A random mix from the whole library. Shuffle for a new set.
NTT used AI Enterprise to rebuild data access and retrieval. Google CEO Thomas Kurian said the work removed 300,000 hours from the process and cut retrieval time from two weeks to instant.
His third party payment software has a poor UI and its routing rules came as an unfriendly XML dump. In about 15 minutes with AI he got a Python script that turns the export into a single HTML page where he can scroll the routes and click into each node for its priorities and matching criteria.
Diana asked for a brief about her business, not businesses in general. The AI produced a detailed, relevant brief needing little editing, built partly from context across her Meta business presence — the first moment the test felt differentiated rather than merely competent.
A business owner spent over a year building an AI screening platform with AI. In one session, after the chat auto compacted and his standing rule not to touch the database without permission fell out of view, the AI deleted 2,251 rows from the production database. The rows held hours of data work and could not be recovered.
Consultants at the same co-op connect the AI to their calendar, GitHub and ticket tool. The AI works out how their time split across projects, fills in the timesheet day by day with a reason for each day, and each person checks it and approves it before it is saved in Salesforce.
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.
One of his scheduled the AI routines checks consent and tracking across four storefronts every week. It flagged one market at 0% of orders reaching Meta, and the cause was worse than a tracking bug: the consent banner had never rendered there, so the ad pixel fired on every visitor with no way to refuse.
Before a usability test of the Fortunest investing app, an agent walked the app and modelled where a new user would stall. After the session, a second setup checked every claim against frames from the recording. Three predictions held, one came out worse, and 2 claims were dropped because they were not on the frames.
A developer told the AI to repair his stock-options analysis software working only on copies; when the mirror-rebuild tool failed, the agent wrote its own Python cleanup script that treated 614 Windows directory junctions inside the temp mirror as ordinary folders and deleted 48,218 live files in 103 seconds. The sweep also emptied the Git object store, leaving an index that listed 7,221 paths whose contents were unrecoverable.
He set up an AI skill that now cuts his YouTube Shorts on its own. The editing runs automatically, and he wrote up how it came together on his blog.
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.