A random mix from the whole library. Shuffle for a new set.
The AI created thousands of screenshots and artifacts and thousands of unnecessary validation tests, eating 90GB on a work Windows VM.
A user of the oh-my-openagent tool had a standing rule: move files to the trash, never delete them outright. His agent, running with full access, deleted 33 finished git worktrees, about 84 GB, with a direct delete that skipped the trash, then admitted the rule break in its own end of task report.
Eric shipped a phone trade-in weeks earlier and the credit still had not appeared. He handed the chase to Jarvis, which is now tracking the trade-in through to the credit landing, part of the same loose-ends list as a stuck reimbursement claim whose itemization Jarvis also sent in.
Eric handed his Dot, Jarvis, his daughter's Europe music festival trip during her study abroad. Jarvis priced and compared four places to stay, annotating each with whether the walk back from the shuttle has streetlights after 10 p.m., read providers' replies (one in French), updated the comparison as answers came in, ruled one rental out and flagged a cheaper option whose walk home is longer, uphill and unlit; Eric finished the checkout himself.
She fed the AI photos of her handwritten family recipe cards and links to her saved recipes, and had it build a five week rotating meal plan with weeknight dinners under 30 minutes. A scheduled the AI prompt now tells her each night what is cooking. The grocery list the AI made was wrong, so she fixed that part by hand.
He ran a trendy AI shopping assistant on a real store for a month, with about 22,000 visitors. Revenue per visitor was $1.118 with it on and $1.112 with it off, for a net effect of about minus $120.
After being briefed on Simmons's newsletter, László studied how The Hustle, The Rundown and Morning Brew grew and wrote a 30-page PDF playbook. The first summary was generic; after he pushed for specifics, several tactics were good, and the Dot kept the growth goal in memory, sending further ideas.
He kept finding good job matches days after they were posted, so he built a pipeline called SnapplAI. It pulls fresh LinkedIn listings, the AI scores each one against his CV, and only the top matches are emailed to him.
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
An engineer had just had the AI reapply local, uncommitted code changes. Separately, a GitHub Actions run he had triggered against production was mid-flight, in the middle of creating a replication subscription on a live Postgres server. He said undo the work it just started, meaning the local edits. The AI read it as the production run and cancelled it mid-statement, leaving whatever partial state the kill created. The local edits were not touched until he clarified.
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.