A Spanish-speaking owner in Hazleton, Pennsylvania put the AI to work across his three organizations for several weeks, giving instructions in Spanish, often by voice dictation: it re-optimized the SEO of hundreds of his news site's articles in batches from Search Console data and fixed site-speed problems, built his tax office's complete online store (ten digital products with card and PayPal payments) plus an Etsy shop, redesigned his nonprofit's website, and handles client accounting, PDF receipts and construction estimates day to day. Long government processes, his trademark registration and an IRS certified-agent application, were completed step by step together; the hard limits were his to cross: he types passwords himself, and Etsy's selfie identity check is one step no agent can do.
Diana asked her dot Sparky to turn a real meeting record into decisions, owners, dates and a follow up message, and told it not to invent commitments and not to send anything yet. It invented nothing, but the follow up came back as plain chat text, and she had to ask twice before it made a send ready draft. When she told it to send, it sent at once.
This only partly worked. Worth knowing before you try it.
He ran AI on the task with 6 agents at a time, about 75 agents in total over about 6 days. They produced transcriptions of tens of millions of handwritten images and indexed them all.
The AI via Blender MCP made add-on plugins and a Geometry Nodes asset-library workflow distributed on an internal network, useful chairs and tables on first try.
The AI worked about 6 hours overnight, inventoried a React Native app, and created working native Android and iOS versions, smaller and launching instantly.
A teacher asked the AI to make a presentation from her pacing calendar. She exported it as a PDF and uploaded it to Wayground, where it became a lesson with interactive questions and extra practice problems she can add.
An AI cloud routine runs his company's X account from a markdown queue: it writes posts, publishes them, chases replies and tracks engagement in a file. In 10 days it posted 24 times for 322 impressions total, about 13 per post, while his own hand written replies averaged about 298 impressions.
This only partly worked. Worth knowing before you try it.
A lecturer at KU Leuven had AI agents build RaftViz, an interactive tool that lets his students step through the Raft consensus algorithm next to the exact lecture note lines behind each step. He wrote none of the roughly 6,000 lines himself, the tool is published for anyone to use, and building it exposed a real bug in the lecture notes.
Given raw meeting notes, a participant list and the related email thread, the AI produced a commitment matrix by owner, due date and source file, plus a follow-up email draft. Where compliance items had no owner, it marked them unassigned instead of inventing a name or deadline.