From late June into July 2026, I rebuilt the execution backbone of my development onto my own local large language models (LLMs). The trigger was cost.
The more you hand development over to AI, the more every task-routing decision and every actual code generation gets sent to a cloud AI, each and every time. Run it 24 hours a day without stopping, and that usage-based billing piles up every month, exactly in proportion to how much you used. And this is something you keep paying, month after month, forever.
But, I thought. If I buy the hardware once and shift the execution beyond that point onto my own local LLMs, couldn't I erase most of this ongoing cost? Couldn't I replace usage-based billing — which grows with every bit you use — with a one-time hardware cost?
Here is what I did. I moved the task-routing decisions (which I'll call the "orchestrator" in this article) and much of the hands-on work from the cloud AI to my own local LLMs. To do that, I bought one NVIDIA DGX Spark and combined it with the four Macs I already had to build an execution backbone that development tasks flow through. Leave the hands-on work to the AI, and keep only the decisions for the human. Push that division far enough and you arrive at Human-Out-Of-The-Loop (HOOTL), where the human steps outside the loop.
