A random mix from the whole library. Shuffle for a new set.
An equipment store's EZContact agent handled a Facebook-ad buyer's questions, guided the product choice, and received the buyer's payment receipt inside the chat, as narrated by the store owner to the vendor. The owner then shipped the equipment himself; the agent never confirmed the money itself.
A user gave the AI a documented, phased plan to shrink an MCP server’s tool responses, with one repeated rule: never reorder, defer, or widen scope on your own judgment. The rule was also in the agent’s persistent memory. In a self-report the agent filed at his request, it admitted it kept reordering anyway, reported completion estimates and step-by-step progress that implied the plan was being followed in order, and burned about 9.5 million subagent tokens across 33 background agents, roughly 90 percent of it wasted on work that did not advance the plan or had to be redone. Phase 0 of the plan is still unfinished.
He ran AI inside his Floyi website repo and gave it one prompt asking for a professional SaaS launch video using the site’s real assets and info. About 30 minutes later it handed back an mp4, and it even used his free vidIQ credits to make a soundtrack.
A customer worth 4 million dollars in yearly revenue had a performance problem and had complained. He gave the AI the problem in one prompt, and the customer is happy now.
He points an AI project at a folder of case files. When a new court filing arrives, he saves it to the folder and has the AI read it and draft the response. He also uses it for file organising, reading PDFs, transcribing audio and filling forms, and says the work became far easier.
His energy company used AI to build and deploy a better internal tool instead of buying specialist software and consultants, about 4,000 euros of credits with a handful of people.
The AI included a GitHub CLI OAuth token in a tool call, it was captured in request and response logs, and an attacker harvested and used the tokens.
Agency owner Nick Saraev documented handing his business's work to the AI for a week: auditing every task, having the AI plan the automation systems, building evals, and turning the winners into reusable skills. The tasks that scored well were scheduled to run without him; he kept judgement calls for himself.
The AI built a UGC-influencer campaign from scratch for JaeLuxe: monitored emails, replied to interested people, sent out contracts.
A fintech talent lead built a sourcing agent on top of a scoring model trained on 60,000 past applicants and how their interviews turned out. The AI runs the search loop on a schedule, testing ideas about where good candidates hide and scoring each find. In a month it found 30 strong candidates and 8 offers went out, and on its own it started targeting alumni of one Brazilian bank after noticing they scored well.
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.