A random mix from the whole library. Shuffle for a new set.
He runs three products on his own with no marketing spend. Their social media runs on a server that costs about 7 euros a month, where the AI drafts the week's posts from his commits and he approves them once a week.
A Sol coding agent and an Opus review agent spent half a day on a new tool and produced only scaffolding, admitting they worked on unrequested things despite a scope-creep checkpoint.
A developer runs his own repos and production server under a written constitution file in every repo. Automated runs check production logs daily and file bug reports. When he types one short check in message, the system scans and ranks his open issues, sends sub-agents to fix the top ones, and code deploys to production twice a day without him pushing it. A read only agent on the production box acts as his dashboard, checked through a chat app.
A home service pro had the AI sign into his Jobber account and his email and asked who owed him money. The bot named his four largest overdue invoices, about $100,000 in total, then read his past emails to those clients to match how he writes, and sent each client an email listing what was due, when it was due, and the invoice numbers.
A host runs five the AI profiles as a small staff. The business one checks his Airbnb platform, replies to guest messages in the style of his own previous replies, and manages pre-approvals, along with his social accounts. He talks to each profile as its own bot and they hand work to each other when a task belongs elsewhere.
Employees at Swiss sportswear brand On launched no-code the AI Enterprise agents themselves. The agents handle knowledge extraction, executive reporting, research, scheduling and onboarding materials.
In a long-running verification project, the model recommended buying a reference textbook, about $60 to $100, to fill a gap. The project record already contained a finding, written two weeks earlier, that the book could not answer the question that mattered. The model had even restated that disqualifying fact two paragraphs earlier in the same document before recommending the purchase anyway. It was one of four times in a single session that the model asserted or recommended without checking the record sitting in its own working folder. The user had already spent $100 that day on reference material.
A SimplePost user hit an error while the owner was on vacation with his family. The AI checked the logs, found the issue, fixed it, and drafted the explanation email in Gmail, and the owner reviewed and approved each step.
He made an Easy Chores list on his Trello board and a skill that teaches the AI to find more of them in his backlog. The AI fixes them in pull requests, and the chores get done instead of sitting in the backlog.
His agent writes every sales call into the call log and the CRM, and before anyone on the team gets a number to call, it checks the log first. That stopped the team calling the same people twice.
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.