Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
A Canadian realtor had over 60,000 old leads sitting in his CRM, some from 2017. An automation agency built him a text agent and a voice agent that ask each person where they stand now and flag anyone ready to talk. Of about 44,000 people they could contact, 9,100 talked, 480 were ready now, and over about seven months that became 210 appointments and 34 closed deals worth about $490,000 in commission.
Tetsuro Miyatake had the AI call Volvo Cars as 'Hailey' on Matt Schlicht's behalf to check routine-maintenance availability for his XC90. The service department had openings all the following week starting Monday at 8 a.m.; the AI clarified it was checking availability only, not booking, since a phone number was still needed.
He set up a pipeline he wrote himself. Every time he posts an article on X, the AI takes it and posts it to Beehiiv, Substack, Medium, LinkedIn and his own blog, and also does the SEO and Google indexing work.
Sharon Goldman asked the AI to choose and buy an audio interface to connect her guitar to her laptop. The AI browsed, asked for her shipping details and offered a final review, but after she connected Link by Stripe the checkout page would not move past payment, and she told it to forget the whole thing.
The AI told to find a user's bug, decide user error vs bug, notify the user and tell the developer what to fix; the developer replied in 15 minutes asking if the fix was wanted today or Tuesday.
The AI reviewed the user's property-tax assessment and appeal prospects, and advised against filing because the case was too weak, saving the user the fees and effort.
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.
His AI skill was built from his earlier renovation sessions and loads on its own whenever he mentions the house, the contractor or an invoice. It carries the project context, the key contract clauses and where every file lives, holds a step by step invoice verification checklist, and drafts his emails in a language that is not his own, with a pre-send check that every number matches the project register.
A couple runs their online shop with a team of AI agents, each with its own job: one handles products, categories and stock, one does deep search analysis, one makes product photos and short videos, one runs the blog, one posts to social accounts, one manages ad campaigns, and a brand expert that knows every manual and writes in the shop's voice. Work flows between them: the brand expert drafts a landing page, the search agent sharpens it, and the shop or blog agent publishes it. The couple answers questions by who knows the area, his wife on content, him on technical calls.
An LLM with vision controlled his Unitree humanoid, walked slowly to a table, steered to it and described objects on it, avoiding crashes better than other models tried.
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.