A random mix from the whole library. Shuffle for a new set.
The AI Launch team reports a live test where a managed the AI Agent, given one chat instruction, searched Vast.ai for an RTX 4060 Ti under a $0.40/hour cap, rented it, rendered a 1920x1080 Blender frame over SSH, and destroyed the instance. The whole cycle took 194 seconds and cost $0.007.
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.
His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
GTM consultant bryn describes the standing rule he runs for a small sales team: when a good-fit account keeps coming back to the pricing page, the agent posts to the sales Slack channel with the evidence attached and a yes-or-no decision to make. The point is that reading and assembling evidence is the agent's job, not the rep's.
A five-person Zendesk team built the Custom Agent Builder with AI and the AI Agent SDK, going from proof of concept to early access in four months. Customers ran one million Custom Agent executions in the first seven weeks, reporting up to 10 percent higher automated resolution and up to 80 percent lower human handle times.
Gave the AI kubectl access and it chased down a sneaky shutdown performance bug in 10 minutes, producing a spot-on diagnosis sent to developers.
Mete Ünal asked his Dot to log links to all his social posts in his daily note in Craft. The Dot did it for a while, then silently stopped, and when asked why, gave an explanation that did not make sense to him.
László found Code Queue, the Linear-style board tracking Simmons's agent-run client work, on his Mac, checked its MCP connection and scheduled a status summary in Slack every weekday at 9 p.m. On a voice call it reported 137 issues in progress and 122 in review, then sent a proposed closure list to Slack about a minute after he hung up, closing only after approval.
He had wanted an automatic provisioner for his boards for a long time but it was a whole project on its own, so he told the AI to write it. Some physical steps still need him, but each board now takes 2 to 3 minutes instead of a few stressful hours.
He has a mobile app with crash reporting through Sentry. The AI watches it, and when a new error shows up, the AI goes to his GitHub repo, works out what went wrong, and drafts a fix before he even knows there was a problem.
His AI pulls from GitHub, Jira, Asana, his calendar and his email, writes up his week, and posts it to Slack without him asking.
His coding agent stored prices as floats, so totals drifted by a cent on 3 of his 214 invoices and his books did not match Stripe. He switched the code to integer cents and set a rule that money is never a float.
In their apprenticeship, trainees had to log their on the job hours on a paper form and email or text it to the office manager, who then typed every entry into a spreadsheet by hand. He built an app so apprentices can submit their hours directly. Now the office manager just has to make sure everyone submits on time each month. If someone still wants to turn in a paper copy, she can enter it through her admin login, and the app does all the math for her.
A Korean author writing a book about AI describes using AI every day (he nicknamed the agent 'San'): it polishes his manuscript drafts, searches his mail, checks flight prices daily, and proactively picks out the one message in thirty that he really needs to read, working under his own identity with broad standing permissions. Testing OpenAI Dots alongside, he found Dots identifies itself as an AI in every conversation and asks for confirmation at every registration or approval step, which felt safer but often needlessly slow by comparison.
A job seeker let the AI apply for jobs for him over 3 days, Sept 22 to 24. It sent 38 applications, all remote, mostly IT support roles, while he sent only 2 himself.
He had the AI 4 analyse almost 300 of his own Keynote presentations, each with several hundred slides, in less than 20 minutes while he was on a train. He then posted what it found.
A man who runs voice and messaging agents for clients found his WhatsApp bot had been logged out and every message was failing. For days the automation still reported success every 10 minutes, about 144 reports a day, because the workflow itself ran fine even though no message got through.
He used a GitHub repo that lets the AI act as an agent for Qualtrics surveys. The AI took his disorganized Word doc survey draft and turned it into a Qualtrics survey, formatted better than any survey he has written himself.
The AI built him a set of local jobs on a Mac mini, and no model runs inside them. New Todoist tasks print as small tickets within half a minute, and at 7:30 each morning a brief prints and arrives by email with the day's calendar, overdue tasks, weather for two towns, and two clients' numbers from the day before, each set against its seven day average.
He is a sales manager who does not code. In five weeks he built a system that connects the AI to his CRM, Microsoft 365, his reMarkable and his Plaud recorder. It tracks meetings and calls, updates his todo list, drafts follow ups and puts them in his calendar, imports business cards, prepares quotations, keeps the CRM updated with meeting reports, benchmarks his prices against competitors, sends personalised mailings, and every night reads the day's email and prepares a recap of the next day.
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.