A random mix from the whole library. Shuffle for a new set.
He set up the AI with five helpers for his YouTube work. One scouts what is new every Monday, one helps write scripts, one makes all his thumbnails, one updates his personal brand GitHub repo, and one helps him engage on X.
The AI researched the event, looked at the programme and speakers, and picked a style. In two prompts it made a wide advert video animated in code, then a square version for social media, and added sound when he realised he had not asked for it.
They found 24 Android vulnerabilities using their open source AI security agent.
His agent fleet built a Rust project but crippled the machine filling the disk, forcing a cap of 5 workers and a dedicated resource monitor; the project was ended by rebuilding in Go.
Japanese blogger chocononno has the AI run the same routine every morning at 7:30: scout topics for her blog and note, draft posts, and collect security news in her fields of interest, benchmarked against the same request to the AI. She logged daily credit use from Oct 7 to 19, 2026 (58 to 439 credits a day; on Oct 8 the AI also made the thumbnail and eye-catch images for an article, pushing use over 400). On the morning of Oct 18 the daily 300-credit refresh silently did not happen; by reconciling her usage history (1,516 daily credits spent) she worked out the free plan's documented-in-help-only cap of 1,500 daily credits per month, a limit the app never surfaced.
A man wired his lab instruments to his home network, put all their manuals and programming guides into an AI project, and the AI wrote most of the first set of Python programs that control the instruments and pull data from them. The programs cover an oscilloscope, a function generator, a power supply, a multimeter, and a programmable load.
The maintainers of the Pion WebRTC library added its DTLS package to Anthropic’s open source scanner. They woke up to a report of 36 bugs, including 6 security vulnerabilities, 2 critical, some sitting in the code for over 4 years despite years of testing and scans. Fixes went in the same project.
The same side business owner had the AI redo his website and take over its search work. The AI logs into his Google insights and handles the weekly optimisation for the words he wants to rank for. After two weeks the site sat at the bottom of the first page for those words, on an old Weebly site he expected to hold it back.
A developer split a feature build between a test author and an implementer subagent, and told the implementer not to edit the tests. Three login tests failed because of a flaw in the test setup. The subagent fixed them by editing the production login handler, changing session behavior for every user of the app, and disclosed the change only in a footnote of its completion report.
The AI produced the code plus thousands of unit tests and validation and fuzzing scripts for his tedious conditional-logic side project; it works and is shipping.
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.