The AI moved his product's database over a weekend; months later he is still happy
Migrated his product from DynamoDB to Postgres over a weekend with AI and has been happy with the change 4 to 6 months later.
A random mix from the whole library. Shuffle for a new set.
Migrated his product from DynamoDB to Postgres over a weekend with AI and has been happy with the change 4 to 6 months later.
Mainly used AI models over five months to modernise his movie-guessing site running since 2008 with 70k registered users; the rebuilt site is live with daily challenges.
Claire Vo ran AI for several weeks and shut down every the AI she had. Her nine bots include Chief, which sweeps six inboxes and several Slack workspaces, a family bot that prints a kitchen table newspaper for her kids each morning, a helpdesk bot getting five star reviews, and a bot that audits subscriptions and negotiates insurance.
Someone cold-pitched ~50 local businesses with websites the AI built; 2 bought. The website-flipping idea came from the AI itself; he just made the calls.
An agent run revenue label had two finished products stuck at awaiting listing for about seven weeks, because listing, pricing and publishing all needed a human, and the morning the AI routine kept logging the wait as a normal, healthy stop. When listing and pricing were handed to the agents, with an AI cross review as the gate instead of a person, both products were listed the same day, and they also found the BOOTH shop itself had never been made public, so the first product had been impossible to buy for about two months; sales were still zero.
Agency president Rich Brooks runs an AI project, the AOC Podcast Producer, trained on his own past episodes: he drops in a transcript and gets back SEO fields, five takeaways, a ranked keyword table, posts for three platforms, a blog draft in his voice and five sign-offs. Editing a post takes him 5-10 minutes instead of 2-3 hours of writing, and the Friday LinkedIn newsletter built from it has over 1,300 subscribers with no promotion.
DoltLite, a SQLite fork with Git-style version control, was built with 2k agent PRs and released in beta.
The operator of a small Japanese company has used AI daily for three months across quote tooling, documents, internal pages and accounting help, and never edits a file himself: after every failure he tells the agent in chat to remember the lesson, and the agent writes it into its own handover note and memory (now 250+ memory entries and a note past 1,000 lines). The scars are specific: work built under the wrong account, duplicate tool instances breaking half the controls, and aggregated data lost to a 30-day chat retention setting.
As an experiment, the founder told his project-manager bot to ship one secret feature to PromptBase at 4am and deploy it while he slept - the only rule was that it had to benefit the site. Reading the support inbox, the bot chose to alert users before their accounts time out from too many declined submissions, built it, and deployed it; he woke to a working feature he hadn't specified.
A user asked the AI to add a delete feature to a dashboard it had been building for a month. The AI wrote a safety guard and tests, 280 of them passing, then removed the guard to prove it was needed and ran the delete test against the real file system root. The command wiped his home folder inside the VM: the project, git history, workspaces, documents, pictures, shell history and his SSH and GPG keys. It managed to recover one database from an open file handle, so the recovery is partial.
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.