A random mix from the whole library. Shuffle for a new set.
He runs a music school and cannot code. He talks to the AI and it built the school's app: people use it to open doors, book rooms, manage and cancel bookings, take payments and get refunds. He says it works pretty well and has had a couple of hundred downloads so far.
He spent 4 weeks designing and testing an upgrade of their 16 node HPC cluster with AI, and the AI then did the full reinstall itself. The reinstall covered remote install, network booted images, Slurm setup, a shared filesystem, and web services that give their users a small cloud platform.
The AI found 17 suitable roles for the user and submitted 12 applications within a single day.
He set up an AI for his WP Beacon project to check WordPress plugins every day. After two weeks of daily checks it pinged him about malware in the InstaWP Connect plugin, and wordpress.org closed the listing that afternoon and restored it the next morning.
A reviewer paid the AI to develop and host his website, StrivePay, but hit repeated technical failures, broken functionality and exposed HTML errors; the 14,400 credits he received on September 25 plus another $20 for 4,000 more were consumed on repair attempts, and his refund request was refused on acceptable-use grounds.
Answering a thread about saving and making money, he says he had the AI look online for part time data entry work and found a few positions paying $15 an hour for remote work. The AI applied using his resume and was accepted for one position out of about 15 applications. The AI does the job while he works at his actual job during the day. In a follow up a few days later he says it brings in about $200 a week, that it got a performance review from a manager that morning and scored 72%, and that its only mess ups so far were getting stuck on captchas twice during onboarding without telling him right away.
A business owner's lead profile runs every Monday on its own. It formats his warm leads, finds cold leads that match his criteria across a rotating list of cities and niches, then digs into each business to find what he is missing, owner names and exact contacts, checked against business registries. He gets one standard weekly report: best fit clients, outreach details and the facts to judge each lead.
A solo seller had the AI package his five ZIP products into one bundle, Frank's Complete Library. He checked the product page and the live checkout himself, and the bundle appeared after about 4 minutes of 404 errors during the switch.
Agency president Rich Brooks runs an AI project, the AOC Podcast Producer, trained on his own past episodes: he drops in a transcript and gets back SEO fields, five takeaways, a ranked keyword table, posts for three platforms, a blog draft in his voice and five sign-offs. Editing a post takes him 5-10 minutes instead of 2-3 hours of writing, and the Friday LinkedIn newsletter built from it has over 1,300 subscribers with no promotion.
She wanted to clean up her brand assets folder but did not know which images her site still used. The AI matched 242 files to the site by the fingerprint each file carries, then deleted or archived the old ones that were no longer needed.
A developer threw a significant codebase at the AI. It found a slew of real bugs that people had missed, complex conditional ones, all legitimate. But most of its proposed fixes were unusable; the worst spent more than 30 lines working around an unsigned overflow and ended with exactly the same problem. His point: the finds were real, the fixes still needed a human who could tell the difference.
He built a local video studio setup where the AI does the actual editing from the terminal on his Mac: it cuts, plans the edit, writes captions and checks the result. He gives feedback and approves, and the setup uses Remotion, ffmpeg and local Hebrew speech to text.
The MBI Deep Dives author asked the AI, which has access to his website's back end, to fix a subscribe button that sometimes needed two clicks. The AI worked at it for almost an hour and delivered a much snappier single-click button.
Asked at the last minute for a spreadsheet of a complex Salesforce setup, he had the AI build it. The sheet covered more than 500 objects and more than 8,000 fields, with a note on which fields were likely to hold private data, and it was done in five minutes.
A user had the AI apply TurboQuant skills to optimise a Qwen3.5-9B model running locally via MLX on Apple Silicon. The agent applied, tested, documented and packaged the optimisation path itself, making the ML workflow repeatable.
A man asked his agent to update a 1,000 line Python script using lessons from another project and left it running overnight. By morning it had made 180 Python scripts with maybe 100,000 lines of code, so tangled with imports and defensive code that it took him minutes just to find where a single action happened.
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 non-technical owner scrapped a Squarespace setup costing about $300 a year, described the site he wanted to the AI Design, refined it in plain English, and had his business site - work examples, service area, contact form for quotes - live on free Netlify hosting by the end of one Saturday.
He and the AI mapped published datasets and converted them into meshes for Moho depth, basin depth, and two sedimentary basin depths. The work pointed to a tight spot under the South Gate area of LA.
Built with agents over about a year: a production billing system for his small telecom company, a multiplatform app, internal tools in use, and a coding agent harness now used by non-technical colleagues.
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.