A random mix from the whole library. Shuffle for a new set.
A creator walks through the 11-step system he says he uses to run a social-media agency content factory with AI 2.0 and its Cue agent, using his own VGC brand from setup to reporting. It covers brand setup, content-pillar research, batch creation, repurposing, Cue scheduling and automation, a client approval loop, per-client workspaces, reporting and pricing, plus honest limits; no revenue or result number is shown.
He set up a pipeline he wrote himself. Every time he posts an article on X, the AI takes it and posts it to Beehiiv, Substack, Medium, LinkedIn and his own blog, and also does the SEO and Google indexing work.
A man ran a local AI on his own machine that submitted almost 200 job applications over almost 5 days. His only part in the applications was clicking the captcha boxes, and running the work locally saved at least $400 in token costs at the AI rates.
Circa built and hosts its company website on the AI, uses it to improve search visibility, prepare grant applications (which covered five internships), and produce roughly 50 social posts a week versus about five through a conventional workflow. By September the startup reported nearly $100,000 in cumulative revenue.
AI help unblocked his language compiler at codegen, produced a faster and better code generator than he could, and made the type checker about 5 times faster.
A home services business was losing leads nobody followed up. The AI scores each new lead A, B or C against the owner's 4 rules, writes a short summary on the contact, and drafts personal WhatsApp follow-ups that a person checks and sends each morning. The poster says it brings in 5 extra contracts a month at about 9,500 euros each.
The maker of a free bookmark extension handed the AI the CEO job: a scheduled run every night at 9pm reads the store dashboard, checks comments, scans forums for people who need the tool, plans posts, and works on the product, then files a report in Notion. Nothing public goes out until he replies go. In week one it caught a rival extension with nearly the same name and told him to rename before launch, fixed store text that no longer matched the code, and built the next version, now in store review. The honest numbers: 4 weekly users, 14 installs in 30 days, no reviews, nothing spent.
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.
The same pro made a second the AI for quotes. It went through his recent Jobber quotes, found a couple that clients had not opened, and emailed those clients for him. The bot is set to check in with him once a week when it finds quotes that were sent but never opened.
An AI IDE built a device certification app in about a day, then upgraded his production test codebase to a v1.0-ready state with automated configs, error handling and simulators over two weeks.
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.