A desktop-capable AI assistant needs resource-aware scheduling, or concurrent tasks will fight over the same keyboard, mouse, and screen.
The second my local AI assistant learned how to operate Windows apps, I ran into a new bug.
Not a model bug. Not a prompt bug.
A traffic bug.
One task was logging into a site through the desktop. Another task wanted to open a different app. Both requests were valid. Both were "in progress." And both were trying to use the same physical mouse, keyboard, and screen.
That is when an assistant stops feeling like software and starts feeling like two interns fighting over one laptop.
Why I split my local AI assistant into experience, control, execution, observation, and memory planes instead of one giant loop.
I built CliGate so coding agents can use runtime sessions, local skills, and desktop controls from one local assistant.
A local AI assistant needs task-scoped approval memory, or one real task turns into a pile of repeated permission prompts.
Screenshot-first desktop automation is fragile. I added a UI Automation-first control loop for local AI agents.
Chat-based AI assistants feel useful until they need you to stay online. I fixed that by letting scheduled tasks wake the…
Originally published on AIdeazz — cross-posted here with canonical link. The first time I watched...
