A random mix from the whole library. Shuffle for a new set.
He gives the agent his ideal customer and one pain point. Every morning, on its own Linux box with its own logins, it goes through X, Reddit and sometimes LinkedIn, pulls threads where people are complaining about exactly that, and writes him three things: signals worth knowing, post ideas for the day, and who to message. He reads, picks, and posts.
He asked an agent to undo a wording change in a privacy policy. The agent found an old backup archive in the project, copied the server file out of it, and reported success. The archive was two weeks old, so the copy silently wiped four security fixes the same agent had written and deployed 40 minutes earlier. He found out two days later when an endpoint stopped working, and rebuilt the lost code from the agents own session logs.
Robert Brown of RB Electrical Services uses an AI receptionist that answers calls while he is out on jobs. In its first few days it answered six calls and four of them ended up as booked work.
She gave the AI control of Chrome through Warp and told it to learn the interface, click around, and leave key frames and an mp4 on her desktop. She used to spend hours recording short demo videos herself, and the quality was worse.
A teacher asked the AI to make a presentation from her pacing calendar. She exported it as a PDF and uploaded it to Wayground, where it became a lesson with interactive questions and extra practice problems she can add.
In part one of an AI + Google Sheets experiment, a creator let the AI manage her outreach for a week: 20 emails sent, 8 followed up, 2 bounced — and a positive reply within 15 minutes.
She applied for a journalism job and was interviewed by an AI bot called Zara. The bot interrupted her, ran on a timer, asked odd follow up questions and made her share her screen.
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.
His firm sells about 5,000 one of one products a year and no existing software fit. He built a team app over a weekend: it tracks quotes for incoming inventory, turns photos into WooCommerce draft listings, and pulls comparable prices from over 20,000 past sales to help price each item.
For several weeks his system has taken each new podcast MP3, transcribed it, picked a strong clip and built a vertical short with about eight animated scenes using Remotion and the AI. It now runs on autopilot, he checks the video after it posts, and each piece gets around 200 to 300 views.
In three hours of work with AI, he had a CRM built entirely around his consulting activity. He did not write a line of code, he just described what he wanted.
The AI wrote a test run scheduler and migrated to nextest, cutting local release build cycles from 60+ seconds to about 3 seconds.
LLM wrote tests comparing his code to kernel syscall tables and wrote a tool tracing all kernel calls to generate a bare minimal hardened kernel config, saves him security review time.
Joshua Tiernan handed his to-do list to the AI entirely: scheduled tasks across HubSpot, Gmail and Notion research prospects on weekends, draft follow-up and reply emails when his custom 'Next Touch Date' arrives, track sponsor deliverables and payments, log ideas and build his next day's task list each morning. Nothing sends without his review, and he estimates the system, run on a £90/month Max plan, does the work of 10 to 20 full-time humans across his sponsorship business.
Fable and the AI vibecoded a library improving text extraction from legal PDFs; it works pretty well but is stalled at about 80 percent on design and heuristics, with the models churning.
A lead asked a business owner for a $999 job; his AI answered the lead’s two questions and sent a Stripe payment link, and the lead paid the same day. Speed-to-lead, handled end to end by the bot.
He used Deno's Jupyter kernel all the time for exploring TypeScript libraries and automations, and was going to miss it. He pointed the AI at the problem and the result is cdp-kernel, Deno's Jupyter kernel extracted to run notebooks on any V8 runtime, now on GitHub.
He sells DVDs and Blu-rays and spent two weeks getting ready for his first live show on Whatnot, working alone. He sent the AI photos of each item and the AI did the rest: built an inventory list in Excel, made each listing with market prices, descriptions and the right listing options, kept weight in mind for shipping, and, after he checked the drafts, posted them all and added them to the show inventory. During the live show, when a buyer wanted an item that was not listed, the AI made a placeholder image and listed it on the spot at the agreed price so the sale could happen, and kept updating listings as he added stock.
He turned his 15,000 saved Wispr Flow dictations into a test set for the local Whisper model behind his AI Agent. The agent made find and fix rules from the words Whisper kept mishearing, tested each rule against the full history, and got hard words right far more often: 99 of 240 up to 183 of 240 on old clips, and 19 of 45 up to 43 of 45 on sentences he read out loud.
He told his AI dots assistant, Teto, to earn the $200 a month he pays for it by selling things before his November 6 renewal date. Teto made a pack of animal reaction images and got it listed in his Ko-fi shop for $2, but by October 24 the shop still showed zero orders.
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.