The owner of three Korean beauty treatment shops says she used to end most days having skipped the blog again; now she hands the AI a few treatment photos and one line of treatment information and a post in her stores' tone and her own voice comes back nearly finished, which she then saves as a Naver draft. Posting frequency is visibly up, the setup handles the recurring work, and she now teaches the same sequence to her staff.
He had the AI set up his Twitch profile and says it does not look too bad. He linked the channel and says he will stream there that night at 8pm ET, as well as on YouTube and X.
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.
This only partly worked. Worth knowing before you try it.
The MBI Deep Dives author asked the AI, which has access to his website's back end, to fix a subscribe button that sometimes needed two clicks. The AI worked at it for almost an hour and delivered a much snappier single-click button.
Before he writes, the AI opens a blank page in Obsidian and pins it, splits the window into two panes, and puts his notes and research in the second pane. He says the small setup steps are done for him, so he can start writing right away.
A photographer and videographer with ADHD has his Dot connected to his Notion lists, his calendar and his Google Drive. It plans his mornings, guards his next day bookings, drafts his monthly social posts from his own photos for his approval, watches 50 competitors, and checks at 9 each night what he actually got done.
He wanted a short explainer video on the Browne v. Clarke case for his class, with the beats of the music dispute explained without playing the songs. He gave the AI the links to the case, the AI researched it and press reports, wrote a script he approved, and coded the whole video in Python, building the music from scratch with NumPy and SciPy, with a MiniMax voice.
A session generated a Word document with a script. The user then edited the document in Word for about 40 minutes and saved. When he asked for one more change, the session re-ran its script, which rebuilt the whole file from its older in-memory content and replaced the file on disk. His 40 minutes of edits were unrecoverable, because the write went through the shell, where the tool’s read-before-write protection does not apply.
This one went wrong. See what happened before you try it.
A real-estate agent shows the follow-up system he runs on his own CRM: new leads are added to Follow Up Boss by pasting a text screenshot into AI, a lead-scoring skill grades each one A, B or C, and a daily 7 AM sweep of the CRM hands him his hot leads with personalized follow-up texts and emails already drafted in his voice. The video walks through the connector, the skill install and automating the daily drafts.
The operator of a small Japanese company has used AI daily for three months across quote tooling, documents, internal pages and accounting help, and never edits a file himself: after every failure he tells the agent in chat to remember the lesson, and the agent writes it into its own handover note and memory (now 250+ memory entries and a note past 1,000 lines). The scars are specific: work built under the wrong account, duplicate tool instances breaking half the controls, and aggregated data lost to a 30-day chat retention setting.