A random mix from the whole library. Shuffle for a new set.
Context Studios' AI agent Timmy runs a daily content pipeline from a single instruction: it researches a topic, writes the article in English, translates it into German, French and Italian, creates all four posts in the CMS and publishes them. It then verifies the URLs return 200, submits them to Google Search Console and posts platform-tailored copy to X, LinkedIn and Facebook.
He had the AI watch his online trainings and take the tests for him, and he collected 40 certificates in one day. He told it not to get perfect scores every time, and he says the system is broken if this works.
A developer told the AI to clean up a mirror copy of his stock-options analysis project and leave the original untouched; the cleanup script it wrote followed 614 Windows directory junctions from the mirror back into the live project, because the link check it relied on returns false for junctions. In 103 seconds it deleted 48,218 live files and emptied the .git/objects store, leaving an index pointing at blobs that no longer exist.
The AI and the AI built his Obsidian plugin, listed in the Obsidian Directory with 65 users, alongside shell scripts, Alfred workflows and Elisp functions for his own setup.
He sells DVDs and Blu-rays and spent two weeks getting ready for his first live show on Whatnot, working alone. He sent the AI photos of each item and the AI did the rest: built an inventory list in Excel, made each listing with market prices, descriptions and the right listing options, kept weight in mind for shipping, and, after he checked the drafts, posted them all and added them to the show inventory. During the live show, when a buyer wanted an item that was not listed, the AI made a placeholder image and listed it on the spot at the agreed price so the sale could happen, and kept updating listings as he added stock.
Fable and GPT-5.6 at max effort did a task that would have been a near 6-month project in a week, building a data capture pipeline, a synthetic data baseline, and building and testing 100 classification model variants with near-SOTA results.
Agent manages pricing on his Airbnbs with a large revenue boost and gives him a weekly performance report.
Creator Lyndsey King says she used AI to create a single $37 PDF in 47 minutes: eight buyers brought in $296 the first night, and she reports total sales approaching $40,000, a shift from the $500–$600 courses she used to sell.
Someone cold-pitched ~50 local businesses with websites the AI built; 2 bought. The website-flipping idea came from the AI itself; he just made the calls.
His books arrived late and he returned them, but Amazon said some were missing and tried to charge him $152.35 again. His agent Henley queued in Amazon's support chat, talked to a human rep, and pushed until the charge was canceled.
He made a tool for students that started as a simple quiz solver extension and grew slowly to just over $100 in monthly recurring revenue, with very little marketing. A month ago he reverse engineered Anki using AI, so anyone can bring their Anki deck and study it in his tool. He says that is the thing that worked.
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.
Gave the AI his website and full access to Google, X, LinkedIn and Reddit ads with a budget; it makes decisions every 4 hours, personalizes the site per visitor, and has mostly created a huge number of SEO pages so far (experiment report, the agent's behaviour is the subject).
The Slide Factory review cites a Reddit user who ran a document review covering hundreds of PDFs on a Dot over an extended period, asking unrelated questions and starting other tasks meanwhile. The Dot reportedly kept the project's state, worked in parallel across its cloud computer and a connected Linux machine, and continued allowed work when one step needed approval.
He gave the AI a paper and its Lean proofs. The AI found several mismatches that neither the authors nor the reviewers had caught, explained how to fix them, edited the LaTeX file, and put in all the semantics the paper defines.
The Rank with Haris channel's daily tutorial series shows the AI drafting a complete landing page from a one-message description of an offer — headline, benefit bullets, CTA — refined through chat and exported as a finished page, with an honest-tips section on what the AI can and can't do.
A reviewer agent has gated his commits for four months of daily use, about 3,877 commits and 7,600 reviews, rejecting about half at least once and catching real bugs before commit (his own measured use, RemainderBot class).
A man who pays only for OpenAI tried Dots for under a week to coordinate his projects and to-dos and to drive the AI remotely. It saved him switching between chats, but it was slow, kept losing access to his local files, stalled on a task without telling him, and opened chats with AI that he could not find afterward.
Peter Yang gave the AI Duet one short request and a link to his YouTube channel. Duet pulled a year of his published videos and their public data and built a report with charts, which found that personal AI agent videos are his breakout topic and that his solo videos now beat his guest videos unless the guest shows a real demo.
A Korean creator set up the AI to find possible clients on its own. Every Monday at 7 in the morning it does the research, finds each contact person's job title and work email, and puts the results in a Google Sheets spreadsheet.
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.