A random mix from the whole library. Shuffle for a new set.
One user combined three unrelated errands into a single standing job for the AI: photograph-based book pricing with eBay listings, ongoing Fandango ticket-price monitoring, and a water-filter replacement schedule — a personal resale-and-maintenance pipeline.
A soil researcher had the AI search papers and read his draft files to turn a rejected article idea into a data driven study, and he kept replying please continue. The AI then used his connected email account to write to five researchers asking for their data, and the replies gave him a data set covering more than 50,000 sediment areas and moved the project forward.
She pulled 50 local business leads for $0.44 and used AI to pick the ones with good reviews and a weak web presence. Then she built and published a live web app for one of them in one sitting, and says the hard part now is turning businesses like that into paying clients.
He tried AI's new Comet browser on his own Amazon Ads campaigns. He says his return on ad spend went up by 120%.
Agent reviews LinkedIn every morning, cross-references levels.fyi salary and leveling and resume fit, surfacing 5 to 10 roles a day narrowed to 0 to 2, and his interview pipeline is full.
Analyzer agents audited and trimmed skill files, cutting about 18 percent then about 600 more lines, and extracting bash into scripts turned up bugs.
Shopify's theme store has no filter for themes using the new Theme Blocks with a Group section, so Dragich, a 10-year Shopify Partner, gave the same cataloging job over 999+ paid themes to the AI, the AI and the AI. The AI returned a passable spreadsheet and the AI failed altogether, while plain-chat the AI (he stresses not Cowork, the AI or the AI) built an interactive artifact with its top six theme recommendations.
The AI knocked out a side-project prototype in an afternoon, which immediately showed the product interaction was a dead end and saved months of further building.
The same clinic director built his own phone switchboard where an AI voice answers patient calls first, transcribes them in real time and writes summaries and to-do lists. When two AI models disagree on how a call should be classified, the call lands on a grading screen where he taps number keys to mark the right answer.
He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.
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.