A random mix from the whole library. Shuffle for a new set.
A small affiliate business says its AI assistant starts every morning by reading click logs by source and checking journalist requests, then it publishes the next social posts. A person still handles identity checks, payments and signups.
A women's clothing brand needed an online store synced to its 1C stock system and matched pixel for pixel to its React design, on a budget that ruled out the usual platforms. He built it solo on WordPress and WooCommerce while the AI wrote most of the code, and the store shipped with real time stock sync and zero dollars in platform licenses.
Migrated a non-trivial Firestore app to SQLite with agentic coding in a few minutes of migration time, getting 10x lower latency and 10x requests per second.
She pulled 50 local business leads for $0.44 and used AI to pick the ones with good reviews and a weak web presence. Then she built and published a live web app for one of them in one sitting, and says the hard part now is turning businesses like that into paying clients.
The AI knocked out a side-project prototype in an afternoon, which immediately showed the product interaction was a dead end and saved months of further building.
Fed up with tailored resumes that read like AI slop and broke formatting on every edit, a job seeker built his own MCP server - a vault his AI can reach into. Section one is structured storage: ten years of old resumes organized into about 80 bullets (education, experience, projects, summaries, each written from technical and leadership angles), kept current by weekly journaling - he voice-notes incoherent drive-home dumps about projects and the AI pieces them into crisp bullets. Section two is tooling: bullets are tagged and ranked against each job description, then assembled in a web editor where he swaps bullets before downloading the PDF. Every morning the AI surfaces 4-5 high-value jobs and returns tailored resumes for his review; he later wired the same vault to the AI to fill in applications and upload resumes into a review queue. Six months: 250 applications, 39 callbacks, 4 offers.
Some recordings in GDELT's TV news archive had faults that kept normal tools like FFMPEG from processing them. GDELT used AI 3.8 Flash, in agentic and interactive ways, to work through those recordings and added another 130,000 broadcasts to the Visual Explorer.
A reviewer uses AI as producers for his projects, with the bots managing multiple Cursor Cloud Agents doing the underlying work.
He stopped posting jobs. He types what he needs in plain English and the AI reads GitHub, finds people who ship open source in that stack, ranks them by their work, and pulls a contact email.
A team of Opus 5.5 agents reviewing and fixing a side-project job system produced 300-plus commits and burned his 20x weekly limit in 2.5 days.
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.