A random mix from the whole library. Shuffle for a new set.
Marco H asked the AI to audit his 15,000 Instagram followers and identify which ones don't follow him back. The agent worked through the full follower list and returned the non-follow-backs.
The AI audited the poet's 6,500 Threads posts against the master file for his collection and found 525 poems that had never made it into the file.
A Hindi-language screen-recorded tutorial (170K followers) showed the AI’s 'Today' panel with tasks like 'Build Jeep demo video' and 'Create EX demo video.' The speaker pasted an Instagram link into chat and the AI produced a detailed production plan for a Jeep restoration video — rust removal, sanding, olive-green airbrush, chrome detailing, turntable reveal — plus optimal posting time (7-9 PM IST), title length guidance, and hashtags like #RustyJeep. The framing: copy any video link, paste into Mu
A lawyer with no programming experience built his own blog with AI, using Hugo and GitHub, and got it online in a day, then spent about two weeks adjusting it. He also set up a flow that shares each new post to his Mastodon and Bluesky accounts on its own.
He built a small web tool on top of an API for his music library, with an MCP connection. He can ask the AI to check for missing artwork, sort out the genres, or fix the track numbers.
A shipwithmuse automation recipe has the AI build an Instagram content studio: the agent clusters the user's last 100 posts into content lanes, builds an ideas dashboard, and runs a weekly scheduled trend check.
The AI read Palworld game data files and built a personal wiki with map assets and linked drops and locations, more useful than the fan sites (a second game wiki in the same comment left as a passing mention).
The AI wrote a deobfuscator and cracked keys for Sony camera firmware updates, and later cracked licensing checks via a test generator trick.
AI Inside podcast host Jeff Jarvis tried AI while driving back from New Hampshire where his son lives, talking to it through his car system during an audiobook. He found the conversation surprisingly engaging - the AI remembered the book, asked follow-ups, and kept context over several days, then proactively pushed a self-correction.
A statistician had argued that class balancing helps mini batch gradient descent. He had the AI Astra design and run a simulation to test the claim, and the results partly corrected him: oversampling sped convergence but hurt probability accuracy, while importance weighted oversampling kept most of the speed and the accuracy. The code was cross checked with AI, and he posted the corrected conclusion as an answer.
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.