Ran a daily Threads + Instagram briefing as a scheduled task
Explicitly described as a 'scheduled task that ran' during the 3-way agent comparison.
Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
Explicitly described as a 'scheduled task that ran' during the 3-way agent comparison.
His company is pushing staff to use AI. He asked it to pull data from 2 spreadsheets and fill in 3 others, and it did the job in under 2 minutes, work he says would have taken him about 4 hours.
He asked his AI for a pet hair robot vacuum under $500. Instead of a long written answer, it sent four cards he could compare, each with a price, a photo, battery life and a source.
An LLM built Homebrew's Landlock Linux sandbox that had sat unfixed on the roadmap for almost a decade, reviewed line by line by maintainers and shipped.
In 1996 he saved his Amiga drives, his music and the bulletin board he used to run, into one archive file before emigrating. The archive was corrupt and years of repair attempts failed. The AI tried for two days, gave up, then with a stronger model found the fault in ten minutes: a single shifted byte. Every file came back intact. He now runs the board again on a Raspberry Pi, with the scripts rebuilt with AI's help, and strangers can dial in like it is 1996.
He used a GitHub repo that lets the AI act as an agent for Qualtrics surveys. The AI took his disorganized Word doc survey draft and turned it into a Qualtrics survey, formatted better than any survey he has written himself.
PCMag software writer Ruben Circelli put the AI, Google's the AI, and the AI through the same five everyday chores. For the grocery test, the AI lacked a direct Instacart integration, fell back to its virtual browser, and finished the cart only after the tester manually typed in credentials and a 2FA code from email — then added an unrequested expensive saffron that a careless approve-click would have paid for.
Built a fully working Live Coloring Book in two weeks with AI writing the code and the AI image models, product live and kept improving since (retrospective post, agents wrote the code).
A first-year teacher sat down with a colleague of 30+ years whose class decks, built during COVID, had broken links, outdated content and material split across two or three decks per lesson. They fed her old worksheets and decks into AI, and in about five minutes it produced a single consolidated deck that followed the teaching framework it inferred from her own slides - she called it the best 45 minutes she had ever spent on professional development.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
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.