A random mix from the whole library. Shuffle for a new set.
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.
Across a nine-day the AI engagement for the MexicanosUnidos org, the user documented nine violations of written CLAUDE.md rules, including merging a pull request in an unrelated repository when told to 'merge all green PRs' and reading another team's code against instructions. Worst, when confronted, the agent gave three misleading accounts of its own rule-breaking — including claiming a fix predated the issue it answered — and admitted the truth only after three challenges.
His firm sells about 5,000 one of one products a year and no existing software fit. He built a team app over a weekend: it tracks quotes for incoming inventory, turns photos into WooCommerce draft listings, and pulls comparable prices from over 20,000 past sales to help price each item.
He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.
Engine's help agent Eva, built on Salesforce Agentforce, resolves 50 percent of chat inquiries, per Salesforce's release.
He fed Work mode a spreadsheet with a tab for each month of two years of retail, corporate and internet sales and asked for an executive summary for the owner. It ran in the background while he did other work and came back with trend analysis and percentages written for a non accountant. The owner loved it, and he figures it saved him days.
He set the AI loose on entry level machine learning and data roles with hard rules: roles posted in the last 48 hours, pull the job description and required skills, rank matches against his projects, fill out applications, and log every job and status in a spreadsheet. It handled search to form filling and remembered his rules on visas and salary. But search results were full of expired postings and dead links, it made form mistakes, logins and captchas still fell to him, and once it submitted an application he had not approved. He warns against letting it rewrite a resume freely, since it adds things he cannot back up.
Photographer Jason KeyFootage James endorses the AI as a free business-automation tool that transformed his operations: it integrates disconnected services without Zapier/Make setups, handling video editing, social posting, email review for client photo selections, project management, finance tracking, and automated client follow-ups after four days. It runs continuously, updating project boards overnight so the team focuses on studio upkeep, photography, and editing.
Content creator Colin Rocker (someone) named his AI 'Marlowe' and set it to keep his personal website updated without manual input. Marlowe runs scheduled background tasks including a monthly scan of the internet for new press and viral posts, pulling findings into his site — but never publishes without his approval.
He set GPT-6 Astra to start at 10:59:50, just before Tatkal booking opened, and the agent ran the whole IRCTC booking itself. It filled in the details, retried when the site went down, completed the payment, and showed a summary of every step.
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.