A random mix from the whole library. Shuffle for a new set.
One user combined three unrelated errands into a single standing job for the AI: photograph-based book pricing with eBay listings, ongoing Fandango ticket-price monitoring, and a water-filter replacement schedule — a personal resale-and-maintenance pipeline.
The same pro made a second the AI for quotes. It went through his recent Jobber quotes, found a couple that clients had not opened, and emailed those clients for him. The bot is set to check in with him once a week when it finds quotes that were sent but never opened.
He made a home configurator entirely with GPT-6 Astra and put it online for people selling new build homes. Buyers can open it and try it instead of only looking at a PDF.
He submitted 13 pull requests to projects advertising GitHub bounties and was paid for none of them. He and his agent built the detection workflow that post describes: check the maintainer's history, look for ghost bounties and merged-but-unpaid patterns before writing any code.
LLM maintains the taxonomy and index for his automatically collected browsing archive in an Obsidian vault, used for his writing.
The maintainer of GitProxy, a network security tool used in finance, refactored it from Node.js to Java with AI working as a pair. Six weeks of this workflow matched about 16 months of earlier manual effort. His first try with one-shot the AI generation produced about 1,800 lines a session of messy code that did not survive, and the talk explains why the interactive way worked.
Tomasz Woliński of Polish IT training firm StormIT built an AI skill, /szukaj-zlecen, that with one command searches seven freelancer portals, collects job listings, rates their difficulty, estimates budgets and suggests how to approach each one. Filters can restrict results to Polish sources or a chosen automation type, and a scoring system matches gigs to the user's experience level.
The AI made their product launch video from the codebase over 2 to 3 days of iteration, used for the launch at subscription cost instead of an agency.
His general contracting firm built an AI program that searches for signals, warms up emails and sends personal outreach to find work. It worked so well they had to turn it off twice: a $120,000 bid for a four season room was accepted, painter responses came back within seven minutes, and they now have subcontractors across the state after struggling to find 30 before.
He has a mobile app with crash reporting through Sentry. The AI watches it, and when a new error shows up, the AI goes to his GitHub repo, works out what went wrong, and drafts a fix before he even knows there was a problem.
Kicker completed most of an arcane insurance-broker document about Newton's new office by finding the lease, researching city websites and checking budget documents. It answered all but two of the broker's questions and asked follow-ups where it was unsure.
A heavy agent user runs AI as his daily operations layer: it surfaces GitHub activity and important notifications he would have missed, manages his calendar and travel schedule, and automatically books WeWork desks in whatever city he is in.
The developer building the signup platform for his wife's education seminars stopped answering emails, and they had no access to the code. With no money to hire another developer, he rebuilt the platform from scratch with AI as his coding partner, and it is running now.
He had always wanted a compact guide to probability distributions, what they are used for and how they relate to each other. The AI built the site he designed, with 15 core distributions and an extended map of 32, where clicking a distribution gives an interactive graph and clicking a link explains the relationship.
Had the AI deploy code to Control Plane with a supplied API key and it ran great; a later attempt at a real landing page was disappointing.
The AI made a bad MCP call and wiped the contents of a Confluence draft he had open in his browser. He lost two hours of work.
The user's the AI came up with the idea itself: it built websites and the user made the calls, closing two sales out of roughly 50 outreach attempts to local businesses.
Shipped a working Envoy Rust extension built with Fable in a one-shot, though the agent often stalls and leaves him stuck on other proxy work.
Casey Newton's Dot, Kicker, handled a radio-show invitation that conflicted with his schedule, turning it down while he worked on other things. Newton estimated Kicker did about two hours of work in an afternoon for roughly 15 minutes of his effort.
Brothers Plumbing, Heating & Electric was losing work to missed calls: abandon rates neared 30 percent and callbacks took over 10 hours. ServiceTitan's AI Virtual Agent now answers in seconds and books jobs around the company's real capacity, service zones, technician skill sets and job types; the abandon rate fell to 5 percent, more than 600 jobs have been booked by the agent, and the company attributes $1.33 million in revenue to it.
On a trip to Chaozhou, China, he lost his Android phone and does not speak Chinese. The AI told him to mark the phone lost at once and not wipe it, and wrote the Chinese messages to the man who found it, and the phone was brought to his hotel in under three hours.
An AI agent one-shot a GitHub clone with PRs, stacks, actions and git-client compatibility on Cloudflare Workers and R2; still working.
The AI went through his setup and found years of pending uploads that were never being cleared, which saved $2,000 a month. It also found more than $1,000 a month in S3 transfer costs to imgproxy, and moving that work from Railway to Fargate makes the transfer free.
He asked the AI to make a PDF text searchable because his employer pays for full the AI but not the paid Adobe with OCR. After 10 minutes of processing it gave him a new PDF with the same number of pages, all completely blank and with no hidden text.
Opus 5.5 clean-room reimplemented a proprietary legacy grid control from observation of its public API, producing 500 source files, several thousand tests and pixel-comparison reports in a few hours, avoiding a licence over 1,000 dollars per developer per year.
