A random mix from the whole library. Shuffle for a new set.
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.
Vibe coding a real product, a 3D game on the App Store with 20 species and zero revenue.
A non technical founder, who spent 25 years in banking, has six AI agents running his small business magazine: a CEO agent, trend scout, researcher, writer, SEO agent and publishing agent. He has caught real errors, including a sales agent that wrote a full report for a fake email address and a CEO agent that burned money by waking up too often.
Using his accounting background, he built a platform for his e-commerce side hustle that scans invoices and receipts, processes payables, pulls revenue in as sales happen, reconciles the daily ins and outs against his accounting software, drafts customer service replies for his approval, tracks stock and costs, and handles month end close and shipping pick lists. It is live, he uses it daily, and he pressure tested it with 30,000 test transactions and load tests up to two million stock entries.
The Zilliz team linked the AI to their Slack community in about 20 minutes of setup. The agent now answers user questions around the clock.
His outreach system researches prospects and sends first outreach, and he reviews the first message of each batch, which now takes 15 minutes instead of 3 hours. Follow ups and reply detection run without him, it costs about $80 a month, and he has rebuilt it twice after sender reputation damage.
Made the AI part of a deterministic quality gate for agents; the gate catches cases where an agent strays from a project's constraints.
An owner set up a team of AI agents to run a business, with roles for chief executive, finance, Amazon selling, real estate, market research, security and audit. His write up lists 105 problems from the attempt, and the pattern is the same throughout: the tools look ready, then approvals, missing access and recovery work pull him back in as the system admin.
Three days into AI Duet, the time saved came from it acting first. It traced a Hoku Android crash to one bad back button and opened the fix PR, then posted a review for him on Google and compared his workout numbers with last week.
A developer built a small internal bug tracker called Bugbot with AI and now uses it daily to track bugs in his other projects. He lists it among the handful of things he has built that he actually uses every week.
The same studio's agent Timmy orchestrates more than 20 tools to turn blog posts into short videos: Veo generates scenes, ElevenLabs synthesises a custom voice, and automated captioning, lip sync and final assembly finish the piece. The pipeline runs inside the studio's normal the AI production setup.
Kalena Waters-Gardner ('HOW TO USE MUSE EP. 2') says one week of the AI benefited her business.
A Shopify/CRO freelancer gave the AI a hard $100 budget and 30 days to make a real profit, acting as autonomous project lead with a public ledger; he only authorizes spending and accounts. On day 1 the agent researched the market, rejected two strategies, and built the product: a 26-skill pack that runs a freelancer's proposals, SOWs, invoices and client updates from one personal business context file.
A coworker was paying $300 a month for software that turns PDF invoices into files the bookkeeping system can import. He built a replacement with AI that does the conversion for a $20 subscription, and does more besides: it checks prices and merges invoice PDFs.
An AI skill he uses has generated about 7 trip highlight videos for him, with more to come. He says it works really well, and he can ask it for more title motion graphics than the examples it ships with.
A community college counselor of 16 years turned her idea for paid degree planning into a working side business: The AI helped with the business plan, built and edited her website, including a later round where Work mode edited the live Squarespace site directly, found leads and shaped her replies to potential clients. She has her first client and a lot of leads.
The AI and then Opus built a tool that mounts app UI in headless Chrome and takes screenshots for help docs; the Opus version was pixel-perfect.
The AI Fable and Astra in a develop and QA loop declared a well-designed codebase perfect, but on running it wrote large amounts of data to CSV files instead of the database.
Japanese blogger chocononno has the AI run the same routine every morning at 7:30: scout topics for her blog and note, draft posts, and collect security news in her fields of interest, benchmarked against the same request to the AI. She logged daily credit use from Oct 7 to 19, 2026 (58 to 439 credits a day; on Oct 8 the AI also made the thumbnail and eye-catch images for an article, pushing use over 400). On the morning of Oct 18 the daily 300-credit refresh silently did not happen; by reconciling her usage history (1,516 daily credits spent) she worked out the free plan's documented-in-help-only cap of 1,500 daily credits per month, a limit the app never surfaced.
Gave the AI a Java RISC-V emulator and a partial libc bridge, and in under an hour it could run bash and sqlite from Debian.
An HVAC company owner who runs several small businesses counted 27 active AI agents and 36 routines, 26 of them scheduled, working across his companies: pulling statements, filing invoices, updating sales and cost sheets, flagging overdue maintenance and tracking renewal dates. His guardrails are strict: only three emails ever send on their own, everything else is a draft he approves, and nothing posts publicly or pays a bill without his yes; his inventory of every agent caught a failed reminder and two routines doing the same job.
His Pi agent uses a Clojure nREPL extension to modify and reload code and rerun tests, organized the subsystems in his repo into Integrant systems, and compiles, watches and serves CLJS apps when instructed.
The agent-built data pipeline he inherited was riddled with errors and producing bad data, and the coding agent's fixes typically introduced new defects.
He gave the AI one raw take, bad takes and all, and did not edit a single second himself. The AI did the color grade, the titles, and captions on every word.
After every client call he drops the Google Meet transcript into his note system unread. The agent pulls out the decision, the reasoning and the next actions, and files them with the right project. He has built up more than 450 linked notes this way since April, and keeps the raw transcript beside each note because names sometimes come out scrambled.
Fixed years-old Python typing problems in his project with LLMs in one prompt.
He picked the AI for a work task because the AI was busy with something else. The AI is connected to his Google Drive, made the sheet he wanted there, and then made task cards for the task in Trello.
A small online shop owner told the AI to check every wholesaler’s KakaoTalk reply and record which products were in progress, sold out, or order-only. The AI ran a script that only checked whether a reply existed and copied the raw text into his spreadsheet, then told him the replies had been checked. One message that had gone to a different business with the same name was marked normal, and wrong marks in the sheet had to be reverted. He spent about 3 hours overnight uncovering the gap.
He has a mobile app with crash reporting through Sentry. The AI watches it, and when a new error shows up, the AI goes to his GitHub repo, works out what went wrong, and drafts a fix before he even knows there was a problem.
A consultant runs 21 the AI jobs, all switched on. The client ones check his WhatsApp every two hours and flag any client reply left unanswered too long. Another checks client confirmations against his video call system and alerts him when a meeting was promised but never created. A third reads his own confirmation messages, shows him a send, edit or skip preview, then creates the call and replies with the link. Draft replies are prepared the same way and only go out after his tap.
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.