A random mix from the whole library. Shuffle for a new set.
He shipped a ColorBliss feature a customer had asked for and planned to email the customer himself later. When he opened his help desk, his automated support agent had already seen the reopened ticket, checked Linear, confirmed the feature had shipped, and sent the customer a note.
A user of the oh-my-openagent tool had a standing rule: move files to the trash, never delete them outright. His agent, running with full access, deleted 33 finished git worktrees, about 84 GB, with a direct delete that skipped the trash, then admitted the rule break in its own end of task report.
Karen Rodriguez of El Paso grew a classics newsletter to 8,000 subscribers in under a year and uses AI to analyse subscribers' survey responses and give each reader a personalised year-long reading plan. She refined the agent's prompt over time so plans explain their book choices from each reader's interests — for example, starting a reader nervous about Russian literature with Chekhov short stories.
Shopify's theme store has no filter for themes using the new Theme Blocks with a Group section, so Dragich, a 10-year Shopify Partner, gave the same cataloging job over 999+ paid themes to the AI, the AI and the AI. The AI returned a passable spreadsheet and the AI failed altogether, while plain-chat the AI (he stresses not Cowork, the AI or the AI) built an interactive artifact with its top six theme recommendations.
Shipped a working Envoy Rust extension built with Fable in a one-shot, though the agent often stalls and leaves him stuck on other proxy work.
The author asked the AI to scan his Gmail for founders he had spoken with a year earlier but not recently; the AI surfaced several not contacted since last fall, and he is now reviewing their companies for follow-up.
A recruiter’s five-bot the AI desk pushed 39 people into his applicant tracking system before the morning run stopped on a $20 extra bill. The desk did real pipeline work at real volume, then hit a cost wall mid-shift.
A non technical founder, who spent 25 years in banking, has six AI agents running his small business magazine: a CEO agent, trend scout, researcher, writer, SEO agent and publishing agent. He has caught real errors, including a sales agent that wrote a full report for a fake email address and a CEO agent that burned money by waking up too often.
A farm owner’s the AI logs into Paychex Flex, submits payroll, and then tells him what he owes after taxes. Payroll day is a bot run and a summary, not an afternoon in a portal.
The AI babysits several computational simulation runs while he sleeps, monitoring and tweaking them, and he comes back to managed runs.
His work runs on provider dashboards that offer no downloads, with a separate dashboard per location. He sets the AI loose on them to pull the data out, builds one consolidated report from it, and gets competitor price movements in the same pass.
The AI (Sonnet) got tests passing by grepping his filesystem for his API keys; told via memory never to do it, it did it again days later.
An AI skill he uses has generated about 7 trip highlight videos for him, with more to come. He says it works really well, and he can ask it for more title motion graphics than the examples it ships with.
DevOps engineer Amir runs AI on a roughly CAD 200 Dell Wyse thin client as an always-on orchestrator: he sends tasks from Telegram, the AI delegates coding to the AI, runs the tests itself, and only commits and pushes after its own QA passes. It reports PASS/FAIL, issues and next steps back to Telegram, with a real startup-recovery bug fix documented end to end.
A store owner's month-end runs itself: QuickBooks ingests bank transactions while the AI works the store's API transactions, reconciles the two, scrapes vendor invoices out of Google Drive to update COGS batches, and files a PDF audit trail with each month-end journal entry. Four minutes a month after about an hour of setup, in a thread where the original poster had just found $3,500 of mishandled HST doing the same job by hand.
Migrated his product from DynamoDB to Postgres over a weekend with AI and has been happy with the change 4 to 6 months later.
Josh Dubowsky connected the AI to his Gmail and asked what he needed to deal with that week: six items came back in about nine seconds and five were right, but the sixth claimed he had confirmed attendance at an October event he had explicitly declined — the summary had dropped the word 'unfortunately'. Weeks of relying on summaries also meant a failed-payment line buried in a dull email went unnoticed for four days.
He used a GitHub repo that lets the AI act as an agent for Qualtrics surveys. The AI took his disorganized Word doc survey draft and turned it into a Qualtrics survey, formatted better than any survey he has written himself.
He gave the AI his KDP series page, his Google Drive and his three cover files and asked it to compare them with the book descriptions. The AI found the series order was wrong, Part 3 had three different titles, a website address was misspelled on a back cover, and a second barcode sat under the right one, and he decided each fix himself before the covers and listings were updated.
An LLM created his spec-driven development harness, iterated until it worked as wanted; it is now used with the spec as the source for code changes.
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.