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.
Gave the AI and the AI performance targets for a port of Dos Navigator, went to bed, and woke up to a fast Swift file manager he now enjoys using, especially for image previews.
Independent musician someone lists 11 daily tasks he delegates to the AI: Gmail cleanup, Meta ad campaign management, email list building, unclaimed royalty tracking, PayPal subscription audits, a disco-house culture digest, Instagram DM opportunity scanning, tour travel booking, gear deal hunting, finance consolidation and streaming stats. He states in replies that he is not paid and that the AI is free.
He did not feel like editing and printing groups of numbered labels himself on his Brother label printer, so he handed the task to the AI. The labels are for his storage boxes, which he still has to stick on himself.
Polish marketing freelancer Damian Nowaczek runs AI agents on his own server: every day at 06:00 one pulls data from his Google Ads accounts, compares performance and sends a report with conclusions to the company chat, so he never opens ad dashboards to spot a campaign burning budget. A second agent scans new public tenders daily and picks the marketing-related ones.
Author Matt Deitke fed an entire book to the AI and had it generate a chapter-by-chapter podcast website, with subagents handling chapters in parallel. The output was a finished multi-episode podcast series derived from the book.
Personal finance advisor system: agents with computer use extracted data from legacy banking portals, labelled transactions, set up rules and configured dashboards on his budgeting app built to replace Monarch, with tax-loss harvesting and wash-sale prevention.
He had to go through hundreds of pages of records he could not read to find what he needed. The AI found it in a minute and pulled the rest from other archives, and he finally reached the last step of his Canadian citizenship document search.
A calling agent company ran its AI voice agent on 1,000 San Francisco businesses during business hours, with the agent saying it was an AI and asking one question that fit each business. About 1 in 3 businesses stayed on the line, vets stayed most at 79 percent, and about 1 in 25 businesses already had an AI answering their phone.
A Japanese learner who had used jisho.org for years hit a word its handwriting search could not match. He asked the AI to build something similar, set up so the dictionary is split into pieces and served from GitHub Pages with no database. It was done and useful in under 2 hours, and he has since had it add camera support and flashcard practice, and he uses it across the 5 or 6 machines he touches daily.
Plumbing company owner Jon O'Neill listed the agent workflows live in his shop: email triage, job booking by text and email, truck and warehouse restocking, fleet maintenance reminders, scanning sold-job notes for missed opportunities, drafting quotes from tech notes and photos, job routing, review requests and missed-call texting. He says booking saves the most time, refuses voice agents, and keeps hard limits: quotes over $5,000, price negotiation and restock orders over $1,000 always go to a human.
Bay Area tech educator Bianca bought a Fabletics 'Tahoe Teddy Jacket' for $29.99 and was silently enrolled in a $69.95/month VIP membership with no email notice. After being charged, she asked the AI to cancel it.
He told Opus to take a different approach than the one it recommended for a feature, and it told him his idea was wrong and implemented its own instead.
He upgraded Proxmox on 3 nodes, with about 30 containers moving from Debian 12 to Debian 13, and put Opus at the wheel. The upgrade finished with zero drama.
His shop sells electronics accessories into Thailand and Malaysia, and one wrong word, waterproof instead of water resistant, once cost two months of returns. Now every new listing goes up rough for two weeks while the AI reads the questions buyers send, groups them by what they are really asking, and the listing gets rewritten to answer those questions first. Buyer questions and returns both dropped.
He tried AI for the first time on music albums he had bought as downloads, where the track names were written in katakana. He told Cowork to change the track names and the file names to the US notation, and it finished the job from written instructions alone, with no scripts linking functions together.
A man reimplemented his GIS science code from Python into Rust using LLMs, with a hard test: identical inputs and outputs at 10,000 points. Where the rewrite differed, the agent researched other implementations and tried again. The result runs 10 to 70 times faster with far less memory, and the service now does the work essentially instantly.
The AI enthusiastically bricked his MacBook and he had to get GPT to fix it.
Her son's school sends home a lot of paper: fundraisers, spirit days, costume parade rules, events like Trunk or Treat. She photographs each flyer and the AI pulls out the dates, times and details and helps her get them on her calendar. When she could not remember whether Trunk or Treat was on the calendar, she asked the AI, and it was added along with the costume parade.
Creator asked her agent 'Everest' for a 5-day PCOS-friendly meal plan at 100g protein per day, Mediterranean style. It produced the plan, a grocery list, a Sunday prep schedule, and set a Saturday 6pm calendar nudge for weekly prep.
Job applications from a Google Form land in his spreadsheet on their own and each one is emailed to him to review. If he replies yes, the applicant gets his scheduling link; once they book they get joining instructions and a reminder an hour before. He runs the business next to a full time job and two kids in diapers, so every message it sends for him counts.
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.