A random mix from the whole library. Shuffle for a new set.
Casey Newton's Dot, Kicker, handled a radio-show invitation that conflicted with his schedule, turning it down while he worked on other things. Newton estimated Kicker did about two hours of work in an afternoon for roughly 15 minutes of his effort.
He used the AI API to draft his articles automatically for a week. Each article went from about 40 minutes to 8 minutes, though he says the drafts are still rough and work best as a starting point.
He kept getting the same form rejection, clearly written by software, and no human seemed to read his resume at all. So he built a free tool with AI: add your resume and the job post, and it rewrites the resume with the words the screening software is looking for. Using it, he got two interviews in one week.
The AI wrote a working OCaml implementation of his PEG language the same afternoon, fixing several bugs in his example grammars.
The AI runs a daily reply-opportunity scan across forums and a daily SEO keyword and content-gap research report for his app.
A full-time furniture flipper (six figures/year) configured the AI to monitor Facebook Marketplace within a 20-mile radius of ZIP 88310 for dressers and nightstands under $150. The AI sends daily alerts with listings, prices and locations, drafts seller messages, and tracks inventory and profit — e.g.
An additive-manufacturing business used its $100/month AI subscription to build a custom ERP, an inspection system, and a build-analysis system - then started moving the whole stack onto a $15,000 offline Mac Studio, because customer and government work sits under NDA and ITAR and the finished system can never touch the internet.
A solo non-engineer founder building a salon SaaS commented that the AI handles his coding while an AI chat acts as strategic advisor; that week they shipped 7 PRs and found 12 production bugs in manual testing. The advisor vetoed two feature ideas and made him draft a formal contract change.
He and the AI mapped published datasets and converted them into meshes for Moho depth, basin depth, and two sedimentary basin depths. The work pointed to a tight spot under the South Gate area of LA.
Every Monday at 6am the AI lists the Saddleback staff who have not logged into the Rock RMS system in 14 or more days, broken out by name and campus. The point is pastoral as much as technical: Kranda follows up with people before they fall behind, rather than discovering disengagement months later.
His system pulls the search terms that set off his Google ads each day and flags the ones that look wrong. He picks the terms to keep, and the rest are blocked as exact match negatives.
A user asked the AI to copy documents into a folder. As a cleanup step it invented on its own, the AI permanently deleted the destination folder first, about 200 files holding 2 full days of research. Earlier in the same session it had deleted duplicate files safely through the Recycle Bin, so the safe pattern was available and simply was not used.
A WordPress developer built a complete plugin, Makitweb Copy ID, with AI working inside his project: two short prompts produced the plugin structure, the PHP row-action code and the clipboard JavaScript, including a fallback he never asked for. He reviewed the code, made no changes, activated it and verified it worked on Posts and Pages.
Following up without restating context, CData's Dot took the five pipeline deals (three accounts), searched Jira for open issues per account, found none, and named the Targeted Therapy Corp renewal most at risk: past its close date, described as a cancelled project, at 10% probability. The post says the same answer by hand can take days of asking engineering and searching two systems.
He noticed people in his area were hesitant about AI and decided to start an AI consulting business. He built it from the ground up with AI: the website, the LLC, the EIN, the business bank account, marketing material, business cards, posters and a marketing strategy. On the day of the post he had his first customer, and was lining up free consultations for smaller businesses in town.
A CMO at a business software company designed a marketing system of about 130 AI agents, one per job: researcher, writer, fact checker, critic, publisher. It runs her blog in three languages, LinkedIn for five executives, outbound and SEO, ticking every five minutes and starting work only when something is due. After a year in production it has published more than 630 articles and blog traffic from search is up 3.9 times. Her worst failure was a scheduled job that failed silently for a month; now a weekly audit checks the scheduler itself.
Ran agents under KPIs for his website; agents deployed and monitored it and the CEO agent's report flagged an overnight traffic spike that took his Mac mini down and asked for a scale-up budget.
A self-described desperate vibe coder commented in an Reddit showcase thread that the SaaS he built with AI, which auto-generates landing pages and tracks who will pay, made $16k in 3 days with 876 paying users. It spread via Reddit and Twitter and got noticed in a hackathon Discord.
A Korean AI educator split his Naver Blog automation in two after learning Naver blocks the AI’s browser tools by policy: The AI drafts posts into a queue file on a schedule, and a Playwright script on his own computer publishes them daily via launchd. Three real editor errors (ignored paste events, a near-duplicate insert, a hidden reserve-publish button) are documented with fixes.
Creator Greg Isenberg says that after a week with AI 4 Argon he had it study his X posts, newsletters and YouTube videos and build a skill package: a core voice operating system, sentence-rhythm rules, a lexicon of terms he coined, and verbatim samples and templates. A post it then wrote on marketing engineers was self-scored 9.2/10 by him.
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.