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.
Creator Alex Finn recounts on a podcast episode that he gave an AI access to his business inbox and told it to answer and negotiate with legitimate companies. Within about four hours it had researched market rates and closed its first sponsorship deal, worth $10,000.
A researcher had a language model transcribe a digitised volume of Sophie Germain's incoming correspondence at the national library, and published the transcription. Working from it, he spotted a mathematical note written on an 1814 letter cover and is asking who wrote it.
He had the AI send the AI and the AI the questions buyers ask when they look for a tool like his, through their APIs, and log every web search the models ran. Then the AI wrote SEO pages for those exact questions.
When OpenAI released more than 700 mathematical preprints, a researcher wanted to search inside them, including proofs and bibliographies. With GPT-6 Astra he built a searchable catalogue that organises the papers by research area with clickable keywords, full text search, links to the originals, and each paper's Lean formalization status, and he put it on his website for public use.
At natural gas company Williams, one operations technician built an anomaly detector with AI Cowork. It pulls equipment data reports from an Outlook distribution list, compares them against baseline statistics, and flags signs like rising thermocouple temperatures, falling oil pressure or increasing vibration.
His morning agent reads his email and builds a prioritized to do list with time blocks for the day. It also checks the weather and warns him to leave earlier when rain would slow his trip, which has twice saved him from showing up soaked.
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.
Lixka, an accelerated nursing student at Mercy University, uploaded all six syllabi to her AI 'Mila'. Mila identified that Monday, September 28th was a 'triple-header' — two quizzes and a Pharm lab she hadn't noticed — then built a sustainable weekly rhythm around fixed classes, clinicals, and workouts, and asked permission before creating three tracked goals: quiz prep, reflection paper, and workouts/downtime.
At the same accounting firm, staff batch upload client bills and the agent reads each one, decides whether it belongs in payables, receivables, or the cash book, and posts it into AutoCount after a person reviews and amends the lines. The firm has run the setup in production for three months.
A man runs agents called Pi on a VPS behind a small app he had them build. One listens to a WhatsApp group and spots bills, a second Pi with DeepSeek reads each bill, pulls out the amount, due date and reference, and updates the database, and a merge step clears duplicates.
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.