A random mix from the whole library. Shuffle for a new set.
Daren runs his film company alone. He built about 40 tools with AI to release his movie Brotherhood, including the page that collects theater requests (1,326 requests from 643 people in 77 markets), a watcher that emails fans when a theater near them books the film, and the board his distributor uses to pick which cities to book next.
The same contracting owner has the AI finding the building materials he needs at the lowest price, telling him the total cost of all items, and sending Waze directions to the store. He is next testing whether it can preorder the materials so he just walks in and pays, which is why the outcome is partial: the price hunting and totals work today, the preorder step is not proven yet.
Handed the AI a planned multi-module change and came back to 4 PRs ready to merge in 15 minutes; the work would have taken most of a day.
A backend developer built a voice agent after work that dials out for his friend's collections call center, which makes 8,000 outbound calls a month, most of them dead air. The agent greets the person, works out if they are interested, not interested or want a callback, and hands live prospects to a human with context. After a free trial the owner signed at $5,000 a month.
The AI wrote a POSIX vi implementation from the POSIX specs over a weekend; he has used it for over a month with no fault found in its POSIX implementation.
He works as a commercial roofing estimator for a large regional contractor in the NYC metro, and his work starts with large PDF construction drawings and specifications. Over eight months he built a system he calls Estimator-OS. Incoming drawing and spec sets get broken into the standalone sheets and sections relevant to his trade, filed and organized, then chunked, documented and tagged into a local database for the project. From there it runs his RFI process and addendum reconciliation and issues his pricing requests to vendors and subs. Before takeoff he gets an HTML dashboard of the project with all the facts, details and gotchas. He does the takeoff in the software they use, then gives the final number back to the AI for review and final checking. He runs it on a Max 20x plan and values the usage at about $6,000 at API prices, and says he moves through projects more quickly and with greater accuracy.
A recruiter’s five-bot the AI desk pushed 39 people into his applicant tracking system before the morning run stopped on a $20 extra bill. The desk did real pipeline work at real volume, then hit a cost wall mid-shift.
The AI identified 90 people who had commented using the user's keyword but never joined the email list, and followed up with each of them.
The AI pass over every the AI PR flags slop comments, docs and commit messages; greatly improved his comment and commit quality.
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.
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.