A random mix from the whole library. Shuffle for a new set.
His meetings are recorded with Fireflies. On a set routine, the AI takes each transcript and posts a Slack recap to everyone who was in the meeting, with the action items and who owns each one. A second routine scans his meetings for tasks that fall to him and works out which ones the AI could do instead, then offers to run them in separate sessions.
He is letting the AI run his websites, with him watching over it. Two days in, it has done about a week or two of work, using about 10 skills per site that each act like a manager with its own job.
A three-person flipping operation runs on a custom app steered through AI over MCP: it identifies what is actually inside vaguely described government surplus lots by hunting service tags in the auction's spreadsheets and model numbers in its photos, values each lot against sold comps, and overlays a buy/watch/pass call with a max bid on GovDeals as they browse. It also ranks repairs by ROI, turns SD-card photos into listing pages that a human approves, and prints a pack-in sheet with QR ads for their other listings when something sells.
A man ran a local AI on his own machine that submitted almost 200 job applications over almost 5 days. His only part in the applications was clicking the captcha boxes, and running the work locally saved at least $400 in token costs at the AI rates.
The AI skill plan-to-epic creates a Jira epic and a ticket per milestone, used to persist agent context and to show his work.
Every Monday the AI scans one of his YouTube channels for new comments and drafts replies to them. He reads the drafts, gives the go ahead, and the AI posts them with its browser. Nothing goes up without his review.
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
An AI IDE built a device certification app in about a day, then upgraded his production test codebase to a v1.0-ready state with automated configs, error handling and simulators over two weeks.
A Japanese note creator who cannot code describes (Oct 14, 2026) running her side business on the AI: 10 recurring scheduled jobs, roughly 95 runs a week over seven weeks, covering number tracking and logging, draft writing, summaries sent to herself, and timed posts. Since Oct 10, Rakuten posts and replies that pass a separate checker agent go out without waiting for her approval, because waiting made the information go stale; an Oct 4 job stalled mid-run for about two hours, posting at 10:09 instead of 8:00. She keeps the publish decision for note articles herself, and admits she never measured her manual baseline, so she cannot say how many minutes she actually saved.
Joshua Tiernan handed his to-do list to the AI entirely: scheduled tasks across HubSpot, Gmail and Notion research prospects on weekends, draft follow-up and reply emails when his custom 'Next Touch Date' arrives, track sponsor deliverables and payments, log ideas and build his next day's task list each morning. Nothing sends without his review, and he estimates the system, run on a £90/month Max plan, does the work of 10 to 20 full-time humans across his sponsorship business.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.