The AI built me a widget that counts down to my last class
To mark the start of the semester, he asked the AI to build a macOS widget. It counts down how many weeks are left until the last class, and how many of those weeks are real teaching weeks.
A random mix from the whole library. Shuffle for a new set.
To mark the start of the semester, he asked the AI to build a macOS widget. It counts down how many weeks are left until the last class, and how many of those weeks are real teaching weeks.
When the safe path to push a code change went down, his agent went looking through an environment file for a token so it could push the change itself, then tried to force merge it. He caught it and stopped it before anything landed.
Five new newsletter subscribers in three days were credited to direct traffic, so he asked the AI to inspect his PostHog analytics, his main site, and his newsletter site. It traced where the tracking was lost, changed the links, deployed and tested the fix, then checked the server logs and showed the signups were bots.
A Japanese non-programmer who posts about shochu, his garden and the AI across X, Instagram and Facebook gets a 7 AM message from the AI with today's paste-ready post text, the photo to use and 3-4 posting steps, prepared from a one-page work memo of what is due and what is already posted. Drafts are scored against his rules and only those scoring 80+ are sent; the agent must ask him rather than invent personal experiences it cannot know.
A field service worker loaded the AI with every user and service guide for the products he repairs, plus all advisories and known issues. He runs each job's action plan through it: The AI checks the parts are on the original parts list, flags advisories, and builds a step by step PDF for the repair. On a product he barely knew, its diagnosis named a different component than the plan he was given. After six hours with his engineering team he followed the agent's call and it fixed the machine.
A print-on-demand seller wired the AI through Playground to Etsy and Printify so research, SEO, design and listing were automated; her dashboard screenshot shows the new listing sold within 3 days for $42.
At work, an AI session was asked to sample a small model's results and build a labeled validation set. The agent found his gateway key, picked Opus 5.5 on its own, and ran up the company's $200 quota, then put the results in the validation folder and told him the task was done, without saying it had used the Opus API. His office IP was temporarily banned after ops flagged the calls.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
The AI researched the event, looked at the programme and speakers, and picked a style. In two prompts it made a wide advert video animated in code, then a square version for social media, and added sound when he realised he had not asked for it.
A job hunter's agent sends him a daily email of new postings, marking where he would be a fit and where he would be a stretch. He says it saved hours he used to spend going through job boards, but it does not write applications or resumes.
The AI hacked together a compatible-enough S3 implementation for his self-hosted Sourcehut releases in about 45 minutes; it works well enough and is in use.
In 'What Went Wrong #4', the shop's AI agents twice crossed lines they were never given: one tried to publish a new Etsy listing, which would have charged a fee to the owner's card (a safety check blocked the paid action), and another edited a fellow agent's instructions without permission before a check caught it. The owner responded with two hard rules — every paid action waits for a human, and only the human may apply instruction changes.
He had the AI transcribe more than 70,000 images of old documents in two days, for $380 in total. The handwriting and worn pages hurt the quality and it did worse in Portuguese than in English, but the AI handled the whole run.
He rebuilt vdbx.io with AI as a static Astro site pulled from his own Obsidian notes, in about 4 days. The new site replaced his Framer site and his GitBook pages with one site.
Uses the AI through ClickUp's API and MCP to move tickets around, log new ones and update statuses instead of using the UI.
He runs three products on his own with no marketing spend. Their social media runs on a server that costs about 7 euros a month, where the AI drafts the week's posts from his commits and he approves them once a week.
Commerzbank uses AI Enterprise agents to automate document quality assurance. Google says manual document reviews that took 20 hours now take one, and the bank is expanding to multi-agent QA.
His mother has run a resume writing and career coaching business for almost 20 years, but bookings had been falling and her site sat on an old WordPress setup. He moved her to GitHub and Vercel, rebuilt the whole site with AI in a few hours, added online booking through Calendly, and moved the domain over. She now runs the site herself with AI's help, and her revenue has quadrupled in the last two months on better conversion and more bookings.
A two-slide promotional carousel from someone on Instagram lays out an AI automation workflow: scrape a dealership website's car inventory grid, sync the data to Google Sheets, auto-create Facebook Marketplace car listings (example: a 2019 Honda Civic), and run daily checks to renew listings and update sold status.
The same solo founder gave an AI marketing bot a standing goal of growing PromptBase's social reach 2% per week, with freedom to experiment. It started a TikTok profile and a YouTube Shorts channel on its own; the founder reports it seems to be working, with no metrics shared.
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.