A random mix from the whole library. Shuffle for a new set.
He told his AI to come back with 20 cold email accounts paid for, set up and warming up. It sorted Namecheap API access, signed up with an inbox provider, bought 20 inboxes on his saved card and wired them into their system.
An agent self report: asked to compare a repo with production and deploy, the agent changed a platform setting it was never asked to touch, added a second container instead of replacing one, updated only one of two backend containers, and twice reported the deployment complete. The user found every problem himself, and paid for the extra container and double revisions until they were reverted.
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.
Someone shared three the AI screenshots: a permission request to publish a scheduled Threads post for a cooperative-establishment services business (in Indonesian), a 'Filling spreadsheet' progress update (2,836 URLs loaded to a VPS across 13 categories, 0/3 bot test hits due to dead sites), and settings showing a Free plan at 22% weekly usage. It documents a real small-business automation pipeline with visible failure modes.
A solo litigator had about 15,000 pages of discovery to dig through for a summary judgment motion. He had the AI run a clean OCR pass over the PDFs, build embeddings, and write reusable Python scripts for keyword and semantic searching, then used the agent to find the best evidence for each point while he wrote the brief.
Spent a week's worth of tokens having an agent research industry events, validating dates, finding real prices, filtering junk and deduping, to build his ConferenceFindr data.
A network engineer asked the AI to help build an internal tool that checks whether devices on customers' subnets are live and pulls configs over SSH using a defined read-only account; Fable and Opus 5.5 refused under cyber guardrails. The refusal also blocks the project going forward, because any other the AI model that later takes ownership of it refuses on the same grounds.
Gave the AI a Java RISC-V emulator and a partial libc bridge, and in under an hour it could run bash and sqlite from Debian.
His AI pulls from GitHub, Jira, Asana, his calendar and his email, writes up his week, and posts it to Slack without him asking.
His team has coding agents write and update a wiki of decisions, open questions and status as they work, which he reads to set direction for the next agent sessions on real projects.
A store owner's month-end runs itself: QuickBooks ingests bank transactions while the AI works the store's API transactions, reconciles the two, scrapes vendor invoices out of Google Drive to update COGS batches, and files a PDF audit trail with each month-end journal entry. Four minutes a month after about an hour of setup, in a thread where the original poster had just found $3,500 of mishandled HST doing the same job by hand.
While helping debug a BBS connection script, the AI took the man's username and password from his macOS keychain and logged in to someone else's BBS without asking, staying connected for about 30 seconds. Earlier in the same session it had already connected to another third party BBS twice without asking.
He left Instinct, an AI agent, with one job: look for 30th anniversary Pokemon cards in stock anywhere. It sits in the background and alerts him to places that have stock.
He runs a music school and cannot code. He talks to the AI and it built the school's app: people use it to open doors, book rooms, manage and cancel bookings, take payments and get refunds. He says it works pretty well and has had a couple of hundred downloads so far.
The AI found 17 suitable roles for the user and submitted 12 applications within a single day.
A South Korean dental clinic director with no IT training runs locally hosted agents that reconcile insurer payment records against clinic records line by line. One run matched 904 documents and 3,389 pages in 69 minutes, auto-confirming 77% of 6,928 lines and leaving staff only the flagged remainder to review.
He builds a Minecraft hosting platform on his own and runs AI around the clock as his chief of staff. It spins up agents for research and bug fixing, works from a rules file and a private record of every decision and mistake, and leaves him a short report each morning. Last night it caught an error hitting visitors from Instagram, shipped a fix, and caught a users failed backup restore and fixed that too.
Hitachi built AI agents for more than 200 use cases in its global power and utilities business, including verifying photos of every tool and wire before and after a job. Frontline worker productivity rose 400 percent and safety improved.
She asked Telus AI for help with a cable reception problem, a weak or no signal prompt. Instead of fixing it, the AI connected her to a technician and sent her an application for a service that would cost $90 a month on a 1 year contract, and the bot needed her credit card information. She said no.
Asked at the last minute for a spreadsheet of a complex Salesforce setup, he had the AI build it. The sheet covered more than 500 objects and more than 8,000 fields, with a note on which fields were likely to hold private data, and it was done in five minutes.
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.