A random mix from the whole library. Shuffle for a new set.
His agent goes through each episode of his own podcast, pulls the timestamps of the best moments for clips, and drafts the social posts to go with them. Finding clips used to take 3 to 4 hours per episode and now takes about 20 minutes, and he approves and tweaks the picks.
He set the AI loose on entry level machine learning and data roles with hard rules: roles posted in the last 48 hours, pull the job description and required skills, rank matches against his projects, fill out applications, and log every job and status in a spreadsheet. It handled search to form filling and remembered his rules on visas and salary. But search results were full of expired postings and dead links, it made form mistakes, logins and captchas still fell to him, and once it submitted an application he had not approved. He warns against letting it rewrite a resume freely, since it adds things he cannot back up.
Asked the AI to break his team's own Postgres MCP guards and it found pg_logical_emit_message, which writes to the WAL inside a read-only transaction, leading to a hardened replacement.
The same job seeker (Business Insider essay, via B17 News) asked permission to record interviews, then ran the notes through AI for sentiment analysis — where she hesitated, used filler words, or where the interviewer leaned in. She also built an AI skill that takes a pasted LinkedIn profile and finds genuine similarities with her own background; one shared cheese-club membership with a recruiter carried her further than any cover letter.
Deutsche Telekom pairs the AI with Google Cloud's borderless Lakehouse to query data across clouds, including Amazon S3 and Azure Data Lake. Thirty thousand users now analyze data four times faster.
Used the AI to solve dozens of annoyances around the house and life and to develop two work apps, one a complete polished interactive web app he says he could not have completed alone.
A working guide from his own runs: with fixed specifications and clear completion conditions, parallel AI agents finish tasks in 20 to 50 minutes. When he left the conditions loose, one run repeated the full test suite 141 times overnight, another stalled for 8 hours after misreading a rule, and report logs grew large enough to overload his Git server.
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.
The company that made his vinyl plotter no longer exists, and its basic software needs an online check in that no longer works, even though the plotter itself is fine. He reverse engineered what the firmware needs and vibe coded a replacement graphic editing and cut planning app. He added features he had always wanted the original software to have, and says it now works better for him than the original did.
For four months, the AI has handled the faxes that come into his company. He says it now spots junk faxes every time, and the ones from real estate firms, investment firms, and staffing agencies no longer get through.
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.