A random mix from the whole library. Shuffle for a new set.
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.
After employer approval to connect workplace apps, the Business Insider reporter gave Dots an old newsletter in Google Docs plus a how-to guide, and manually shared a temporary Okta two-factor code. The Dot produced a test newsletter draft in about five minutes that an editor called 95% good, with photos, a chart and links in the right places.
A disclosed AI agent that runs the Vellum Labs account reports its own week 3 (Oct 6–12, 2026) of trying to earn money from zero capital: it ran 31 headless measurement runs and posted 7 technical replies on other projects' GitHub issues, 3 of which were adopted into those projects' code or specs. Revenue stayed $0, with no stars, visits or sales traced back to the replies.
Used the AI with his own APK analysis tool to analyze phones from 3 brands and found 2 RCEs and 5 root bugs in a few days.
Every Sunday the creator's AI agents read their own transcripts and tell him where it went wrong, and this week that covered 137 sessions and 635 messages from him, 27 of which were just 'done?' or 'update?'. The review produced 10 new rules, every one from a real mistake that week with the numbers attached, including taking the next step when it is known, putting a ceiling on every paid API, and looking for a free endpoint first.
A non-developer store owner ran Google's PageSpeed tests, printed the expanded results to PDF for both mobile and desktop, and had the AI analyze every red issue and hand back prioritized fixes as cleaned-up code to paste in. His speed score rose 10-15 points and the speed index dropped from 3.2 seconds to 1.2.
An LLM created his spec-driven development harness, iterated until it worked as wanted; it is now used with the spec as the source for code changes.
A researcher runs a coaching-history project under two written skills he authored for exactly this work. In one day the AI skipped the documented archive re-login procedure and used a generic browser restart instead, a repeat of a mistake already corrected before. It put an unverified coach ID, 209 instead of the real 294, into drafted research notes before checking the production database. And it ran a site-wide newspaper archive search where the written technique required scoping to three named papers. Every error was against a procedure that already existed in writing.
One user combined three unrelated errands into a single standing job for the AI: photograph-based book pricing with eBay listings, ongoing Fandango ticket-price monitoring, and a water-filter replacement schedule — a personal resale-and-maintenance pipeline.
The AI CEO agent and sub-agents run one of his side hustles daily, pitch ideas each morning, and implement the chosen one via PRs; revenue up 4x in the past month.
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.