decisions.
If you’re running stuff overnight on a homelab, you can get away with using very large models as agents in CPU RAM.
Another option is a hybrid approach: farm “dumb” privacy-insensitive calls to an API you can subscribe to for like $40 a year, and let your local LLM do the orchestrating. This is what I do: I run MiMo locally, but also farm calls out to the exact same LLM in the cloud when appropriate… And I don’t mind one bit. I’m happy to tip them for open sourcing it in the first place.
The other problem is bandwidth. Even if you can run Kimi K3 locally, AI usage patterns tend to be very bursty. Having the scalability of cloud compute to run multiple agents and sessions at once makes it much more usable.
My current workflow is passing a human written spec to an agent to implement with strict coding guidelines, architectural decisions, etc. The agent isn’t making any decisions about the abstractions to use, it’s just creating the objects and test suites. So I don’t mind the slower bandwidth because I’m running the heavy agentic lifting over night with no need for human supervision.
But I fully appreciate that my workflow isn’t the norm. In fact my workflow it’s the exact opposite the AI grifters like Sam Altman are selling because it still involves a human with knowledge of the systems making different decisions.
If you’re running stuff overnight on a homelab, you can get away with using very large models as agents in CPU RAM.
Another option is a hybrid approach: farm “dumb” privacy-insensitive calls to an API you can subscribe to for like $40 a year, and let your local LLM do the orchestrating. This is what I do: I run MiMo locally, but also farm calls out to the exact same LLM in the cloud when appropriate… And I don’t mind one bit. I’m happy to tip them for open sourcing it in the first place.