A random mix from the whole library. Shuffle for a new set.
He is treasurer for a nonprofit food co-op. He gave the AI the co-op's financial statements, some notes on what he wanted to explain, and the logos, and 20 minutes later he had a ten slide deck and separate printouts for the annual meeting.
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.
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.
Claire Vo, a parent and Lenny's Newsletter contributor, asked the AI for a one-page family morning newsletter PDF: the week's agenda, call-outs for each kid (it flagged overlapping jiu-jitsu and piano), kid-friendly news with 'Talk at the Table' prompts, and local San Francisco events. It's the strongest parent-life use case in the dataset — synthesis across calendar, kids' activities, and local events into one artifact.
His charity food pantry needed a barcode scanner and stock checker but could not afford a paid system. He built one with AI in two days, and it now runs on a laptop in the pantry.
Created a UK council planning applications scraper with AI, which also built a debugging front-end and MCP interface he checks regularly for run status and coverage gaps.
The AI’s tools produced printable booth artifacts for a nonprofit he advises; in about 30 minutes he had a professional PDF uploaded to FedEx Kinkos for printing.
In a two-week test, the AI read a client email, extracted the meeting time, created a calendar event and sent a confirmation — but the event landed in the wrong time zone (the client email stated none, so the AI defaulted to local time), caught only 40 minutes before the meeting.
He asked the AI to screen his unread Instagram threads one morning. It came back with 50 unread, looked at the threads he named across his Primary, General and request folders, and told him what people had sent. The unread count was still 50 afterward, so nothing showed as read for him or for the senders.
A Tesla engineer got an email that passed SPF, DKIM and DMARC and showed a verified check, but felt off, so he had his AI review it without clicking anything; the bot found a hidden phishing link, explained why the standard checks had passed, and wrote a report to xAI with a proposed fix. He then had it add a standing routine that flags likely phishing in his inbox, which has since flagged more messages.
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.