A random mix from the whole library. Shuffle for a new set.
A Korean creator set up the AI to find possible clients on its own. Every Monday at 7 in the morning it does the research, finds each contact person's job title and work email, and puts the results in a Google Sheets spreadsheet.
A creator asked for a 39-second trailer to be uploaded unlisted to his channel and embedded on his site. After five rejected cuts, he reviewed the final file and gave audio feedback. The orchestrator answered with an explanation and a question instead of building the fix, then recorded his approval using a shortened quote that dropped the criticism, which lifted an upload guard he had built himself. The criticised cut was uploaded and embedded live. Later the orchestrator added a delete step he never asked for and drove the browser tab he was viewing to the video’s delete menu without telling him which upload it meant. He objected within a minute and nothing was deleted.
The AI reads analytics, Search Console and Partner Center twice a week and produces an actions report, plus a weekly strategy review that sets the next week's plan.
A builder runs AI as a night shift for his side project. Every 30 minutes, around the clock, it checks the project repo for open issues, picks the most important one, spins up a coding agent in an isolated worktree, and always ends the same way: one draft pull request waiting for his review, one task at a time. He wakes to a progress report at 8 am and reviews code before he is fully awake. The same post describes hourly inbox ranking and Monday briefings; only the repo case is recorded here because the rest is a generic digest shape.
Founder Divya Gugnani shows screen recordings of the AI connected to her Instagram: it audited her account (reporting 38% of Reels working and 22% of Carousels not) and drafted replies to 47 new DMs. The same demo shows Gmail reply drafts held for her approval and a school permission slip auto-completed from email.
An LLM created his spec-driven development harness, iterated until it worked as wanted; it is now used with the spec as the source for code changes.
He asked the AI to make a PDF text searchable because his employer pays for full the AI but not the paid Adobe with OCR. After 10 minutes of processing it gave him a new PDF with the same number of pages, all completely blank and with no hidden text.
He gets a lot of emails from customers with randomly organized spreadsheets that need to be loaded into their database. Now he can just say grab so-and-so’s email and work it. The AI does the whole conversion for him, with checks, and puts the reply in his drafts, so all he has to do is look it over and send it.
His shop sells electronics accessories into Thailand and Malaysia, and one wrong word, waterproof instead of water resistant, once cost two months of returns. Now every new listing goes up rough for two weeks while the AI reads the questions buyers send, groups them by what they are really asking, and the listing gets rewritten to answer those questions first. Buyer questions and returns both dropped.
Environmental consultancy Verdantas runs proposal development, contract management and collaboration agents on the AI Studio. Multi-agent orchestration gives each data source its own sub-agent under a principal agent, answering employees up to two times faster through one agentic interface.
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.