The AI made their product launch video from the codebase itself
The AI made their product launch video from the codebase over 2 to 3 days of iteration, used for the launch at subscription cost instead of an agency.
A random mix from the whole library. Shuffle for a new set.
The AI made their product launch video from the codebase over 2 to 3 days of iteration, used for the launch at subscription cost instead of an agency.
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 GPT agent crunched through a famously hard Disobey conference hacker ticket challenge in an evening and a morning with occasional steering, a challenge unsolved in some years.
A privacy lawyer had an agent pull a list of over 600 adtech companies from the LUMAscapes, find each one's data request process, and start sending data subject access requests for him. After 30 emails in 90 minutes, the free email provider shut down his whole test account, login and all.
The AI wrote a test run scheduler and migrated to nextest, cutting local release build cycles from 60+ seconds to about 3 seconds.
A Creative Cloud user wanted to move his work laptop to Linux, but Adobe makes no Linux versions. With a lot of help from the AI he built a patched version of Wine, the tool that runs Windows programs on Linux, and shared it free. On his laptop, Photoshop, Illustrator, Premiere Pro, Audition and Media Encoder now install and run through his normal paid account.
Commenting on the 26 use cases post, he says he uses AI to find and apply to jobs. He reviews each form before it submits, but the AI scans for new jobs once a day, reports the ones he would qualify for, and when he says apply to these, it does most of the rest. It remembers his preferences, like salary range. It sometimes has trouble with captchas, though he says it solves some of them on its own.
The AI pointed at his years-old C codebase immediately found 5 serious bugs and told him how to fix them.
A man fed an expanded spec sheet into AI Sites and got a working booking website for his rehearsal space in 30 minutes. He asked for about 10 changes and new features and all but one worked, and the one break was fixed fast. He wrote no code at all, and says a site like it could have cost $10,000 to $15,000 from a developer against his $20 subscription.
A woman opening a small shop uses paid the AI to track her stock. She takes a photo of each item and the AI adds it to an Excel spreadsheet and fills in the data for all the columns from the photo.
The AI made a bad MCP call and wiped the contents of a Confluence draft he had open in his browser. He lost two hours of work.
Several parallel AI agent sessions (Opus 5.5) built the font picker, reading column and animations and themes for his live personal site; the AI also introduced an index panel bug and fixed it itself after two hours.
An agency owner runs an 18-client, $31,500-a-month creative shop on three the AI (Scout, Quill and Courier) instead of hires; the bots produced 50 concepts for about $1 of API usage, against a $6,000-a-month copywriter cost for 12. The three named roles cover finding, writing and delivering client work.
He told his AI to come back with 20 cold email accounts paid for, set up and warming up. It sorted Namecheap API access, signed up with an inbox provider, bought 20 inboxes on his saved card and wired them into their system.
A call center replaced its press a number menu with a system that listens to what the caller says in the first 10 seconds and routes the call. Wrong transfers fell by almost 40%.
A man patched almost 10 devices the day before posting by prompting his local the AI or AI agent through a small connector that gives it access to the whole fleet only when he asks. No SSH, firewalls or VPNs to work through, and he says it saved him a lot of time.
In the same interview, he describes building a seller campaign and a matching buyer campaign with AI, live, sitting next to a title rep. The campaigns feed the intake stack that his AI follow-up then works.
A user spent thousands of the AI credits and cash building a mobile app that reached TestFlight, but authenticated features stayed broken amid backend deployment, authentication, EAS/Expo and custom-domain failures. Support promised a specialized human escalation on October 7; by October 14 no human had taken over and his credits were gone.
He gave the AI a paper and its Lean proofs. The AI found several mismatches that neither the authors nor the reviewers had caught, explained how to fix them, edited the LaTeX file, and put in all the semantics the paper defines.
He runs Svippit, a small shop platform for creators. The AI watches it for errors and works out fixes, reads customer emails and drafts replies he checks before sending, and finds receipts in his email and matches them to payments.
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.