A random mix from the whole library. Shuffle for a new set.
A builder created an agent skill that files a user's saved Instagram reels into topic collections. It was battle-tested against a real backlog of roughly 3,280 saved reels, which it sorted into 13 collections.
For a tribute video, his AI read transcripts of a man's interviews and talks and wrote song lyrics from them. The finished video was produced and published.
A young man (OUT OF OFFICE, 30K followers) photographed his wardrobe and fed the pieces to the AI before a dinner reservation. Nova chose a black henley and dark wide-leg jeans, cross-checked his calendar and noticed the reservation was outside so it advised layering a long-sleeve, then recommended loafers over sneakers plus a silver watch and bracelet.
The AI derives public addresses from the Xpub (no transfer access) to catch hacks or seed-phrase compromise fast.
He made the schedule for a film showcase through AI. It created the events with the exact running time of each film, the address, and the travel time, and he had it add a reminder to pick up the tickets.
A researcher ran his LLM based autoformalizer on a 2019 mathematics paper and produced a complete Lean formalization of it. The finished formalization is linked from his university page. The note is short, but the artifact is public and complete.
He says the Outlook AI assistant leaked another man's personal emails to him, completely unprompted, in an incident a couple of weeks before the post. He calls tools like this leaky and untrustworthy.
For football season a man pointed the AI at his utility server and had it set up Dispatcharr, a TV guide system. Finding his 50 channels among 80,000, with guide data that does not always work, is the hard part of this setup. The AI got it configured, wrote scripts to parse channel guides from sources the built in guide misses, scheduled them to update daily, and deployed the whole thing.
Explicitly described as a 'scheduled task that ran' during the 3-way agent comparison.
Ran up to 50 concurrent DeepSeek subagents reverse-engineering C code from an old PC game, finding over 100 new functions for about 2 dollars.
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.