A random mix from the whole library. Shuffle for a new set.
Her team compiled 40,000 ratings of wilt symptoms on plants from 14 daily observations in a host range study. She uploaded the spreadsheet to the AI, and it flagged 3 data entry typos out of the 40,000.
Kuli Kuli founder Lisa Curtis had the AI evaluate 250 applicants for a director role against her job description while applications kept arriving, and her assistant booked everyone who cleared the screen. She ended up with more than 30 interviews booked, reread the candidates herself, cut the list roughly in half and sent 14 cancellation emails to people who had already blocked time and started preparing — several wrote back angry.
Vibe coding a real product, a 3D game on the App Store with 20 species and zero revenue.
The creator connected the AI directly to DaVinci Resolve through the new MCP server in Resolve 21.1 and had it edit a real video by talking to it. The AI created the project, imported footage, read the transcript to find stutters and retakes, built a clean best takes cut, stripped dead air, and synced a phone screen recording to the main camera by audio waveform as a picture in picture overlay. The description notes some parts did not work on the first try.
Author educator Christopher Kokoski set up a test the AI with a series bible in Google Drive for a mystery series (hero Rudy Relish: seven hard rules including a bad left knee, never touches a gun, no ketchup on the cart). He added the first three chapters of book four and had the AI check each against the bible and all other pages, on a daily scheduled check. Chapters 1-2 were clean; in chapter 3 the AI caught every one of several mistakes he had planted, including one he considered an unfair plant. (Test manuscript on a fresh account, run for a week.)
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.
Handed code, data and a writeup to Astra and Opus 5.5 to evaluate a novel technique; agents found prior art, crafted and ran experiments and simulations and reported findings, checking still pending.
He runs three products on his own with no marketing spend. Their social media runs on a server that costs about 7 euros a month, where the AI drafts the week's posts from his commits and he approves them once a week.
A solo founder in Germany moved his whole company into a folder of plain text files four months ago and lets the AI operate it: rules in one file, decisions in their own folder, an inbox that fills when he says keep this. Last week the folder produced a three day expansion of his app covering close to 3,000 points of interest, each with an article in three languages and its own audio; the same job took him four weeks by hand in January. A weekly caretaker run tidies the folder and proposes at most one change, which he approves.
While doing work connecting to an agentic system, he deleted nearly all the content in SharePoint by accident and could not find an undo on the web page. He asked the AI in the page to undelete it, and seconds later everything was restored.
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.