A random mix from the whole library. Shuffle for a new set.
Paired with Zapier, the AI checks his spam folder for mail that should not be there; most scans find nothing, but the few real messages it rescues make it worth running. It also uploads and catalogues invoice payments for the finance team, work he used to do by hand.
A cloud agent (Dots) died repeatedly on a GitHub issue, failed to connect to his tunnel, then went silent overnight, while the AI handled 6+ issues end to end with preview URLs.
The AI wrote a test run scheduler and migrated to nextest, cutting local release build cycles from 60+ seconds to about 3 seconds.
A community college counselor of 16 years turned her idea for paid degree planning into a working side business: The AI helped with the business plan, built and edited her website, including a later round where Work mode edited the live Squarespace site directly, found leads and shaped her replies to potential clients. She has her first client and a lot of leads.
A developer tired of managing terminal windows one way and other windows another way grew his own replacement with AI over the course of a few months, one that treats graphical apps as part of the session too. He uses it now and says he has never looked back.
He needed a foreign language voiceover on a video presentation at work. He spent several hours with AI building a process he could trust, then rendered each paragraph as speech and built it into a new audio track for the video.
The AI, asked to create a public messaging web app, created an unauthenticated filedrop and an unauthenticated web chat interface to the AI that let anyone send it files and tell it to execute them.
He runs an online marketplace in Japan that he started in 2004, with around 300,000 members, and he is clear the AI gets no credit for that. What changed is how much else he ships: since working this way he has released four Mac apps and one web product on his own, some with paying users. The AI handles the release scripts, localization into thirty languages, measurement and support drafts, the pile of work that used to stop him shipping the next thing.
Kantar's People Team split HR data preparation into subtasks and gave each to an AI Studio agent: one located documents, another translated everything to English, a third found duplicate policies and drafted formatted versions. Ten agents cleaned, tagged and organized 4,000 artifacts into 400 policy documents in six weeks, grounding the firm's new HR advisory agent.
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.
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.