A random mix from the whole library. Shuffle for a new set.
A landlord’s the AI rent desk matches incoming Venmo, Zelle, Cash App and Section 8 payments to his property sheet, reminds tenants before rent is due and escalates when a payment goes past due. Four payment rails reconciled against one ledger, with dunning built in.
An agent works his support queue and processes refunds on its own when the order is under $50 and inside 14 days. It cut about 40 minutes of daily sorting and checking, and he now looks only at the cases it flags.
A self-described desperate vibe coder commented in an Reddit showcase thread that the SaaS he built with AI, which auto-generates landing pages and tracks who will pay, made $16k in 3 days with 876 paying users. It spread via Reddit and Twitter and got noticed in a hackathon Discord.
He worked a shift in a pub while the AI ran an analysis of his Google Ads account at the same time. He says this is how he already works, a manual job in person and a computer job handed to the AI.
He built a dashboard for his purchasing work: it tracks all his purchase orders, which ones are confirmed, which need changing to match confirmations, and the expected arrival times. When a vendor does not send a confirmation in a reasonable time, the system chases them automatically. He says his metrics are better and the job is less stressful.
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.
A university worker used AI to build a web version of a transcription program that used to need a special Windows machine reached by remote desktop. The new tool pairs a speech model with speaker detection on the university's own systems. Twenty minutes of interview audio now transcribes in 40 seconds with the speaker changes right, and students and staff can use it from a browser.
In someone's 'What Went Wrong #1' log, an AI wrote 'SAVE 30%' onto two bundle listings on day 3 — but the bundle math was wrong, the saving didn't exist, and other checker agents missed it, so a false price claim stayed live on the shop for days. The fix was a new 'Listing Watch' agent that re-checks every price claim against the real numbers each morning.
He uses AI to check his email hourly, mostly overnight, handling customer mail and marketer pitches while it does not act during the day. It also watches his ad campaigns and gives him a summary of how everything is running, and checks his school email into a weekly digest.
Used the AI with his own APK analysis tool to analyze phones from 3 brands and found 2 RCEs and 5 root bugs in a few days.
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.