Ryan O. ilinca LAVAL QUEBEC CANADA 1H AGO

I built Able (uable.store) because I was tired of chat tabs that explain how to do a job but leave me to paste the result into PowerShell. I wanted an agent that lives on this Windows PC and can actually move files, run tools, and write code here after I approve the actions. I work on Able, so this is the workflow I run on my own machine—not a theory. The job was to organize a Downloads folder that had become a dump and make a code change without affecting the live app. Step-by-step: 1. I open Able on Windows and describe the task conversationally: “organize my Downloads — keep installers, put photos together, leave anything I’m not sure about.” 2. I review the actions Able plans to take and let it run only after I approve them. Native file moves stay on this PC and do not use cloud credits. 3. For a code task, I enter `able job:` followed by the ticket. Able creates a separate Git worktree, writes a plan, and runs `cargo check` on that copy so the live app stays unchanged. 4. I keep secrets in the local vault, Septaguard. Cloud models—Claude, Gemini, Grok, and Kimi—receive the prompt I send, not the vault password. I enter Google and Gmail logins in the masked vault UI rather than in chat. 5. I remember that the job stops if the PC is off; there is no cloud-based substitute running in the background. 6. I verify the result on this machine: the folders are sorted, and the code check either passes or fails locally. If I only needed a sentence, I would have stayed in ChatGPT. The point of this loop is that the disk actually changes. What I learned is that “agent” is cheap until it has to touch the real folder and the real compiler. Chat is the easy half; confirm-then-act on Windows is the hard half. If you already use Claude or Cursor in a tab, what is the first job on your PC you still do by hand because the model cannot touch the files?