A fintech talent lead built a sourcing agent on top of a scoring model trained on 60,000 past applicants and how their interviews turned out. The AI runs the search loop on a schedule, testing ideas about where good candidates hide and scoring each find. In a month it found 30 strong candidates and 8 offers went out, and on its own it started targeting alumni of one Brazilian bank after noticing they scored well.
He let the AI run on its own for 40 minutes on a billing refactor. The changes looked clean, but it had quietly renamed a Stripe webhook handler, and he only caught it when he reviewed the work.
This only partly worked. Worth knowing before you try it.
A solo developer built a women's clothing brand's production WooCommerce store with AI writing most of the code: a custom block theme matched to the client's React prototype, real-time CommerceML sync with their 1C ERP (including patching a stock parser that silently dropped nested per-warehouse counts and cleaning up roughly 1,000 orphaned variations), and zero dollars in platform licenses. Two client revision rounds of about thirty items each cleared in days, and the whole build took weeks instead of months.
A lecturer at KU Leuven had AI agents build RaftViz, an interactive tool that lets his students step through the Raft consensus algorithm next to the exact lecture note lines behind each step. He wrote none of the roughly 6,000 lines himself, the tool is published for anyone to use, and building it exposed a real bug in the lecture notes.
He has a mobile app with crash reporting through Sentry. The AI watches it, and when a new error shows up, the AI goes to his GitHub repo, works out what went wrong, and drafts a fix before he even knows there was a problem.
His team gave an agent live access to their inventory system on a Friday and it adjusted 973 items incorrectly within 30 seconds. He gives the value as $160,00 as written in his post, and the team spent the weekend putting the numbers back.
This one went wrong. See what happened before you try it.
In AI Agents Library's structured 10-task business test (Sept 27, 2026, free plan, real business material), Diana gave the AI almost nothing for meeting prep: it found the correct email thread among several plausible candidates, located the calendar invitation, and prepared questions — including some she had not thought of. The task scored 10/10, the best result of the test day.
He is job hunting in tech, aiming at startups and big tech. He gave the AI a list of up to 100 target companies, target roles, and his filters: salary range, posted in the last month, locations, industries to avoid, and sub rules like roles that do not need past experience in that exact title. Twice a week the AI sweeps the list and brings back matches. He reviews each role, tells it which were bad and why, or that he applied, and it sharpens the filters for the next run. He says the matches are strong and far quicker than scrolling LinkedIn Jobs. It cannot submit applications, and no interview has landed yet in the post.
A self-published author says the AI, his personal agent, made every cover, flipbook and promo for his 7 books that are now live. The post carries his AI referral code, so he and readers who sign up both get tokens.