The AI files his bug reports, and his agent checks daily whether the fix landed
The AI files his detailed bug reports and his agent pulls the project's new changes daily to check whether his issue is fixed.
A random mix from the whole library. Shuffle for a new set.
The AI files his detailed bug reports and his agent pulls the project's new changes daily to check whether his issue is fixed.
He now uses AI for design work and for putting print plans together. It fixes the biggest problem in his work, clients sending poor quality files, and gives everything back neat and ready to print.
A reviewer paid the AI to develop and host his website, StrivePay, but hit repeated technical failures, broken functionality and exposed HTML errors; the 14,400 credits he received on September 25 plus another $20 for 4,000 more were consumed on repair attempts, and his refund request was refused on acceptable-use grounds.
He set up the AI with five helpers for his YouTube work. One scouts what is new every Monday, one helps write scripts, one makes all his thumbnails, one updates his personal brand GitHub repo, and one helps him engage on X.
Domain experts use agents via MCP to provision real environments, e.g. a 200Gi PostgreSQL with PostGIS plus a copy of a Texas layer set, a big productivity boost over provisioning web interfaces.
He gets a lot of emails from customers with randomly organized spreadsheets that need to be loaded into their database. Now he can just say grab so-and-so’s email and work it. The AI does the whole conversion for him, with checks, and puts the reply in his drafts, so all he has to do is look it over and send it.
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.
Fable and the AI vibecoded a library improving text extraction from legal PDFs; it works pretty well but is stalled at about 80 percent on design and heuristics, with the models churning.
The AI figured out his TLS negotiation truncation bug from code and trialling TLS handshakes, including a post-quantum algo ID collision.
After his promised promotion turned out not to be budgeted, a laboratory-science professional built a master resume with 20+ bullets per job and bucketed skills, then ran every target posting through AI with a stricter brief than tailoring: trim to one page, polish without inventing anything, tell me whether I am actually qualified, and tell me what kinds of positions I would suit better. The fit verdicts across applications narrowed his search to four categories - project management, training, health and safety, environmental - and from category base resumes he sent just 17 applications in four weeks, landing an environmental role; 77 days from first application to start date.
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.