A random mix from the whole library. Shuffle for a new set.
The AI planned, then wrote a finished Google Sheet into the tester's Drive from one named source file, with the requested columns, sort order, summary row, formulas and a chart. She iterated in chat while watching the writes land in the open Sheet, and still checked every formula by hand.
The same solo founder gave an AI marketing bot a standing goal of growing PromptBase's social reach 2% per week, with freedom to experiment. It started a TikTok profile and a YouTube Shorts channel on its own; the founder reports it seems to be working, with no metrics shared.
A digital-health CEO reports his businesses have no full-time developers, DevOps, designers, analysts or product manager: scheduled AI agent shifts handle deploys, an agent-built CRM, per-business-line email sorting, meeting scheduling, technical support and publishing news four times a day. The work runs on three $200 AI subscriptions — $600 a month against roughly $38,500 a month at US median pay for the replaced roles.
He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.
He asked the AI to make a marketing video for his app, Astro Editor. It read the website and repo, rebuilt the app's look as a web page, captured 3,600 frames into an MP4 and generated the soundtrack in code.
The AI cuts his video in DaVinci Resolve by editing a subtitle export with IntelliScript; Resolve then produces the cut version; works well.
He runs two AI agents to check his research writing against a workbook of about 200 sources. One run sampled about a third of his claims, said it was complete, missed a stale fact he knew was wrong, and made up a detail that was not in his sources.
A fully blind accessibility consultant used AI to build a plain language front end that talks to a therapy journal system through its API, so his blind client can ask what appointments she has instead of fighting an interface her screen reader cannot use. He tested every change with the same screen reader she uses and sold the finished product for $1,700.
He fed a career vault tool 21 resumes from the last 10 years plus weekly journal entries about projects, wins and goals. Every morning the AI pulls jobs that match his profile, tailors his resume per posting from that real history, generates the PDF, and fills out the whole application, then stops and waits for his review before anything goes out. Review takes him about 30 seconds per application, against 4 to 6 minutes each when he did it with AI. The post also promotes his own paid tool, so it is marked promotional.
His AI rechecks all 31 providers and 89 plans on TokenPlans.dev on a schedule. It writes the changelog and drafts posts like the one announcing it, and every price is dated and linked to its source.
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.