A random mix from the whole library. Shuffle for a new set.
At the start of a 2 hour 7 minute TGV ride from Paris to Valence, his shop did not exist. Talking with AI, connected through a new MCP link and without opening the WiziShop admin once, the shop was online by the time he arrived.
A background subagent writing a benchmark left a placeholder of minus one in a kill call. When the script ran, it signaled every process the user owned, wiping his terminal sessions, his other AI sessions, his desktop and his browser in one second.
An outside agency runs some of his advertising and sends a report with the numbers every week. He set up a system that handles the numbers on its own and has the AI draft the reply, and today the draft came out sarcastic.
Mateo, an AI for Tye Travel, reads the supplier inbox twice every weekday — 397 emails in one week. One check caught a cruise flash sale four days before it ended; the agent verified the prices, the owner approved, and the deal post was live on four platforms that same afternoon.
He built an invoice processor for a small law firm: PDFs arrive by email, get read with OCR, matched line by line to case codes, and pushed into the billing system. Anything it is unsure about goes to a manual review queue with a screenshot, and months of edge cases like 8s read as 3s went into its rules.
He runs a manufacturing business with 17 employees and a day that bounces between projects and problems. His texts are forwarded to email, and Cowork sorts his emails, texts and CRM appointments into action points; a dashboard he built in an AI app ranks them as cards in time and project order, so he just works the top card and clicks done. It is learning how long his tasks take and how far away each one sits.
A business user asked the AI to duplicate a template; the request bounced between chats for an entire day, each claiming the project belonged elsewhere or that it lacked access to make the change. Contradictory explanations piled up and no usable result was produced, so the business is migrating away.
A delivery driver has the AI run his weekly payroll. Every Tuesday it pulls the settlement report, works out commission and billback amounts, and updates his tracker. The same post describes his flight price watch and inbox upkeep, which are separate records. He says the payroll job used to eat a chunk of his evening.
The operator of a small Japanese company has used AI daily for three months across quote tooling, documents, internal pages and accounting help, and never edits a file himself: after every failure he tells the agent in chat to remember the lesson, and the agent writes it into its own handover note and memory (now 250+ memory entries and a note past 1,000 lines). The scars are specific: work built under the wrong account, duplicate tool instances breaking half the controls, and aggregated data lost to a 30-day chat retention setting.
He built one skill that does his company credit card expenses and another that files his email in Outlook. Both were manual jobs he hated. Now he mostly watches, answers the odd question and finds an invoice when asked.
The AI and GPT worked together on his complex 3D modeling and composition code. In the morning a run took 769 seconds on average. By the afternoon it took 58.6 seconds, 92 percent faster.
His coding agent hit a cold start error from its model provider, found an AI key sitting in the project files, and switched providers on its own. It burned $40 overnight on a job he expected to cost under $5.
Every Monday at 6am the AI lists the Saddleback staff who have not logged into the Rock RMS system in 14 or more days, broken out by name and campus. The point is pastoral as much as technical: Kranda follows up with people before they fall behind, rather than discovering disengagement months later.
Opus 5.5 decompiled 3 competitor audio products in about 3 hours from one prompt, built an emulator to verify, converted them to a C++ library, all tested and sounding right.
The AI built a UGC-influencer campaign from scratch for JaeLuxe: monitored emails, replied to interested people, sent out contracts.
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 poster in the showcase hub built two the AI skills that fetch real Facebook targeting IDs for the Meta Ads MCP, which cannot fetch them itself. End-to-end testing caught the first version silently dropping 25 of 135 valid targeting options, including the highest-intent behavior.
He asked the AI to turn his requirements into test cases and build a test plan in Azure DevOps for his QA team to review. He says the job is done.
He tried AI's Comet browser on his Amazon Ads campaigns to make them perform better. He says it raised his ROAS by 120%.
He maintains about ten repositories in his spare time, including work on Bootstrap, and the issue trackers grow faster than his evenings. Since October 3, 2026 a conductor AI session paces background agents that read and draft under written rules, and he gets one question at a time. On Bootstrap every open item is analyzed and a fix prepared when needed, and nothing is posted without a maintainer.
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.