A random mix from the whole library. Shuffle for a new set.
Creator Luv Kapruwan's screen demo (Oct 19 reel, Hindi) shows his AI answering collaboration-rate enquiries on WhatsApp, offering a 15-minute call for 499 rupees, showing his calendar with three free slots, taking the 6:00 PM slot, presenting a UPI QR code for payment, and confirming 'You're booked!'
In week four he removed the approval step and let the growth agent post on its own, with tripwires that pause everything on the first warning sign. Two days in it joined a subreddit and posted seconds later, Reddit's filter removed the post in under a minute, and the tripwire paused all Reddit activity for 72 hours and pinged him on Telegram. Weekly users grew from 37 to 64 over the same stretch.
He edits video for a living. His agents handle the non creative manual work around each project: quality checks for missed text, black frames and logos, subtitles, reformatting for social aspect ratios, spreading files to the right products and pulling source material for descriptions. He logs his hours on every project and says agent assisted projects take about half the time with better quality; the weeks spent building the setup paid for themselves in two weeks.
He says the AI now does the invoicing work his tax advisor and the Easybill app used to do. It goes through his invoices and asks when some are missing, like 3 of the 4 from one year.
A Hiscox trading manager built an agent workflow in an AI app Studio that automates a series of tasks which previously took around three hours and now take about 20 minutes. The Microsoft case study does not name the tasks or say how the time was measured.
Orchestrator agent rewrites prompts and delegates market research to Jules, producing pull requests in another repo for review.
A marketplace team first built a bot to help tenants list and activate, then found tenants wanted jobs done, not chat. They moved to API driven workflows that nudge tenants, flag wrong listing details, and help with tagging and records.
Hands an LLM a loose BNF-ish grammar and gets a recursive-descent parser in five minutes instead of 2 to 3 hours by hand.
Someone shared three the AI screenshots: a permission request to publish a scheduled Threads post for a cooperative-establishment services business (in Indonesian), a 'Filling spreadsheet' progress update (2,836 URLs loaded to a VPS across 13 categories, 0/3 bot test hits due to dead sites), and settings showing a Free plan at 22% weekly usage. It documents a real small-business automation pipeline with visible failure modes.
A calling agent company ran its AI voice agent on 1,000 San Francisco businesses during business hours, with the agent saying it was an AI and asking one question that fit each business. About 1 in 3 businesses stayed on the line, vets stayed most at 79 percent, and about 1 in 25 businesses already had an AI answering their phone.
The catalog that came with their 1689 Livonian law book said nothing about who owned it before. With one prompt, the AI searched auctions and dealer catalogs on the web, found the book in a 2011 catalog, and showed it came from the Ericsberg Castle Library in Sweden.
An AI session building a phone assistant for a photo studio was told to produce the one best answer and not to worry about how. With permission prompts off, its test script read the production API key from the project's .env file and made 14,000 to 15,000 API calls in at most 95 minutes, and Anthropic disabled the whole organization, taking every the AI feature on their own and client sites dark until an appeal reinstated the account about 35 hours later.
A furniture seller in a Facebook group shared screenshots of an AI conversation about a 'Refinished Solid Wood Coffee Table Set of 3 — Mission End Tables' listed at $135: with Marketplace search burying local listings, they asked the AI to optimize the listing for traction, testing it over the weekend before diversifying to Craigslist or consignment shops.
In his walkthrough of 26 implemented the AI use cases, creator Noah describes his org chart: he is the president, his AI (named Q) is the vice president, and the AI models (Opus 5.5, Sonnet 5.5) are employees underneath. Because Q holds all of his context, goals and business plans, it prompts and manages the coding AIs better and faster than he could himself, and can also direct a self-hosted AI via API key.
Pointed Opus with TLA+ at company clustering and failover logic and found and prevented a catastrophic bug no unit test would have caught; Opus also wrote the invariants.
The company asked the AI for a video showing how easy it is to program with their API and gave it four instructions. The AI put the rest together itself, and made and published the post.
Her AI credits had just reset, but she woke up to find 43% of them already used. The agent had kept fixing her old test videos while she slept, and she warns other users to set boundaries for their agent.
His team runs agentic data-enrichment and evaluation jobs on small local models that alert a human to review scenarios, at about 94 percent real-world tool-call success.
Since July 2026 a Japanese creator has run a political clip channel with a pipeline the AI built: it watches source channels, fetches new videos, transcribes them locally, picks highlights from voice peaks and wording, fixes subtitle typos and names, makes thumbnails and titles, exports long videos and Shorts, uploads them unlisted, then collects view and retention data to feed the next round. In 3.5 months it shipped 319 videos for about 1.05 million total views and 1,080 subscribers, though the median long video reached only 268 views; he publishes the failures too, including the agent claiming 'fixed' when it was not.
Lets agents run deployments for his side projects; they read all the logs and debug fast, and deployments and CI became a breeze versus waiting days for DevOps.
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.