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.
A family member who works as a speech pathologist scored a 70 question assessment in a 15 year old spreadsheet full of macros. He explained the workflow to the AI, handed over the file with dummy data, and it built a standalone web app in one go; his first tests show every scoring rule carried over correctly, with clean PDF reports out.
Had the AI convert several thousand firewall rules to FortiManager global rules.
A forum user gave the AI the days his family could go to a Christmas season show that runs all through December. The AI went through the ticket site day by day, found the seats, and ranked the options by price and seat placement, which he says helped the family pick the best tickets.
He runs his own small businesses with AI agents. Each job has its own agent with its own instructions file: one triages the inbox, one handles outreach, one prepares quotes. The agents only draft; he approves every email and quote from his phone before it goes out. A brief arrives at a fixed time every morning with the overnight replies, what is waiting on him, and the follow-ups for the day, and it replaced his own daily check-in. When he corrects something, the correction goes into a shared notes file that every agent reads, so he only has to say it once.
He has invested for five years in big index funds, but as a Muslim he must purify any gains from companies that are not Shariah compliant by giving that money to charity. Companies move in and out of compliance every quarter, across more than 20 quarters of his holdings with gains feeding later gains. Four hours of work with Cowork and about $150 of tokens produced the full model and his answer.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
He found the Excel games he used to play as a student, where you guess TV shows from their theme music. They no longer run on modern Excel and the site that hosted them died years ago, so he ran them through AI and put them on the web.
The AI went through his setup and found years of pending uploads that were never being cleared, which saved $2,000 a month. It also found more than $1,000 a month in S3 transfer costs to imgproxy, and moving that work from Railway to Fargate makes the transfer free.
On day 19 of using her AI Mila, the agent offered three beginner low-impact workout options built around her garage equipment — she did all three — and keeps weekday workout reminders running.
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.