In Tye Gordon's Lauderhill businesses, the agent roster includes Hope, whose whole lane is the gap after the AI receptionist takes a call: Hope follows up on those calls and sends the customer update texts and thank-you emails, each one approved by Tye first. The design point is that an answered call is not a finished job — somebody still has to close the loop with the customer, and that somebody is an agent working from the call record.
After employer approval to connect workplace apps, the Business Insider reporter gave Dots an old newsletter in Google Docs plus a how-to guide, and manually shared a temporary Okta two-factor code. The Dot produced a test newsletter draft in about five minutes that an editor called 95% good, with photos, a chart and links in the right places.
The same business owner runs AI on a weekly cycle for search traffic. It scans competitor sites for the keywords he tracks, compares his own pages against them, suggests changes, and finds and puts in internal linking fixes itself.
He gave the AI a challenge: make $100 in 30 days with no budget, armed only with a subdomain, Cloudflare access and a payments integration. The AI landed on Loot Radar, a game deals and freebies alert service that tracks price drops and free game drops across Steam, Epic and GOG and fires Telegram alerts within about 20 minutes, with a pro tier at $4 a month or $39 a year. The site is live and taking sign ups, payments wired. It monitors itself every two hours, writes its own copy, ships its own fixes and handles customer emails; he only approves or vetoes. The $100 itself had not landed yet on day one, so the outcome is partial.
This only partly worked. Worth knowing before you try it.
With the AI doing the grunt work he built a compiler for his own language that runs on Mac, Linux and Windows and cross-produces for those plus WASM, iOS, Android, Zynq, m68k and 6502, with a signed binary running on his phone.
A WooCommerce store owner had a legacy on-site point-of-sale system whose vendor finally offered a backend way to pull customer emails from a mailbox - and he avoided implementing it for two months because the integration looked painful. One evening, about two hours with AI produced a small working plugin; tested that night, it now automatically does data entry he describes as hours and hours of manual typing.
His business scans every invoice into a Google Drive folder. The AI reconciles them there: checking they were billed correctly, that credits for returned goods actually arrived, and that there is no slippage. He says it is far faster than reconciling by hand.
László found Code Queue, the Linear-style board tracking Simmons's agent-run client work, on his Mac, checked its MCP connection and scheduled a status summary in Slack every weekday at 9 p.m. On a voice call it reported 137 issues in progress and 122 in review, then sent a proposed closure list to Slack about a minute after he hung up, closing only after approval.
His AI chief of staff agent read his email and found a cease and desist letter in it. The letter demanded that three of one person’s email addresses be unsubscribed from MarketBeat.
This only partly worked. Worth knowing before you try it.
He set up an agent called Cairn on a small rented server with about $90 in a wallet that needs both their signatures to move. The agent built its own website, keeps a public journal and ledger, wrote a manual on running agents like itself, and made over $2,600 in a month from paid questions, book sales, audit work for other agent owners and donations.
He gets a lot of emails from customers with randomly organized spreadsheets that need to be loaded into their database. Now he can just say grab so-and-so’s email and work it. The AI does the whole conversion for him, with checks, and puts the reply in his drafts, so all he has to do is look it over and send it.
Threw plain the AI at a git repo move with no skills or tools and finished in 20 minutes while multitasking, while a senior spent 6 to 8 weeks building the AI skills for the same job and moved only 5 repos.
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.
This only partly worked. Worth knowing before you try it.
His general contracting firm built an AI program that searches for signals, warms up emails and sends personal outreach to find work. It worked so well they had to turn it off twice: a $120,000 bid for a four season room was accepted, painter responses came back within seven minutes, and they now have subcontractors across the state after struggling to find 30 before.
The AI modernized a 22-year-old legacy system, updating dependencies, auditing security and adding features, raising test coverage from about 65 percent to about 80 percent.
His weekly reporting agent builds the report and then reviews only the rows that do not match between systems, which is the part he used to check by hand. His own review is down to about 15 minutes a week and the setup has run for months.
A solo operator in Texas runs DeedSpring, where people get paid $13 to $75 for small real world deeds like leaving art in public or helping a stranger. His agent Vesper runs the desk: recruiting, acknowledgments, checking photo evidence against the posted rules, and payouts through escrow. Six deeds have been paid so far across five countries, and every receipt sits on a public ledger.
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.