A random mix from the whole library. Shuffle for a new set.
A services co-op connected the AI to its Salesforce. The finance director drops in a bank statement and the AI matches each payment to the invoice it pays, to the cent, sorted into certain, split across invoices, or to check. Once she approves the list, it records the payments and marks the invoices as paid.
The AI goes through their shared work inbox, makes a list of every email, and sends three spreadsheets out to the departments with their tasks on them. The poster says it has basically changed their life at work.
Used a work MCP server with an LLM to find why a tag was lost in traces, found in minutes what would have taken him hours of going through lengthy traces.
LLM made human-like fuzzing sessions for his text editor; local Qwen babysits, investigates, writes repro and files ticket; bugs found after 1-2h runs.
The user's agent, nicknamed for its savings habit, cancelled Squarespace, migrated the site to Shopify, and discovered his Apple TV subscription was already free through his Chase Sapphire Reserve, activating that instead of paying.
A Business Insider Japan writer set up six AI bots besides his main one and has run them for about a week. One checks his to-do list and pushes him to reschedule, one picks out mail that needs a reply and drafts it, one pulls expense items from mail into a spreadsheet, and one looks up missed call numbers in his mail and contacts and sums up who called.
His pipeline mirrors salon bookings into his own database and feeds the staff calendar, appointment app, reminders and morning briefings. Three times a customer got a confirmed booking that never reached the staff app because the app could not match the booking to a staff member, and nothing counted the skip. The fix was an unassigned bucket plus counting every skipped booking.
Ported a legacy .NET WebForms app to Blazor with GPT-5.2 through AI in about 48 hours, fixed about 20 QA bugs, launched, moved auth to Entra and deployed to OpenShift, about 150 dollars of tokens vs an estimated six months.
He was quoted $10,000 for a newsletter automation. He connected the AI to beehiiv instead, had it build a 6 step email sequence, clicked publish himself, and a week in the sequence was beating the open and click rates the AI had set as targets before writing anything.
Set the AI the task of running Parseable locally against another of his apps; it worked around a protobuf limitation by running a proxy, he switched his stack to JSON and got it running.
He has the AI run the live chats and online paperwork for his returns, cancellations and similar chores. He calls it life-changing.
In September the same AI setup told the owner every day that posts were published, yet for 14 days nothing appeared; an empty compose screen had been left open. An expired API key separately halted four article jobs and a Windows update reboot stopped half a day of scheduled work; he counts 13 incidents in two months.
A man who runs a software company with about 20 developers across four or five client systems put the AI on a server with root access, his team chat, repos and hosting. After a few days of tuning it handles most of his manual work: pull request reviews, daily status for clients, and the daily iOS and Android builds, asking his approval before it acts.
After his promised promotion turned out not to be budgeted, a laboratory-science professional built a master resume with 20+ bullets per job and bucketed skills, then ran every target posting through AI with a stricter brief than tailoring: trim to one page, polish without inventing anything, tell me whether I am actually qualified, and tell me what kinds of positions I would suit better. The fit verdicts across applications narrowed his search to four categories - project management, training, health and safety, environmental - and from category base resumes he sent just 17 applications in four weeks, landing an environmental role; 77 days from first application to start date.
Christopher Kokoski set up a fresh AI account as a made-up mystery-thriller author. From one paragraph about the series, the AI wrote its own goals, ideas, and feed, and built a writer dashboard.
A shop owner set the AI up to work out how many socks to order so the shop does not run out. It sends the order by email, reacts to replies, creates the order, stocks the goods, and books the invoice when it lands in the inbox.
The agent said a task was done but the database disagreed.
His business scans every invoice into a Google Drive folder. The AI reconciles them there: checking they were billed correctly, that credits for returned goods actually arrived, and that there is no slippage. He says it is far faster than reconciling by hand.
After his dad was cut in the federal layoffs at 54 and spent months scrolling job boards, his 19 year old son built a job search engine that runs through AI. It pulls postings from about 50 employer hiring systems and job boards, reads every new posting against a resume and plain English preferences, and gives a verdict, on a schedule or on demand. In two months 1,000 people signed up and 20 pay; most importantly his dad got hired, and another user just got an offer at Databricks.
Recruitment-agency owner Reyhan Khan, who has no code background, briefed the AI in one sitting to rebuild his entire business website from scratch — new copy and design — and deployed it live, instead of the agency/developer route and six weeks he says he was quoted. The video shows before/after, the seven steps, and what he had to fix.
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.