A random mix from the whole library. Shuffle for a new set.
Jacob runs four AI products alone. He built Tracetify, a tool that rebuilds how a competitor got its first users from twelve public data sources, in about three days, and gave the AI one instruction for the whole interface: you decide. Users later asked whether he had hired a designer.
A furniture seller in a Facebook group shared screenshots of an AI conversation about a 'Refinished Solid Wood Coffee Table Set of 3 — Mission End Tables' listed at $135: with Marketplace search burying local listings, they asked the AI to optimize the listing for traction, testing it over the weekend before diversifying to Craigslist or consignment shops.
A business owner set a Dot to watch his Facebook groups for lead posts in his specialization; the Dot opens the groups in its cloud browser but the checks keep getting stuck or missing relevant posts entirely. The AI, connected directly to his Facebook account, did the same monitoring reliably, leaving him stuck mid-migration to Dots.
He says his business got serious about Power BI reporting about twelve months ago, then three months ago threw it out and replaced it with AI licences. A third party warehouse tidies the data with an MCP on top, and the finance team now builds its own reports quickly instead of waiting on a reporting team.
Nokia built an ecosystem of specialized the AI Enterprise agents that automate troubleshooting. Issue resolution time has been cut by up to 80 percent.
Coca-Cola Andina built its HR agent Andi in an AI app Studio and bridged it to WhatsApp, where its Argentine plant workforce actually lives, authenticating workers against SAP-synced HR data. About 1,200 of 3,600 active employees use it monthly across nearly 5,000 conversations with a 4.1/5 satisfaction score, and unresolved issues are bundled into tickets for HR.
Keerthi Jeethuri, a Seattle-based designer and TEDx speaker (19.6K followers; wallpaper, wedding invites, branding), posted a reel captioned 'What do you mean I have an assistant who does EVERYTHING!!!! someone someone.' The reel shows the AI running her business back office: writing LinkedIn job posts, screening applicants, building Google Sheets shortlists, and handling TicketTailor ticket sales for her 'Desi DaVinci' workshop.
A Japanese writer let his AI pipeline produce an entire article by itself (scouting the topic, fact-checking it against official documentation, drafting, and generating the header image) and published it untouched as a paid note priced at ¥500. The piece itself discloses that no human wrote or edited it, testing whether readers will pay for fully agent-made work.
Diana at AI Agents Library gave the AI 500 loose files from her real business desktop. It proposed a folder plan first, she approved it, and it filed 490 where she wanted them, held 10 back for review instead of guessing, and caught all 48 exact duplicates and all 34 renamed ones.
His AI runs four standing jobs: each evening it checks new job posts in his niche and emails him only the ones worth his time; his messy end of day notes come back as tasks with due dates in his to do app; receipts he forwards get logged into a spreadsheet by amount, date and category; and on Sundays it tells him what slipped and what he keeps avoiding.
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.