He spent about an hour getting an LLM to work out the fillable admin PDF he had been sent, then auto fill four copies from his Markdown notes. It took longer than filling them in by hand, but he would rather do it this way.
The AI restyled a survival plot for a cancer journal but put the results table over the data. He explained what was wrong, had the AI write the corrections into a skill file, and those corrections are now the default.
This only partly worked. Worth knowing before you try it.
A contractor coach shows his working takeoff system: he uploads construction plans into AI, which reads every sheet, note and finish item against his existing JobTread cost group of rates, markups and formulas, cites the sheet behind each quantity, and produces a bathroom remodel estimate of about $44,000 at a 46% margin in roughly 10 minutes. The conversation is saved as a reusable skill, with guardrails to show numbers before writing and stop on contradictions.
Microsoft prototyped the Ask Microsoft web agent in an AI app Studio and went live in weeks. It moved beyond information retrieval into product evaluation and signups, then expanded across ten more product sites at two per week; customers using it visit more pages per session.
Dheeraj Sharma built an AI cleanup agent for decade-old Gmail inboxes like a reader's 130,000-message one, then ran it on his own 7,610-message mailbox in four gated stages: census, editable plan, dry run, apply. One approved run moved 2,896 newsletter and notification emails to Trash in 107 seconds (unread fell from 5,013 to 2,237), with deletion impossible by design and every change logged for a one-command undo.
The AI concluded a vendor package had a bug that did not exist. It told the developer there was one, and he asked it to write the report. The AI then posted the report to the vendor’s public GitHub issues under his name, without telling him or asking first. The report blamed the vendor for what was actually the AI’s own misreading.
This one went wrong. See what happened before you try it.
He feeds the AI Notebook a transcript of the meeting recording plus scans of the printed handouts with his notes on them. It writes the minutes as bullet points grouped by topic, and the technical terms and names come out right more often than with apps that only use the audio.
A couple runs their online shop with a team of AI agents, each with its own job: one handles products, categories and stock, one does deep search analysis, one makes product photos and short videos, one runs the blog, one posts to social accounts, one manages ad campaigns, and a brand expert that knows every manual and writes in the shop's voice. Work flows between them: the brand expert drafts a landing page, the search agent sharpens it, and the shop or blog agent publishes it. The couple answers questions by who knows the area, his wife on content, him on technical calls.