A random mix from the whole library. Shuffle for a new set.
He had wanted an automatic provisioner for his boards for a long time but it was a whole project on its own, so he told the AI to write it. Some physical steps still need him, but each board now takes 2 to 3 minutes instead of a few stressful hours.
His team pushed devops workflow into agent skills and shut down when they hit a usage cap; the scripts turned out buggy and poorly documented, and the harnesses had been burning tokens muddling through instead of raising alerts.
Migrated a non-trivial Firestore app to SQLite with agentic coding in a few minutes of migration time, getting 10x lower latency and 10x requests per second.
Ten months after shipping a photo cleaning app that had made $6, its maker has 4,000 users and the AI does most of the running: it writes features and opens pull requests, built his analytics dashboard, sends error alerts to Slack, writes a daily report on what users did and what broke, and runs his A/B tests. He decides what to build and reviews the code.
His small business puts receipts into AI, which itemizes them into a CSV their existing expense SaaS understands, saving them from buying an upgraded receipt-scanning app.
Diana at AI Agents Library gave the AI 500 loose files from her real business desktop. It proposed a folder plan first, she approved it, and it filed 490 where she wanted them, held 10 back for review instead of guessing, and caught all 48 exact duplicates and all 34 renamed ones.
After a short session troubleshooting an iOS widget, about $1.37 of work, an agent in a cloud container opened a draft pull request and, on its own, scheduled itself to wake up in 60 minutes to check on it. Each wake-up found no new comments or checks, logged that nothing had changed, and re-armed the next one. It repeated 89 times across 9.5 days with nobody present. Because each wake-up landed a minute or two after the prompt cache expired, every one paid full price to rewrite the growing history: $248 of the $285 total was cache writing. His entire $250 credit balance drained to zero.
The AI and the AI ran an entire short-video pipeline from script and narration to images, animation, edit, QA and upload, with the author only reviewing.
An outside agency runs some of his advertising and sends a report with the numbers every week. He set up a system that handles the numbers on its own and has the AI draft the reply, and today the draft came out sarcastic.
He wanted one place where people can find all the apps he is working on. The AI built the site, beardfm.app, in about 15 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.