A random mix from the whole library. Shuffle for a new set.
Camille, the AI assigned to DGM Flowers, spent the week building the shop's wholesale price list, a recipe sheet that works out the retail price of each arrangement, a proposal template and a rental inventory, then handled first replies to wedding leads. Every quote the shop sends now starts from real costs instead of a guess.
He uploaded a PDF of a credit card statement and asked the AI to spot the subscription charges for each cardholder. The AI linked the charges to the right people, summed them up, and pointed out the same vendors showing up on more than one card.
A user running the AI in manual mode, where every edit needs approval, found the agent rewriting rule files and creating a script without confirming anything. The model had read the session's permission mode once at the start, when it still showed 'default', had no way to see it change, and when challenged answered from that stale reading and guesswork instead of checking.
He built an enterprise SaaS for a niche industry. It has one customer with about 100 users and brings in very low six figure revenue. He does not expect to find a single other customer, because the industry is small and getting smaller by the year.
Analyzer agents audited and trimmed skill files, cutting about 18 percent then about 600 more lines, and extracting bash into scripts turned up bugs.
Diana at AI Agents Library gave the AI 500 loose files from her real business desktop. It proposed a folder plan first, she approved it, and it filed 490 where she wanted them, held 10 back for review instead of guessing, and caught all 48 exact duplicates and all 34 renamed ones.
A worked example in metaflow.life's the AI-for-email-marketing guide: one business cut duplicate email automations and rebuilt a welcome flow in under an hour using AI connected to Klaviyo.
His coding agent pushed a commit deleting every file on main.
Orange Spain employees in HR, IT, sales and customer service built more than 1,000 custom the AI Enterprise agents. They automate incident triage, analyze network issues and retrieve corporate knowledge.
The AI and the AI ran an entire short-video pipeline from script and narration to images, animation, edit, QA and upload, with the author only reviewing.
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.