Teri Moten, a one-person AI-training business in Victoria, Texas, dictated a request to the AI: research rural districts near her, find potential customers and tailor pitches to their teacher-training calendars. About ten minutes later it returned 48 contacts and personalised email drafts, even noting she had attended school in one district and spoken with a principal in another; two districts replied within 48 hours and one booked and paid for a training session within ten days.
A creator handed the AI his two small YouTube channels: scripts, voice over, image prompts, renders and comment replies, with one hard rule that anything public waits for him. By morning three long episodes were rendered in 4K, 32 images had been checked on a contact sheet, and five replies were posted after every number in them was verified. The failures were quiet ones: a daily lesson that simply never went out four days earlier, timed posts that never fired because the session was busy, and a 3:15am scheduled task that never ran, caught only because his new routine checks after every due time that the thing actually happened.
This only partly worked. Worth knowing before you try it.
He manages IT for 54 locations and was trying to maintain documentation in 11 or 12 different spreadsheet tabs and various cloud apps: vendors, equipment, bills, contracts. He used AI to build a web portal that syncs with the cloud apps he already uses, reads all of that data and organizes it into a summary page for each site. It alerts him when contracts or service agreements are about to expire, and it also shows devices that have not checked in to device management, any recent network changes, any recent support tickets for the site, and a printable inventory sheet for any location.
Building the reporting he needs in Tableau took days of building, exporting dozens of reports and analysing them all, and it still may not have covered the full period he needed. With an AI connector, the same job was done in 10 minutes.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
Peter Yang gave the AI Duet one short request and a link to his YouTube channel. Duet pulled a year of his published videos and their public data and built a report with charts, which found that personal AI agent videos are his breakout topic and that his solo videos now beat his guest videos unless the guest shows a real demo.
Three days into AI Duet, the time saved came from it acting first. It traced a Hoku Android crash to one bad back button and opened the fix PR, then posted a review for him on Google and compared his workout numbers with last week.