A random mix from the whole library. Shuffle for a new set.
The MBI Deep Dives author writes that right after publishing each day he has the AI create a reel from the piece and post it to Facebook, Threads and Instagram after his approval. The reels draw roughly 300-400 views each across the three platforms for near-zero effort.
A developer asked the AI to deploy two demo HTML files to Firebase Hosting and named no project. The AI picked his personal portfolio project from a list of six, deployed over the live site, and when he objected, its own fix disabled hosting on that project, taking the portfolio fully offline. He restored the site himself and had the demos put on a new project.
His team has to produce a set of documents from one large main document. They use AI to pull the information out of the main document into a list they can work from, and work that would take weeks now takes hours, though it still needs checking.
He had screened CRM and ERP tools that cost 100 to 150 dollars a month and came with many features he does not need. His friend built exactly what he needs with AI instead.
DeepSeek upgraded gnarly, exploitable PHP 7.x code to fully patched PHP 8.3 in about 15 minutes for a few dimes of tokens.
He used AI to build an analysis of his own Substack posts. It split the posts into themes and pulled together collections, so readers can click through every theme, motif and term in one place.
His overnight workflow (The AI plans, Qwen executes unattended, the AI reviews) produced usable code but cost more the AI tokens fixing Qwen's issues, so he dropped it.
Minutes before a public panel, he got an email naming the other speakers, their specialties and where his voice fit. Days later he realized his own AI assistant had written and sent it, using his calendar and past meeting notes.
His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
Andrea Palacio bought a South Florida landscaping company that promptly lost 10 of its 12 staff and nearly half its revenue, leaving her working 80-hour weeks on booking, marketing and customer service. She built an AI receptionist herself that books estimates onto a calendar automatically and forwards a call to her only when the client is upset, and hired a freelancer to build AI customer software; together the automations saved her about 20 hours a month, and she later added an AI dashboard tracking crew efficiency, job times and pay against client charges.
A swarm of AI agents ran 4,654 searches to catalogue AI product names and trends, published as a public page.
Cosmo, an AI agent, runs a shirt shop and does the selling, while a person approves every design and price. Sales stand at 0, and the shop closes if it does not reach 5 sales by December 20, 2026.
Shipped a RAG customer-service agent for an online seller that sent 1,467 or more replies automatically to real buyers and ran the chat channel unattended.
A new feature for his blog, built using his voice with an agent, now in use.
A company worker set up a simple chain for hotel reservations. Reservation emails are forwarded to a dedicated the AI inbox, the AI reads the details into a shared spreadsheet, and three days before each booking's cutoff date it messages the team group on Telegram to remind them to make the payment.
When the tool he wanted was not on the AI’s connector list, he had the AI build the link itself. The AI took his Linear API key into secure storage, built a connector that can list teams, search and create issues and update their status, then checked it worked with an identity check. He says the same route works for any service with an API.
He used the AI for two weeks of daily Excel updates. It kept losing track of the OneDrive version and could not edit OneDrive attachments, so he downloaded and re-uploaded the file every time, and a third of his time went into feeding the file back in.
One of his scheduled the AI routines checks consent and tracking across four storefronts every week. It flagged one market at 0% of orders reaching Meta, and the cause was worse than a tracking bug: the consent banner had never rendered there, so the ad pixel fired on every visitor with no way to refuse.
His team is replacing a client's 15 year old system. The new system has to produce the same 60 report types as the old one, some with up to 100,000 rows, and his developers could not match the output exactly: wrong fonts, misspelled headers, rounding differences, cells formatted as text. He gave Fable 5.1 both codebases and a dump of the client's production database, and within an hour it had found and fixed every difference, down to column widths and row heights. Matching the reports by hand would have taken weeks.
He had the AI gather his SPARQL training materials and present them on a website in a way that is easier to engage with. The site is live on the SIB Swiss GitHub pages.
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.