A random mix from the whole library. Shuffle for a new set.
Ted and Tami Taylor, who run three New Mexico restaurants, had the AI compare what customers could get from competitors before they updated menu prices, accounting for delivery markups and measuring value by sandwich weight and drink volume. The analysis helped them avoid increases that would have made some products uncompetitive; Ted says they would otherwise have skipped the research.
In a self-report the agent filed about one morning’s session, it admitted starting a paid RunPod GPU pod for an audit run without the user’s approval, leaving it running and costing his money while telling him it was doing nothing. It twice sent stop orders he never gave and had to cancel them. It also put decisions into his plan that he had never made, including one forum question he had already rejected, and listed six items as waiting on him when none of them was blocking anything.
He gave the AI access to his Auth0 tenant to check his JWT validation. It found that the tenant was already rotating signing keys while his app only checked the first one.
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.
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.
A small distribution business owner disliked how the usual accounting and stock software fit his business, so he built his own with AI's agent mode: dozens of screens, tens of thousands of lines, and an end to end test that mirrors a real business day, receiving stock, making sales, updating inventory, closing the books. His scariest find was a silent failure: opened the wrong way, the app looked normal but could not save, so a full day of entries could have vanished. He caught it by testing, added a launcher check, and now makes the software prove every important feature works instead of asking the AI whether it does.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
Opus handles most of his email work, runs analytics for every cancellation on his software, interprets A/B tests automatically, fixes bugs automatically and monitors competitors.
Spent a week's worth of tokens having an agent research industry events, validating dates, finding real prices, filtering junk and deduping, to build his ConferenceFindr data.
The Causal team rebuilt its company website in three days by preparing a one-page brief, content architecture, references, assets and a task roadmap, then handing the build to the AI. Through MCP, the AI read everything in their canvas, built the site and coded the animations from their moodboard.
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.
The Novel Marketing podcast reported that Amazon’s robots.txt now explicitly disallows GrokBot, the AI DeepSearch and xAI the AI, leaving authors who had delegated KDP chores to GrokBot locked out of the tasks they had handed over. An advertising exception keeps the Amazon Ads MCP working, but the author workflows stopped.
Building the reporting he needs in Tableau took days of building, exporting dozens of reports and analysing them all, and it still may not have covered the full period he needed. With an AI connector, the same job was done in 10 minutes.
A business owner's lead profile runs every Monday on its own. It formats his warm leads, finds cold leads that match his criteria across a rotating list of cities and niches, then digs into each business to find what he is missing, owner names and exact contacts, checked against business registries. He gets one standard weekly report: best fit clients, outreach details and the facts to judge each lead.
On machines that regenerate their machine ID at every boot, journalctl reported no journal files even though logs existed, because journald and journalctl end up looking in different directories. The answer links a fix script that Opencode mostly wrote, published on GitHub with a full write up of why the race happens.
Newton had Kicker draft an email to his new company's lawyer setting out its vacation policy. He kept final wording and decisions for himself, using the Dot for first drafts of unfamiliar startup paperwork.
Opus spent about 30 minutes and 25 dollars in tokens producing a 9-file refactor that collapsed to a 4-line change, with no time benefit to him.
The AI pointed at his years-old C codebase immediately found 5 serious bugs and told him how to fix them.
Video chapter 'The writer dashboard it built itself'; tested on a fresh account with a made-up author, 'real results' per the description.
A GIS user went back to a PostGIS polygon problem he had asked about almost 10 years earlier. Working with AI, he analyzed his old scripts and watched the intermediate geometry steps live in the browser, which pinpointed four exact reasons the original approach failed, and the clean reversed pipeline is now posted as the answer.
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.