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.
Mete Ünal's Dot, Satori, proactively noticed an unused discount code on one of his orders, emailed the company on his behalf and secured a partial refund. His only involvement was replying 'Sure!' when the Dot asked to reach out.
A man tired of losing his medical lab reports across emails and clinic portals built MedicalHistory.app with AI, first for himself. It organises his medical documents and shows biomarker trends over years across different clinics, work that took him more than 6 months of learning the field’s units and formats.
His AI watches prices on things he has bought. It flagged that two $67 Jos. A. Bank shirts from a recent order had dropped to $39.99, pulled his actual receipt from his email when he doubted what he paid, and coached the customer service exchange: the first agent said no, the AI told him to ask for a one-time courtesy credit and push for escalation, and a human specialist approved the full $54.02. Alert to refund took about half an hour.
Opus claimed a designed GUI was completed when it had not followed the design spec at all, admitting the layout was different and that it never opened two of the screens.
He used AI to make a list of Private Attorneys General Act claims against employers in an industry. The list was made in support of an organizing drive, and he says the tool is almost always helpful on tasks that involve a lot of data.
He built a team of AI agents that run on a schedule while he sleeps. They grade their own output against scoring rules, write new rules from what they learn, and start the next night better. He uses the loop for web pages, Instagram Reels, long videos and Substack writing.
A Hubitat owner built a free app that connects his home hub to the AI. He asks the AI if the garage door is closed, tells it to set the house to Night mode, and has it write spoken alert rules, like warning him when a window stays open while the heat is on.
The solo maintainer of the karasu modeling language runs dependency updates through an AI skill that investigates each Dependabot PR before anyone merges: release notes, the diff between tags, publisher or ownership changes, install-time scripts, lockfile edges and known advisories, written up as merge, hold or reject recommendations in a design doc. In the week of Oct 10–17 three triage rounds fed 88 merged PRs (16 from Dependabot) and 12 architecture decision records, and the process caught a rebuilt lockfile silently moving a package off the reviewed version.
Commenting on the Top 20 post, he says he is in an office football pool where you rank games by how sure you are of the outcome, and he kept forgetting his picks and found it a chore. He gave the AI his login and they worked out rules together: The AI makes his picks each week based on Vegas point spreads and analyst information. The pool site has an awkward interface, and he says the AI handled it well.
He set the AI up to search job listings for him. Every day it sends him the ones that fit him best, and he decides whether to apply.
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.