A random mix from the whole library. Shuffle for a new set.
A solo reseller's the AI-built spreadsheet app is his back office: he dumps in a batch of receipts and it enters them, creates a folder for each new item, and sorts the item's photos and receipts into it alongside measurement files. The same app recommends prices, writes his listings and does resale research, which is the part he says makes the $20 a month worth it.
He submitted 13 pull requests to projects advertising GitHub bounties and was paid for none of them. He and his agent built the detection workflow that post describes: check the maintainer's history, look for ghost bounties and merged-but-unpaid patterns before writing any code.
A system admin moved his workplace off an old domain that dated back to the NT4 days and onto a new one. Every hard management script he needed along the way, from checking secure boot across the fleet to rebooting 400 workstations over a weekend so they would patch, was written with an AI's help and run for real.
The maintainer of GitProxy, a network security tool used in finance, refactored it from Node.js to Java with AI working as a pair. Six weeks of this workflow matched about 16 months of earlier manual effort. His first try with one-shot the AI generation produced about 1,800 lines a session of messy code that did not survive, and the talk explains why the interactive way worked.
He is a general contractor. Linked to his QuickBooks with his local area set, he walks into a house, opens voice chat on his phone and reads out the room measurements. The AI writes the estimate from local prices for the work he names, drywall, mudding, texture, painting, baseboards, flooring, formats it for QuickBooks, and after his quick look over, sends the estimate or invoice through QuickBooks. The whole thing takes about 45 seconds, from the job site.
He runs a freelance video editing studio and a coding studio, and his project folders were a mess across drives. He started a small folder making script with AI and kept adding to it daily for five months. It is now a 100,000 line Rust program with 1,390 tests that holds every project he has, gives each a permanent ID, and jumps him to any project on any drive or machine with one command.
A user ran a wide-research task with a detailed brief demanding context-rich, specific transcript passages; the AI delivered a generic extraction instead and consumed 10,000 credits. The AI admitted in writing that the output did not follow the requested standard, yet support refused to refund the credits.
He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
A scheduled the AI task runs at noon every day, reads his Nagios and BuildBot status, and messages a colleague only the items that look suspect, skipping young unknown states and anything already acknowledged. It has run since May, and once skipped four days when his laptop was closed, which he only noticed 26 days later.
The AI built a primary/secondary API callout in 2 minutes after he ported his busiest app stage to Haiku, saving about 200 dollars a month.
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.