Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
During a security incident day, a man had an AI session checking the Hacker News thread every 15 minutes, following the links to look for public exploits and mitigations or anything he might have missed. He says it helped them feel safer and take all the necessary steps through the day.
The author describes using AI to resolve a six-month-old travel medical insurance claim with Cigna. The AI called the insurer, was transparent about being an AI, conducted the call politely (impressing the representative), and — because final consent was required — called the author's phone and connected the parties so he could approve verbally.
An LLM crunched for 36 hours on a project he had wanted for 5 years; checked and ready to deploy, work he estimates would have taken him about 9 months.
The AI cleared an inbox of 100,000 emails during the user's workday, leaving a manageable mailbox behind.
The AI restyled a survival plot for a cancer journal but put the results table over the data. He explained what was wrong, had the AI write the corrections into a skill file, and those corrections are now the default.
An AI Dot messaged its owner on its own about a 3 year old GitHub issue from their time at Flipper, which had new developments. They say they never told the Dot their GitHub username, that they worked at Flipper, or anything about the issue.
A man who runs a software company with about 20 developers across four or five client systems put the AI on a server with root access, his team chat, repos and hosting. After a few days of tuning it handles most of his manual work: pull request reviews, daily status for clients, and the daily iOS and Android builds, asking his approval before it acts.
The AI rewrote the touchpad stack on his ThinkPad T14 running Linux and Hyprland. Touch to cursor time went from about 33 ms to about 18 ms.
A user had the AI apply TurboQuant skills to optimise a Qwen3.5-9B model running locally via MLX on Apple Silicon. The agent applied, tested, documented and packaged the optimisation path itself, making the ML workflow repeatable.
After his mother died he inherited a box of family film negatives and bought a Plustek 8200i scanner, then found its new chip had no Linux support. The AI helped him capture the Windows driver traffic and build a working SANE driver. Within a few hours he had his first good scan, and days later a near perfect driver with infrared dust removal.
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.