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.
He gave the AI a paper and its Lean proofs. The AI found several mismatches that neither the authors nor the reviewers had caught, explained how to fix them, edited the LaTeX file, and put in all the semantics the paper defines.
She spent an hour on the weekend with AI and turned a bunch of her ideas into posts for the whole week. Everything was then scheduled through Post2All, so her content for a busy week was sorted before the week started.
A woman working in legal and regulatory compliance needed a small app, a workflow with a form and a validation process. Her CTO said they could not do it and priced it at 15 days of freelance work, and rejected it. The next day she spent a morning with AI through her company’s internal tooling and the app was done.
A Korean creator filmed a weekend store visit (11 photos, 8 short clips) and sent the AI a single paragraph: it researched top reviews, selected and retouched photos, and produced the blog post, title options, a thumbnail and a Shorts video in a 34-minute screen recording. Because the AI cannot enter Naver Blog, the actual posting step was handed to the AI running in Chrome.
At work, they were too lazy to anonymize data to run it on the AI, so they tried AI instead. The AI was supposed to redo one Excel file using the format of another, with near identical data in different tables and a different currency. Two hours were lost.
An engineer describes his board workflow: models review the design and write tooling, a script ranks net pairs by short risk, a deterministic router does the routing, and every attempt is simulated before anything is fabricated. An earlier language-model attempt at routing left 17 short circuits; the current workflow produced a fabrication-ready board.
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.
A designer treated the AI like a design engineer for his client Wordsmith AI. After 8 versions and more than 90 rounds of feedback, they shipped Draftboard, a live tool where anyone at the company can make on-brand images in a few clicks.
Jenny Stojkovic (95K followers, AI/wealth/investing) documented building a tax-deduction tracker in an AI app after discovering she overpaid taxes by $46,324. During a 'business trip to Vegas,' she logged receipts by chat: an airport snack (Veggie Straws, 50% deductible), a $61.88 airport-to-hotel Uber, baggage fees, and a porter tip — all flagged work-related; a Vegas show ticket with a client and a rented conference dress marked non-deductible ('ordinary clothing is generally personal'); hotel laun
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.