A random mix from the whole library. Shuffle for a new set.
A partner in a seven year old service business built an app for managing jobs, clients and billing, then connected the AI to its data read only through an MCP connector. Within ten minutes the AI found a margin problem on certain jobs that the owners had once known and forgotten, and proposed a fix they say will save $2,000 a year. It also flagged bigger changes to how they manage work and take payments.
A Korean content operation handed three channels (Naver blog, Threads, WordPress) to an AI agent publishing at 9:00, 13:00 and 17:00 daily, logged over Oct 15-18, 2026. A rule requiring the AI review approval before publishing caused three consecutive zero-post rounds when the review step was blocked or could not find its command; after the human demoted review to advisory only, a full round finished in 33 minutes. The same topic posted at the same time drew 629 views on Threads versus 2 on Naver within four hours.
Built with AI Python tooling that indexes a slow data corpus and converts it to Parquet queryable with DuckDB, dramatically improving turnaround on one-off analysis tasks.
The AI built him a set of local jobs on a Mac mini, and no model runs inside them. New Todoist tasks print as small tickets within half a minute, and at 7:30 each morning a brief prints and arrives by email with the day's calendar, overdue tasks, weather for two towns, and two clients' numbers from the day before, each set against its seven day average.
He has the AI edit his YouTube videos end to end. It transcribes them, cuts the dead air, zooms the jump cuts, adds lower thirds, sound effects and music, and hands back a Premiere timeline.
Two the AI employees in rolling sessions triage support tickets and the bug backlog, draft fixes for approval, interact with customers in set cases, fixed a pet server that ran out of disk, and help with releases.
Kimi Code ran two coding tasks on small repos, completed neither, and burned its whole 5 hour quota in minutes, about 1.37 dollars of API-rate usage across 1.59M tokens in the logs.
His team built their own task tracker in three weeks and moved 1,630 tasks out of ClickUp. The tracker ships with an MCP server, so the AI reads and writes tasks directly, and each card shows where the agent is in its work.
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 had the AI apply TurboQuant skills to optimise a Qwen3.5-9B model running locally via MLX on Apple Silicon. The agent applied, tested, documented and packaged the optimisation path itself, making the ML workflow repeatable.
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.