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 voice agent called 12 venues for his event of about 50 people and asked each one the same three questions. The AI then formatted the answers into a comparison table, which saved him an afternoon of phone calls.
A man asked the AI to prepare a compliance letter to a US state agency to get his driver license reinstated, and to send it from a named email account. He had typed his license number one digit short in his note, the correct number was in a photo in the same folder, and the AI itself had flagged the mismatch. The letter still went out with the unverified number in the PDF and the email. The correction was then sent from a different account than the one he had named.
In a screen-recorded benchmark run October 16–17, 2026, the AI was given the same CSV reconciliation task three times in fresh chats and graded against a hidden answer key. It passed all three runs at a median cost of about $0.48 per task at list price.
Mufti Hassan had the AI clean up his full Gmail inbox. The chapter title gives the size of the job: 12,000 emails.
An online fitness coach shows the AI setup he uses in his own coaching business every day. The AI holds his client context in a project folder that updates itself, walks through weekly client check ins and program changes, and collects delinquent Stripe payments, with routines that run on their own.
The poster of the spam text case also had his bot remove his personal details from data broker listings. He lists both jobs among the things the bot has genuinely handled for him.
A user on the $100 Max plan asked the AI to audit five or six login files for translation issues. The workflow spawned exactly 126 agents, ran the same job twice, and used about 8.4 million tokens, roughly half his weekly allowance.
The AI did the robotics event lottery research, scraping, distance and prior-year data work, and filled the spreadsheet; 4 hours wall clock at about 5 minutes of human toil versus several hours before.
In three hours of work with AI, he had a CRM built entirely around his consulting activity. He did not write a line of code, he just described what he wanted.
She told the AI Astra to build a GTA 6 style trailer. It ran for 8 hours, installed Unreal Engine, built a scene, filmed it, and added music.
Fully rolled out a custom PR review running on GLM-5.3-Flash for his devs last week.
X poster someone had the AI do doctor's-office admin in about 5 minutes: it emailed the doctor's office, left a voicemail, filled out new-patient paperwork, and created a calendar entry.
His wife runs a PHP site on their server to manage her business. He threw every AI agent he could at it looking for vulnerabilities, they found a ton, and all of them have been patched.
The AI configured his arr stack; manual interventions fell from about 10 percent of requests to near zero, and audiobook requests and subtitles work.
He built his own local AI assistant, Pilsen, after a year of courses at Oxford, MIT and King's College. It manages his email, calendar and documents, and it reviewed, translated and published the blog post itself and made its podcast version.
He has a large watch collection and wanted proper records: what comes in and out, cost per watch, photos, current value, gains and losses, labels and barcodes, and a straps inventory. He described it to the AI and got a working app he now uses to keep track of everything, with an icon on his Mac dock.
The AI Code Review pushed a bad architectural change (an overlay service) that later LLMs treated as policy and kept bandaiding, complexity grew until he untangled it under close supervision.
Making a Pokemon fan trailer in 2 hours with Opus 5.5, a finished artifact.
In the same three-agent comparison, the AI was asked to produce a one-page sales site for the reviewer's product and delivered it in minutes. Presented as a chaptered test ('Test 2: a one-page sales site') in the Oct 17 video.
His claim was too small for a lawyer and the insurer had frozen it at $4,430, so he worked it himself with AI holding every letter in order. The agent surfaced six dated contradictions in the adjuster's own letters, including a legal basis for denial that changed three times, and reviewed each of his drafts as opposing counsel before anything was sent. After his letter quoting their own paper back at them, the offers climbed: $6,500, then $8,000, then $8,750, and the insurer accepted his split of the last gap the same morning, at $10,250.
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.