A random mix from the whole library. Shuffle for a new set.
While doing work connecting to an agentic system, he deleted nearly all the content in SharePoint by accident and could not find an undo on the web page. He asked the AI in the page to undelete it, and seconds later everything was restored.
One of his AI shortcuts had prices written out, like $10/hr and $1,200. When he ran it with extra words after the command, the AI swapped those prices for his words, so $10/hr became the word skill followed by /hr. No error, no warning. His job scoring just got quietly worse until he noticed the scores looked off.
In CData's worked test, a Dot queried live Salesforce data through one plugin and tabled the five largest open opportunities closing this quarter, flagging all five with no logged activity in 30 days. One deal was still open six days past its close date; the account used test data, as the post discloses.
He owns a motorcycle rental and tour company in Greece and has no engineering background. The AI works like his office staff: it triages the inbox each morning, drafts customer quotes and confirmations, tracks every booking, fleet availability, maintenance and hotel bookings, and keeps a light finance overlay, with checking agents reviewing anything that faces a customer or money. A database it built replaced a dozen spreadsheets. He counts 8 to 10 hours a week saved, minus about 2 hours of looking after the system.
Opus 5.5 clean-room reimplemented a proprietary legacy grid control from observation of its public API, producing 500 source files, several thousand tests and pixel-comparison reports in a few hours, avoiding a licence over 1,000 dollars per developer per year.
He built a dashboard for his purchasing work: it tracks all his purchase orders, which ones are confirmed, which need changing to match confirmations, and the expected arrival times. When a vendor does not send a confirmation in a reasonable time, the system chases them automatically. He says his metrics are better and the job is less stressful.
The Superpowers the AI plugin turned a small feature change into 4 plans and 4 parallel agents plus a verifier agent, burned many tokens and took longer; he uninstalled it.
He connects an AI to his posting app with one key. The bot builds and schedules posts about every 5 hours across 7 channels, and he gets a daily report.
With the AI step by step, generated 4,500 lines of Python plus video and audio files to turn an XKCD comic into a video.
Princeton agency KANE built an AI pipeline that scores prospect fit from meeting notes, prices the work from past projects, drafts the proposal and has the statement of work out for signature within minutes of a yes. Co-founder Michael Kazantzis said a process that took weeks now happens almost within minutes in a day.
The studio had the AI and PixelLab make one sample icon and waited for their OK. Then it made the other 24 in the same style, plus the gray locked versions Steam needs, for a demo at Next Fest in October.
Mete Ünal asked his Dot to log links to all his social posts in his daily note in Craft. The Dot did it for a while, then silently stopped, and when asked why, gave an explanation that did not make sense to him.
He has worked in SEO since 2009 and has no coding background. He shipped a SaaS with paying users anyway. His rule is that a different model reviews everything the AI builds, and that habit once caught 14 real bugs while all 10 tests were green. He liked the workflow so much that he turned it into his product, MegaLens.
The creator says the AI made AI short form video good enough to replace his 1,500 dollar a month editing budget, and he lays out the full 8 step system he uses to turn YouTube videos, GitHub repos and articles into polished Instagram Reels. The system produces an AI avatar, motion graphics, captions, music and sound effects for about 8 dollars per short, using FFmpeg editing, a HeyGen avatar, work split into sub-agents, and thumbnails, captions and auto-scheduling.
He runs a one person business that makes electronics for hobbyists and his customers run several of his devices at once. The AI built a stand alone program that plots their measurements together, the job he had been doing in Matlab, and worked out on its own how data from different products fits together and how to line it up in time.
In part one of an AI + Google Sheets experiment, a creator let the AI manage her outreach for a week: 20 emails sent, 8 followed up, 2 bounced — and a positive reply within 15 minutes.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
John Voorhees has his Dot, Puck, coordinate his work: he spins large projects off into AI threads and lets the Dot manage them, alongside setting up tasks, checking email and doing light research. He says the delegation has worked reliably the couple of times he has used it.
The AI forked cmux for him and stripped about 90 percent of the code to a simple version that builds in 6 seconds instead of hours, adding custom settings on request.
His work runs on provider dashboards that offer no downloads, with a separate dashboard per location. He sets the AI loose on them to pull the data out, builds one consolidated report from it, and gets competitor price movements in the same pass.
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.