A random mix from the whole library. Shuffle for a new set.
He had the AI build a website for his friend's painting company, then guide him from GitHub to Cloudflare and finish the SEO. His friend can now get changes made just by asking the AI, instead of paying someone each time.
The same user asked his Dot to publish one article to his own site, DEV and Medium; the workflow had worked in one shot the previous week. This time the cloud GitHub connection could not write to his blog repository and the file-transfer helper failed on Windows, so he ended up coordinating the handoffs himself and told it to start over locally.
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.
AI manager agents review transcripts of every demo call, compile insights, update company docs and give feedback to the humans who ran the demos; agents own KPIs and escalate to humans.
He built Stackboard, a free Chrome new tab page that shows your own bookmarks as a board. An AI runs its growth on a schedule: it scans Reddit every hour, answers replies to them first, drafts posts, and checks daily caps per channel before anything goes out. The caps live in code, and the agent backs off on its own when a sub removes something. In week five the project had 96 weekly users, up from 4 at the start, and 3 ratings.
He is not a developer; he is a management trainee at a company made up of five entities. Every quarter, full financials go out to the management team: a batch of at least 25 PDFs from their SAGE accounting software, several of them over 20 pages long. He spent weeks reviewing test files and batches and debugging formatting mistakes to build his own converter, after finding that the AI models themselves could not do the job faithfully on larger documents: they would combine rows and categories or produce totals that added up but were not identical. The AI itself told him the task would need a hard coded deterministic app. His converter can now batch format the whole set at once, with the bolding, blank rows, sum lines and column sizing he is picky about.
A Chinese AI-tools blogger (Guizang, op7418) set the AI a scheduled task, live since Oct 9, 2026: every day at 9 AM it compiles yesterday's AI news from four sources (his new X bookmarks, found by diffing a daily bookmark-ID snapshot; a radar of his own posts ranked by likes + 2x comments + 2x saves, with substantive comments pulled in; his newsletters; AI news sites) into a 5 to 7 item brief under hard rules: every item links the original post, prices and benchmark numbers must be rechecked on official pages, rumors are labeled unverified, and an unreadable source is reported as 'not read', never merged with 'no update'. In the Oct 7 trial run (99 bookmarks filtered to 62 from that day, distilled to 7 storylines, about 6 minutes) the check caught a circulating price error: Nano Banana 2.1's 4K price was $0.113 on Google's pricing page, not the widely shared $0.076. The same pipeline produced a 90-second narrated video (TTS per sentence, React + GSAP scenes, Playwright frame capture, FFmpeg assembly, about 5 machine-minutes) entirely on the AI’s cloud VM; the video step is still a trial and not yet in the daily schedule. He published the full reusable prompt and his three open-source skills.
Pulse Research Lab sells to government and prime-contractor buyers who must ship orders on their own UPS/FedEx accounts through a $0 Ship Collect option, but Shopify disconnected the store's UPS account without any notification. After Kan set up a custom app granting the AI permission to edit theme code, the AI restored UPS in 10 minutes, rewrote the collect-shipping pre-checkout cart flow in under an hour, and the store now uses Shopify Flow to store each customer's carrier, account number and service level, pre-populated on new orders.
Gave the AI a classification task he had been doing by hand and it raised accuracy from 81.3/89.9/92.1 to 90.9/99.3/99.6 in a little over 2 hours.
He edits video for a living. His agents handle the non creative manual work around each project: quality checks for missed text, black frames and logos, subtitles, reformatting for social aspect ratios, spreading files to the right products and pulling source material for descriptions. He logs his hours on every project and says agent assisted projects take about half the time with better quality; the weeks spent building the setup paid for themselves in two weeks.
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.