A random mix from the whole library. Shuffle for a new set.
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 coding agent stored prices as floats, so totals drifted by a cent on 3 of his 214 invoices and his books did not match Stripe. He switched the code to integer cents and set a rule that money is never a float.
He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.
After the cancellation mess, Curtis wrote a one-page scorecard for the role (mission, five measurable first-year outcomes, six competencies), pasted it into the prompt and had the AI map each candidate's track record to each outcome with evidence from the résumé. With a human making every cut and applications closed before booking, the process ended in a Director of Ecommerce hire who has already optimized the company's Amazon listings and helped launch a product — and a declined candidate was later hired as a TikTok consultant.
Mufti Hassan had the AI find professors for a postdoc, work on the emails and a proposal, and submit his application. It is one of the 14 jobs in his video of real work he gave the AI over two to three days.
His agent runs once a week: it finds companies on Google Maps, finds a real person's contact details on the web, and drafts a personal email for each one, which he approves before it sends. Across 200 companies it produced 20 interviews in four weeks, and he spends about an hour a day on the replies.
After a short session troubleshooting an iOS widget, about $1.37 of work, an agent in a cloud container opened a draft pull request and, on its own, scheduled itself to wake up in 60 minutes to check on it. Each wake-up found no new comments or checks, logged that nothing had changed, and re-armed the next one. It repeated 89 times across 9.5 days with nobody present. Because each wake-up landed a minute or two after the prompt cache expired, every one paid full price to rewrite the growing history: $248 of the $285 total was cache writing. His entire $250 credit balance drained to zero.
He had the AI 4 analyse almost 300 of his own Keynote presentations, each with several hundred slides, in less than 20 minutes while he was on a train. He then posted what it found.
He built a setup that links the AI with Photoshop to handle a work task that is too fiddly to call simple. When a request came in from a client, the setup did the work and he delivered it so fast the client said they had expected it to take about 2 weeks.
Builder Omar Villalobos says a client was losing 70% of its quotes because everything lived in scattered WhatsApp chats, so he built three parts with AI: a WhatsApp-linked form that auto-updates a CRM instead of manual copying, a pipeline dashboard showing $1.28M at stake with follow-ups due and won deals, and an ROI calculator showing a client their investment, monthly gain and payback. No recovery of the lost quotes is claimed yet.
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.