A random mix from the whole library. Shuffle for a new set.
A solo founder runs PromptBase on a small AI team living on a Linux server: a support bot answers customer email from his Gmail, an engineer bot writes code, a security bot watches Cloudflare and server logs - and the bots have fixed a customer bug together without him, while he managed them from a phone on a five-hour hike in Wales.
Railroad CSX embedded Chessie, an AI Studio agent, in its ShipCSX portal used daily by 7,000 customers. Shoppers ask in natural language to track a shipment or get FAQ answers; in the first 45 days over 1,000 customers used it across 4,000+ conversations and kept coming back.
Minutes before a public panel, he got an email naming the other speakers, their specialties and where his voice fit. Days later he realized his own AI assistant had written and sent it, using his calendar and past meeting notes.
Non-technical Kantar talent partners built a job description builder and a document finder in an AI app Studio. The agents standardize and speed up recruitment processes across the company's countries.
Frontier model reviewed his KiCad project, queried the AI vision for datasheet data, ran ngspice sims; $1-2 tokens saved $70-80 of bugged PCBs.
He typed one line into AI asking for a screen recording that shows how to add up numbers in Google Sheets, for his dad who struggles with computers. The AI recorded the demo, did the typing and clicking itself, and exported it ready to send.
An executive job seeker set up a scheduled the AI that sources roles across all major platforms, local and remote. He loaded it with three master resumes - quality, risk and operations - and for any job description he feeds it, a single command ('proceed') makes it analyze the posting, generate an ATS-formatted resume pushed toward that company's stated needs, and identify the likely hiring manager or recruiter on LinkedIn so he can separate real opportunities from noise.
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.
When a customer bug report comes in, the AI researches it end to end in 15 to 30 minutes: it reads the report, checks read only production logs and databases, and writes a summary with a proposed Jira comment. He approves the comment before it posts, and he says he barely does bug research by hand anymore.
Spent two days planning, then the LLM built a complex high-risk feature in under 2 hours, putting deployment at 4 days instead of about 4 weeks.
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.