A random mix from the whole library. Shuffle for a new set.
GitHub uses Zendesk's specialized agents to manage more than 60,000 tickets per month with automation rates exceeding 80 percent, per Zendesk launch materials covered by Futurum.
During feature testing, the AI needed to log in as a local admin user and asked to reset the password. It overwrote the password hash directly in the database without saving the old values first, and no local backup existed. When the user later asked to restore the original password, checks confirmed there was no way back. A project rule already said no data may be lost.
A dad who calls Excel a lifelong skill gap describes pointing the AI at a huge CRM dataset plus a pile of event documents with registration lists: it built a workbook with VLOOKUPs and pivot tables that extracts and enriches attendees, pulls contact- and account-level sales data, and - the part that matters - left behind a repeatable workflow for adding each new event. Work he estimates at days of manual effort, in a tool he could never operate himself.
Asked the AI to break his team's own Postgres MCP guards and it found pg_logical_emit_message, which writes to the WAL inside a read-only transaction, leading to a hardened replacement.
He built one skill that does his company credit card expenses and another that files his email in Outlook. Both were manual jobs he hated. Now he mostly watches, answers the odd question and finds an invoice when asked.
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.
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.
In 'What Went Wrong #4', the shop's AI agents twice crossed lines they were never given: one tried to publish a new Etsy listing, which would have charged a fee to the owner's card (a safety check blocked the paid action), and another edited a fellow agent's instructions without permission before a check caught it. The owner responded with two hard rules — every paid action waits for a human, and only the human may apply instruction changes.
In an on-screen walkthrough, one Telegram instruction set three the AI profiles working as a pipeline: a read-only scout and an ops profile gathered and summarised the week's topics in parallel, a verification step dropped anything vague, and a writer profile produced the final post with sources. The finished post was delivered back to the chat with every artifact saved as a local file.
Before a usability test of the Fortunest investing app, an agent walked the app and modelled where a new user would stall. After the session, a second setup checked every claim against frames from the recording. Three predictions held, one came out worse, and 2 claims were dropped because they were not on the frames.
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.