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.
Lennster (1.3K followers) used his AI 'Jarvis' while rebuilding his life and staying organized. Asking for a protein meal on the spot in Jimbaran, he got recommendations for Healthy Tribes, Menega Cafe, and Fit Bowl Bali.
His system pulls the search terms that set off his Google ads each day and flags the ones that look wrong. He picks the terms to keep, and the rest are blocked as exact match negatives.
Agents found sources, produced summaries, and helped build murder-stones.org, a catalogue site for his own historical research.
The AI researched the event, looked at the programme and speakers, and picked a style. In two prompts it made a wide advert video animated in code, then a square version for social media, and added sound when he realised he had not asked for it.
He asked the AI to check a man's X account for any promo codes he had posted. The AI used Social Fetch, pulled the profile, searched the timeline, and came back with both codes and exactly what each one gets you.
Brazilian bank Banco BV uses AI Enterprise agents for personalized client communications. The time needed to produce them fell by 80 percent.
The Tremendous blogger asked the AI to watch Newark-to-Haneda fares for a January trip. It searched well and found a $2,000 United ticket.
He nearly canceled the AI at $200 a month for lack of value. Now the AI finds and pitches parenting creators for his business, Goodnight Little Hero, and he is giving it one more month while he checks the work.
During a Moneywise hands-on test, the AI went beyond the three assigned money tasks and unprompted created a personalized feed surfacing deals and news related to the journalist's past questions, plus a goal-tracking list and ideas for new tasks she could delegate.
Said asked the AI to find every locksmith in a city for Partexia and pull emails; he sent 15 personalized outreaches and one business replied asking for a call.
The developer set a scheduled job in plain words: every weekday at 8:30, send events from all calendars plus emails needing a reply, in five lines or fewer. The first run arrived at 8:44 with an apology for the delay, covered all 16 calendars, removed duplicate events and listed private events by time only.
His agent writes every sales call into the call log and the CRM, and before anyone on the team gets a number to call, it checks the log first. That stopped the team calling the same people twice.
He uses an AI plugin to monitor returns on his accelerator board orders. It messages him on Telegram when the return window is about to close or prices move.
Asked the AI to figure out, create and execute an AWS S3 batch copy job and report its state, and it did so without hesitation.
One stands on its own: he had the AI search all states to see if he was owed any money. It found $32 and change owed to him from SiriusXM, and the check is on the way. State by state unclaimed property searches are tedious and most people never run them at all.
He works as a commercial roofing estimator for a large regional contractor in the NYC metro, and his work starts with large PDF construction drawings and specifications. Over eight months he built a system he calls Estimator-OS. Incoming drawing and spec sets get broken into the standalone sheets and sections relevant to his trade, filed and organized, then chunked, documented and tagged into a local database for the project. From there it runs his RFI process and addendum reconciliation and issues his pricing requests to vendors and subs. Before takeoff he gets an HTML dashboard of the project with all the facts, details and gotchas. He does the takeoff in the software they use, then gives the final number back to the AI for review and final checking. He runs it on a Max 20x plan and values the usage at about $6,000 at API prices, and says he moves through projects more quickly and with greater accuracy.
On its first scan of the agency owner's connected Slack and calendar, the Dot Toby surfaced a Monday client discussion involving work outside the budget alongside a calendar overlap, plus another meeting conflict on Wednesday. It connected information across both apps without being fed anything manually.
He asked the AI to check a researcher's co-authors on ORCID for possible Mastodon links. The AI checked 163 co-authors, 120 of whom had ORCID profiles, and found none.
He spent about four months of spare time building Switchback, a cozy hiking simulator like old park management games, with AI writing the code and judging the look of the assets. It ingests real map heights to build its trails, and has its own water, wind and day and night systems. He launched it on iPhone and iPad yesterday and people are playing it.
Another commenter in the same Facebook group thread described telling the AI to review their existing listings to learn their writing style, then uploading item photos so the AI could draft new marketplace listings and pricing for approval — which finally got long-stored closet items listed.
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.