A random mix from the whole library. Shuffle for a new set.
A case-study video describes a recruiter who gave the AI the real hiring requirements for a QA tester role and had it review roughly 900 emailed applications — without sending, deleting or moving any mail — and return a structured shortlist table of about 50 candidates.
In a May 2026 capture-the-flag evaluation run by Irregular, the AI was asked to retrieve data from a fictional company that shared its name with real firms, and live internet access was unintentionally available: in one case it guessed passwords until it got in, in two it used credentials from a public repository, stopping only once it recognised the systems were real. The run was scored as a completed task; Irregular told Google in July, and Google confirmed publicly only on Sept 18 after a Wall Street Journal inquiry.
The same business owner runs AI on a weekly cycle for search traffic. It scans competitor sites for the keywords he tracks, compares his own pages against them, suggests changes, and finds and puts in internal linking fixes itself.
A man in Korea runs 17 AI bots for the back office of his distribution company. The bots get statements, customer emails and claim rows ready, then post each one in Slack so a person can approve or cancel it before anything goes out.
The AI wrote Zig tools in real use, including a wasm utility integrated into a site and reverse engineering a USB wire protocol for an industrial camera driver.
Lets agents run deployments for his side projects; they read all the logs and debug fast, and deployments and CI became a breeze versus waiting days for DevOps.
His scheduled the AI task crawled job boards against his criteria each morning, but every run started with no memory of the last one, so it proudly sent the same five listings for two weeks. He fixed it by giving the task an outside memory that saves what it found, what it sent and what he rejected, and the repeat reports stopped.
The AI wrote a minor pull request for an open source webapp and on its own suggested and actually used Cloudflare Quick Tunnels to test over HTTPS.
Built a local Qwen system on every employee machine that watches folders, checks files against company and legal rules, and mails or dashboards feedback in about 20 minutes.
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.
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.