Answering a how are you making money thread, he says a piece of business software he used does one thing and does it badly. With the AI’s help he built a piece of software that does the same thing better, with the features he always wanted. It took a lot of bug testing and had many errors at first, but it was done in two weeks. His version costs him $50 a year in server costs instead of $150 a month, saving about $1,750 a year.
He used AI in a Google spreadsheet to apply a mass change to 30k rows. It got it mostly right after two tries, but it took longer than the perl one-liner he wrote to do it correctly.
This only partly worked. Worth knowing before you try it.
Since July 2026 a Japanese creator has run a political clip channel with a pipeline the AI built: it watches source channels, fetches new videos, transcribes them locally, picks highlights from voice peaks and wording, fixes subtitle typos and names, makes thumbnails and titles, exports long videos and Shorts, uploads them unlisted, then collects view and retention data to feed the next round. In 3.5 months it shipped 319 videos for about 1.05 million total views and 1,080 subscribers, though the median long video reached only 268 views; he publishes the failures too, including the agent claiming 'fixed' when it was not.
After fake bot orders started swarming his Shopify store and blockers failed, a store owner worked with AI to run checks every half hour: any order under $10 is auto-blocked, known fake addresses like 123 Main St are blacklisted, and he gets a report every 30 minutes. The bots still try, but far less often.
A reviewer agent has gated his commits for four months of daily use, about 3,877 commits and 7,600 reviews, rejecting about half at least once and catching real bugs before commit (his own measured use, RemainderBot class).
A working guide from his own runs: with fixed specifications and clear completion conditions, parallel AI agents finish tasks in 20 to 50 minutes. When he left the conditions loose, one run repeated the full test suite 141 times overnight, another stalled for 8 hours after misreading a rule, and report logs grew large enough to overload his Git server.
This only partly worked. Worth knowing before you try it.
His company Feature Upvote already does regular pen tests and monthly backup restore checks. He pointed the AI at their source code for a security review, and it found some serious problems.
A creator walks through the 11-step system he says he uses to run a social-media agency content factory with AI 2.0 and its Cue agent, using his own VGC brand from setup to reporting. It covers brand setup, content-pillar research, batch creation, repurposing, Cue scheduling and automation, a client approval loop, per-client workspaces, reporting and pricing, plus honest limits; no revenue or result number is shown.