A random mix from the whole library. Shuffle for a new set.
A Purdue professor built Docable, a working tool that turns PowerPoint, Word and PDF files into accessible versions for new disability rules. The AI and the AI helped him create a codebase of about half a million lines in a few months, and his talk explains the checking method he uses so agents can validate their own work.
Rei Derek's Facebook post describes cleaning out his garage for a sale: he gave the AI photos and a price, and the AI autonomously created a live Facebook Marketplace listing — writing the description and correctly identifying the refrigerator's brand from the photo with no prior input.
The AI forked cmux for him and stripped about 90 percent of the code to a simple version that builds in 6 seconds instead of hours, adding custom settings on request.
On October 1 a creator gave the AI his VPS, accounts and Korean course material with one goal: earn $1,000 from overseas customers in 72 hours. It shipped an ebook, two paid offers and two Product Hunt launches and posted on schedule, but revenue was $0 — six store orders all chose $0 on pay-what-you-want.
In his walkthrough of 26 implemented the AI use cases, creator Noah describes his org chart: he is the president, his AI (named Q) is the vice president, and the AI models (Opus 5.5, Sonnet 5.5) are employees underneath. Because Q holds all of his context, goals and business plans, it prompts and manages the coding AIs better and faster than he could himself, and can also direct a self-hosted AI via API key.
The AI goes through their shared work inbox, makes a list of every email, and sends three spreadsheets out to the departments with their tasks on them. The poster says it has basically changed their life at work.
The AI runs automated pitching of the artist's tracks to Spotify playlists, working the placement circuit that independent musicians usually do by hand.
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.
A session generated a Word document with a script. The user then edited the document in Word for about 40 minutes and saved. When he asked for one more change, the session re-ran its script, which rebuilt the whole file from its older in-memory content and replaced the file on disk. His 40 minutes of edits were unrecoverable, because the write went through the shell, where the tool’s read-before-write protection does not apply.
In Tye Gordon's Lauderhill businesses, the agent roster includes Hope, whose whole lane is the gap after the AI receptionist takes a call: Hope follows up on those calls and sends the customer update texts and thank-you emails, each one approved by Tye first. The design point is that an answered call is not a finished job — somebody still has to close the loop with the customer, and that somebody is an agent working from the call record.
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.