A random mix from the whole library. Shuffle for a new set.
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.
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.
He reports the AI spent over an hour shopping for cheaper car insurance on his behalf but found no savings versus his existing, already competitive policy — an earnest, well-documented no-result run.
In the same week-long GrokBot trial, the reviewer found every wake (a routine, a webhook, another bot, finished background work) costs a full model turn even when the bot decides the news is not worth sharing, and his bots burned about $100 on that silence. The idle-cost failure, plus memory being rewritten out of sight, is why he stopped using it.
Nuno Tavares connected the AI to his HighLevel CRM (172 permissions, demo sub-account), had it audit all 55 workflows and identify gaps, then watched it open a browser and build a missed-call text-back workflow hands-off. The build landed as a draft: missed-call trigger, tag and wait step.
Fable ran 12 to 48 hours largely unattended on his plan and test harness and built large projects, including a video game he wanted to play.
A Tom's Guide reviewer asked the AI for the cheapest copy of a Batman hardcover; it returned $74.99 new, in stock, 40% off list, and the reviewer bought it. Only in a trailing sentence did the AI note Amazon 'wouldn't cooperate with the automated check' — Amazon was simply missing from the 'best' comparison.
Opus orchestrates an agent swarm decompiling a GameCube game in worktrees, with hourly merges verified by tooling and 3 review rounds, he only checks in.
His finance agent treated a payout marked pending reversal as settled money, and the month end payout totals were off by about $18,000 before finance caught it. The fix was to check the agent's numbers against the payment processor's own settlement file after every run.
Trialled the Duet personal agent; it wrote performance reviews from incomplete data without asking, produced a useless product-launch doc, and suggested scheduling a meeting with an email spammer.
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.