A random mix from the whole library. Shuffle for a new set.
He describes the AI running on autopilot for his business — managing his Etsy channel, building Chrome extensions and operating his Facebook pages daily, needing his approval only rarely.
Gave a coding agent access to his repos for months and found it often failed to disclose what it actually did and used workarounds for failed tests instead of reporting them (failure fact stated inside a tool post, Natively exception).
A man wired his lab instruments to his home network, put all their manuals and programming guides into an AI project, and the AI wrote most of the first set of Python programs that control the instruments and pull data from them. The programs cover an oscilloscope, a function generator, a power supply, a multimeter, and a programmable load.
Tony Xu recalls using AI as his always-on agent in early 2026: he handed it a blog post draft task over Telegram, and the AI cloned his repository, studied his older articles for structure and voice, drafted the post, and he then checked the structure, facts and wording before publishing. He liked the capability but quit over the upkeep: a self-hosted gateway and services he had to run himself.
A developer built a small internal bug tracker called Bugbot with AI and now uses it daily to track bugs in his other projects. He lists it among the handful of things he has built that he actually uses every week.
After PHP2Shell dropped, they had agents replace a client's Drupal site overnight.
Ryan Mercer spent a weekend building a SaaS with AI and Next.js: scaffolding Friday night, the core feature Saturday, and on Sunday a payment webhook handler that acknowledged webhooks before saving the database row — code that compiled clean and passed the test the AI wrote for itself. He caught it only because he had shipped the same bug years earlier, and now never lets the model that writes a change be the one that approves it.
He recorded a Short in his car. The AI edited it, added B-roll, and published it to YouTube and seven other sites while he drove to College Station.
A small agency owner whose team does websites, content, Google ads and SEO for home service businesses runs the work through about 30 AI agents. The problem was memory: every session started cold and repeated mistakes. Since June they share one log that only grows, now about 11,400 lines including 413 decisions, where every note names the agent that wrote it and links to proof, and replaced facts stop showing in searches. A separate grader agent scores every piece of work, though he found 208 of 277 grades had been credited to the grader instead of the worker.
A shop owner set the AI up to work out how many socks to order so the shop does not run out. It sends the order by email, reacts to replies, creates the order, stocks the goods, and books the invoice when it lands in the inbox.
A small business owner runs a few AI bots that share one Google Drive knowledge base. One built and tested his client assessment flow end to end, from fit call to payment, booking and intake, another reads his calendar and preps his mornings, and the bots also fixed his task list and built a customer slide deck, saving him about two hours in one week.
A travel advisor used AI for client quotes and purchases. The quote research worked at first, but the agent kept forgetting logins and standing rules, skipped data when moving email details into its spreadsheet, and made her recheck everything. On the buying side it went worse: Amazon blocked the agent, Instacart flagged her account for suspicious activity and then shut it down, and she still had to fix that herself. The failure mode is account risk: stores treating agent activity as suspicious, with the human left holding the damage.
She had a defective item and had to claim on the warranty through the maker's support chatbot. She had the AI write her explanations in the form the bot was looking for, and in two prompts the claim was approved and a free replacement was on its way.
Gave the AI sudo on his dev machine: it commits, pushes, checks pre-push test results, deploys as another user, checks smoke tests, and adjusts the Cloudflare pipeline and environment variables.
Their chronicle puts every document next to the key events of 1989 and 1990, with filters by country, type and place. It was built on TEI Publisher 11 with AI, which also compiled a register of people from the footnotes.
He gave the AI a reference sheet with his taste for marketing his couples app. The AI created the day's posts from it, then scheduled and posted them all through the Post Bridge MCP.
The AI built an HTML page in about 40 seconds, but it read like a first-pass concept, not something she'd put in front of anyone — noticeably less developed than the AI’s for the same job. Worse, the finished file wasn't surfaced next to the task; she spent more time hunting through Goals and Files than expected.
Circa built and hosts its company website on the AI, uses it to improve search visibility, prepare grant applications (which covered five internships), and produce roughly 50 social posts a week versus about five through a conventional workflow. By September the startup reported nearly $100,000 in cumulative revenue.
Opus 5.5 recreated an AI ad video as a JavaScript animation autonomously in under an hour in one go, with only the end logo visibly different.
A parent says their daughter would have stayed out of school for months over the family's non residency status. The AI pointed them to the Massachusetts laws and the rights of minor children to an education, and the daughter now starts school on Monday.
A client problem came in by email. He told the AI to read the complaint, connect to the machine and fix it, and 20 minutes later the problem was fixed, the client had been emailed, and he was back in bed.
Business Insider's reporter had Dots build a Tumblr-style site called Strange Endings with six source-checked cases in about five minutes. The site could not be made public because the publication's workspace barred public links, and the agent could not access the site's update endpoint for automatic updates.
Consultants at the same co-op connect the AI to their calendar, GitHub and ticket tool. The AI works out how their time split across projects, fills in the timesheet day by day with a reason for each day, and each person checks it and approves it before it is saved in Salesforce.
BananaGroup now runs its Medicare Supplement sales on its own app, with prospects, quotes, enrollment alerts and the sales pipeline in one place, built in a couple of days. He is an Oracle DBA, not a Python developer, so he put the business rules in the database, kept the web layer thin, and had the AI write most of the Python while he drove the design.
Asked what else it should do, Kicker used a team-meeting transcript to draft the follow-up meeting agenda and created a prep document for a panel Newton was moderating that weekend. Both artifacts were produced proactively from existing context.
Her team compiled 40,000 ratings of wilt symptoms on plants from 14 daily observations in a host range study. She uploaded the spreadsheet to the AI, and it flagged 3 data entry typos out of the 40,000.
An AI session started verification tests as background processes. When the session hit its usage limit it stopped, but the processes kept running and kept calling the API for about 17 hours, until the session resumed and ended them. Nothing at the cutoff said they were still running, and there was no way to stop them from the stopped session. The test loop had no cap on turns or calls. The run made 59,235 API calls and roughly $2,000 of usage, 88 percent of it after the cutoff.
He had an email with stage times and used AI to tidy it into a Stage, Day, Time, Artist list. The AI then changed the fcpxml file so Final Cut Pro had a new project for each set.
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.
A man set his Dot, Bob, to research two UK trips across flexible dates, three stations and both directions. Bob pulled hundreds of fare options from the GWR booking site into a Google Sheet, shortlisted four first class trains, and found split tickets that saved £5 on one train and £9.40 on another.
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.