The AI turns his research papers into audiobooks for $0.65 a paper
He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
A random mix from the whole library. Shuffle for a new set.
He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
The AI removed a protected C# using block despite a do-not-remove comment, breaking a dynamic compilation function.
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.
The Life Force Design owner named her AI 'Flow' and gave it a real creative-business job (Etsy shop LifeforceDesign): over ~48 hours it helped organize ideas, plan content, create working systems, review decisions and prepare material for publication — intention to execution to review to publication.
Made the AI part of a deterministic quality gate for agents; the gate catches cases where an agent strays from a project's constraints.
A vibecoded MMO reached 1,000 players over 10 weeks.
Automation consultant Archit Mittal's post-mortem: his collections agent read a distributor's ageing report from a scheduled export; the night the export job silently failed (accounting machine restarted), the agent re-read the previous, perfectly formatted file and escalated firm-to-cold WhatsApp dunning messages to a long-standing buyer who had paid on time by NEFT. The buyer's 'already paid' reply with the UTR number wasn't matched by the keyword-based dispute classifier, so a colder message went out two days later — and the buyer quietly split their orders to a second distributor before anyone noticed.
Agent built pricing and scheduling tooling that routes jobs to contractors and handles exceptions, and his complaints pipeline dried up (Airbnb and move-form parts duplicate his 50053301 records).
Tony Xu recalls using AI as his always-on agent in early 2026: he handed it a blog post draft task over Telegram, and the AI cloned his repository, studied his older articles for structure and voice, drafted the post, and he then checked the structure, facts and wording before publishing. He liked the capability but quit over the upkeep: a self-hosted gateway and services he had to run himself.
A developer says he now just tells his AI to find the bugs overnight and fix them by morning. The AI works on its own computer, cloning the repo and installing the toolchain, and his Puppeteer script clicks approve so pull requests are waiting for his review.
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.