A random mix from the whole library. Shuffle for a new set.
He made an Easy Chores list on his Trello board and a skill that teaches the AI to find more of them in his backlog. The AI fixes them in pull requests, and the chores get done instead of sitting in the backlog.
Sandboxed agent checks spec and code drift daily, triages findings, and surfaces issues; it revealed product miscommunications on the team.
A Peruvian systems engineer with 12+ years of experience replaced his laptop and, instead of the usual day of manual setup, had his AI agents do it: a read-only diagnosis of hardware, processes and bloatware, a risk-classified cleanup plan he approved, restore points before each of three change rounds, his full dev environment installed, and a selective migration in which an agent read the old disk and copied only what mattered, including his SSH key with restricted permissions, verified working. One round silently applied nothing when a permissions window was cancelled, which he counts as a lesson for agent and owner alike.
Polish marketing freelancer Damian Nowaczek runs AI agents on his own server: every day at 06:00 one pulls data from his Google Ads accounts, compares performance and sends a report with conclusions to the company chat, so he never opens ad dashboards to spot a campaign burning budget. A second agent scans new public tenders daily and picks the marketing-related ones.
Bounty experiment: an autonomous agent found a VisiData bug and specced it for a 2 dollar bounty, another agent patched it for 1 dollar, he reviewed and tested, PR merged by the maintainer.
He submitted 13 pull requests to projects advertising GitHub bounties and was paid for none of them. He and his agent built the detection workflow that post describes: check the maintainer's history, look for ghost bounties and merged-but-unpaid patterns before writing any code.
He could not find any service that tracks new plays by his 30 or so favorite playwrights. Now the AI checks and tells him every week.
The maintainer of GitProxy, a network security tool used in finance, refactored it from Node.js to Java with AI working as a pair. Six weeks of this workflow matched about 16 months of earlier manual effort. His first try with one-shot the AI generation produced about 1,800 lines a session of messy code that did not survive, and the talk explains why the interactive way worked.
An Opus 5.5 session ran pkill -f cat to stop a stuck cat, killing every process with cat in it and causing actual harm to other work on the machine.
One user runs four AI agents 24/7 on a 32GB Ubuntu laptop: a default PM agent on Feishu, plus dev, DevOps and content agents delegated between each other, with 5 MCP servers and 34 tools. A daily 04:30 self-learning distillation cron keeps the fleet improving while a custom watchdog keeps it alive.
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.