A random mix from the whole library. Shuffle for a new set.
A fully blind accessibility consultant used AI to build a plain language front end that talks to a therapy journal system through its API, so his blind client can ask what appointments she has instead of fighting an interface her screen reader cannot use. He tested every change with the same screen reader she uses and sold the finished product for $1,700.
Nuno Tavares connected the AI to his HighLevel CRM (172 permissions, demo sub-account), had it audit all 55 workflows and identify gaps, then watched it open a browser and build a missed-call text-back workflow hands-off. The build landed as a draft: missed-call trigger, tag and wait step.
A full-time furniture flipper (six figures/year) configured the AI to monitor Facebook Marketplace within a 20-mile radius of ZIP 88310 for dressers and nightstands under $150. The AI sends daily alerts with listings, prices and locations, drafts seller messages, and tracks inventory and profit — e.g.
A field service worker loaded the AI with every user and service guide for the products he repairs, plus all advisories and known issues. He runs each job's action plan through it: The AI checks the parts are on the original parts list, flags advisories, and builds a step by step PDF for the repair. On a product he barely knew, its diagnosis named a different component than the plan he was given. After six hours with his engineering team he followed the agent's call and it fixed the machine.
Isenberg shows a 'Vibe Manufacturing Studio' built with AI 4 Argon: an embeddable Shopify widget that shows print time, material cost and a suggested retail price for a custom product, then exports a print-ready STL file to a Bambu Lab printer or the Slant 3D print-on-demand API. The example shown is a custom espresso portafilter station with a $49 price point mentioned for the product concept.
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.
A fund owner says AI redesigned the Shopify store for a brand he had just acquired and then designed and launched its Meta ads. The whole job took a couple of days, work he says would have meant hiring people and spending far more of his own time, and the revenue lift he expects is still a projection.
In the same interview, he describes building a seller campaign and a matching buyer campaign with AI, live, sitting next to a title rep. The campaigns feed the intake stack that his AI follow-up then works.
Jay Kranda, the Innovative Tech Pastor at Saddleback Church, has the AI check the church's Rock RMS people-management database every Monday and Thursday at 5am. It reviews all 124 scheduled jobs in the system and reports back in plain English which ones are erroring, so problems surface before Sunday instead of during it.
Author educator Christopher Kokoski set up a test the AI with a series bible in Google Drive for a mystery series (hero Rudy Relish: seven hard rules including a bad left knee, never touches a gun, no ketchup on the cart). He added the first three chapters of book four and had the AI check each against the bible and all other pages, on a daily scheduled check. Chapters 1-2 were clean; in chapter 3 the AI caught every one of several mistakes he had planted, including one he considered an unfair plant. (Test manuscript on a fresh account, run for a week.)
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.