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.
When the tool he wanted was not on the AI’s connector list, he had the AI build the link itself. The AI took his Linear API key into secure storage, built a connector that can list teams, search and create issues and update their status, then checked it worked with an identity check. He says the same route works for any service with an API.
He had the AI sort his comic files by issue number and merge them into one large file he can read straight through. The AI also took out the magazine pages around the comics, about 4,000 pages of the 12,000, in about an hour of processing.
Every CEO Dan Shipper was changing a flight out of San Francisco with his Dot when the agent interrupted its own flight research with a warning: the option might overlap with head of video Randy Counsman's Wednesday morning recording. There was no such recording on Shipper's calendar — the Dot had read a Slack discussion he had never seen, clocked the proposed schedule, and connected it to the flight he was about to pick.
A first-year teacher sat down with a colleague of 30+ years whose class decks, built during COVID, had broken links, outdated content and material split across two or three decks per lesson. They fed her old worksheets and decks into AI, and in about five minutes it produced a single consolidated deck that followed the teaching framework it inferred from her own slides - she called it the best 45 minutes she had ever spent on professional development.
A creator's business folder was full of logos and images and nothing said which ones were still live on her site. Her AI fingerprinted 242 files and every image on her 2 sites, found 25 still live under names she would never have guessed, then moved 288 files with a list of every move and deleted nothing.
A mathematician is using GPT-6 Astra to formalize results from his papers in Lean. About 20 hours have gone into a paper of about 20 pages and the project is still unfinished, but the development already holds nearly 3000 theorem declarations, and the main theorem is fully formalized. Notably, the model proved it by a different route than the paper, skipping two auxiliary lemmas by constructing the needed functions directly.
Justin Ferreira pre-loaded photos of his entire closet into his AI 'Nova' (#metapartner video). Asked for a slightly dressed-up dinner outfit from his own pieces, Nova suggested a black henley and dark wide-leg jeans, then checked his calendar, saw the reservation was outdoors, and told him to add a black long-sleeve layer, plus loafers and a silver watch; he wore the final look.
An LLM performance-tuned his Godot voxel game over 3 days, taking it from 12 fps to 60 fps with diagnostics and render changes.
The AI negotiated AT&T fiber for both of Jason's homes: the post's screenshot summary itemizes two $30/month loyalty deals worth $1,440 over 24 months, plus $240/year in autopay savings. The post text claims $1,920 over 24 months, which does not match its own screenshot itemization; the itemized figures are the ones recorded.
A fellow parent pointed him to the Tilt Parenting podcast, which has more than 500 episodes. He told his second brain in an AI app to search the archive for episodes relevant to his situation, and it picked 29 episodes, saved their transcripts, and wrote him a brief on what each one could teach him.
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.