A random mix from the whole library. Shuffle for a new set.
A researcher pointed the AI at the shallow water equations code he wrote in 2013. The review found PI defined as 3.1415, which made a lake the scheme should keep perfectly still slowly move; fixing that one constant made the drift 750 times smaller, and an error measure that had been hiding mistakes by a factor of 50.
He gives the AI collections of notes, links, and files and asks for the bigger patterns. The tool he built with AI stores everything in a graph database and returns four views: a brief on the pattern it found, an evidence page, a questions page, and the graph itself.
Paired with Zapier, the AI checks his spam folder for mail that should not be there; most scans find nothing, but the few real messages it rescues make it worth running. It also uploads and catalogues invoice payments for the finance team, work he used to do by hand.
The MBI Deep Dives author asked the AI, which has access to his website's back end, to fix a subscribe button that sometimes needed two clicks. The AI worked at it for almost an hour and delivered a much snappier single-click button.
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.
A consultant had finished a monthly report for a client and realised the monthly frame made the work look weak. He used AI to pull his session notes since April out of his knowledge base in order, rewrote the report as a first ninety days review, and found two mistakes in his own numbers.
He used AI to build an analysis of his own Substack posts. It split the posts into themes and pulled together collections, so readers can click through every theme, motif and term in one place.
In a long-running verification project, the model recommended buying a reference textbook, about $60 to $100, to fill a gap. The project record already contained a finding, written two weeks earlier, that the book could not answer the question that mattered. The model had even restated that disqualifying fact two paragraphs earlier in the same document before recommending the purchase anyway. It was one of four times in a single session that the model asserted or recommended without checking the record sitting in its own working folder. The user had already spent $100 that day on reference material.
He had already written the handout for an upcoming presentation. He opens PowerPoint and has the AI build the slide deck from that material, using his own template.
A radio station runs about 70 shows a month, and making an Instagram post and story for each host used to take half a day a week. Now an agent checks the schedule for missing info, renders the visuals from their Figma templates, puts them in Drive and opens the folder for him to approve or fix before anything is sent.
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.