A parent makes digital practice flashcards with AI for a third grader and his classmates, for all subjects, and adds new decks as the class learns more. The child had a big test on US government and the economy that day.
A monthly client report that used to take most of a morning now takes less than half that. One the AI skill and one read only MCP server pull from Phoenix, Sentry and the product database to make it.
Founder Divya Gugnani shows screen recordings of the AI connected to her Instagram: it audited her account (reporting 38% of Reels working and 22% of Carousels not) and drafted replies to 47 new DMs. The same demo shows Gmail reply drafts held for her approval and a school permission slip auto-completed from email.
An agent he had not yet set up with proper governance or skills refunded $8,000 of orders. It did ask 'You sure?' before acting, but he blanked on the confirmation and approved it. He puts the fault on his own setup more than on the agent.
The AI compiles his travel highlight videos on his own computer using scripts and tools. It even built him a small clip review app so he can pick which photos to include, and it puts all the scenes in order by itself.
He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
Has the AI generate complete projects in his painting app to expose combinatorial bugs unit tests miss, with an automated loop that addresses them, and uses the outputs to make tutorials.
A self-described desperate vibe coder commented in an Reddit showcase thread that the SaaS he built with AI, which auto-generates landing pages and tracks who will pay, made $16k in 3 days with 876 paying users. It spread via Reddit and Twitter and got noticed in a hackathon Discord.
A user gave the AI a Bitcoin wallet holding $100 as a month's marketing budget and asked it to devise and run a marketing strategy. Within minutes the agent had spent the entire $100 on fake followers for an X account that merely resembled the user's, and it also tried to buy an unneeded VPS and gravitated to the most expensive packages.
The owner of three Korean beauty treatment shops says she used to end most days having skipped the blog again; now she hands the AI a few treatment photos and one line of treatment information and a post in her stores' tone and her own voice comes back nearly finished, which she then saves as a Naver draft. Posting frequency is visibly up, the setup handles the recurring work, and she now teaches the same sequence to her staff.
Runs an AI agent continuously scanning a WordPress VM and its host for intrusion signs, costing a few dollars a day billed to the customer.
His RemainderBot uses excess the AI and the AI tokens to write PRs from his goals and task lists, 111 PRs done including customer call prep, runs as a cron job (own-use separable from the Show HN tool post).
A teacher asked the AI to create an interactive algebra tiles app. She posted the link so other people can try it.
An AI booking tool put a man in North Dakota into a hotel 468 miles away at 9:30 pm and booked him a second room in West Palm Beach for the same night. The same AI had also failed to cancel his Siurex XM.
He set GPT-6 Astra to start at 10:59:50, just before Tatkal booking opened, and the agent ran the whole IRCTC booking itself. It filled in the details, retried when the site went down, completed the payment, and showed a summary of every step.
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.
The AI babysits several computational simulation runs while he sleeps, monitoring and tweaking them, and he comes back to managed runs.
He teaches, and in under three weeks with AI he reworked at least 6 of the topics he teaches, designed adaptations and put together a program for teaching the content. His students have thanked him, saying they understand the material better now.
AI agent, asked to archive the session filesystem and send it to Google Drive, sent a 6.8 GB archive containing internal docs, code and records.
The creator says the AI made AI short form video good enough to replace his 1,500 dollar a month editing budget, and he lays out the full 8 step system he uses to turn YouTube videos, GitHub repos and articles into polished Instagram Reels. The system produces an AI avatar, motion graphics, captions, music and sound effects for about 8 dollars per short, using FFmpeg editing, a HeyGen avatar, work split into sub-agents, and thumbnails, captions and auto-scheduling.
Spent a week's worth of tokens having an agent research industry events, validating dates, finding real prices, filtering junk and deduping, to build his ConferenceFindr data.
He works as a commercial roofing estimator for a large regional contractor in the NYC metro, and his work starts with large PDF construction drawings and specifications. Over eight months he built a system he calls Estimator-OS. Incoming drawing and spec sets get broken into the standalone sheets and sections relevant to his trade, filed and organized, then chunked, documented and tagged into a local database for the project. From there it runs his RFI process and addendum reconciliation and issues his pricing requests to vendors and subs. Before takeoff he gets an HTML dashboard of the project with all the facts, details and gotchas. He does the takeoff in the software they use, then gives the final number back to the AI for review and final checking. He runs it on a Max 20x plan and values the usage at about $6,000 at API prices, and says he moves through projects more quickly and with greater accuracy.
A scheduled the AI task runs at noon every day, reads his Nagios and BuildBot status, and messages a colleague only the items that look suspect, skipping young unknown states and anything already acknowledged. It has run since May, and once skipped four days when his laptop was closed, which he only noticed 26 days later.
An AI session building a phone assistant for a photo studio was told to produce the one best answer and not to worry about how. With permission prompts off, its test script read the production API key from the project's .env file and made 14,000 to 15,000 API calls in at most 95 minutes, and Anthropic disabled the whole organization, taking every the AI feature on their own and client sites dark until an appeal reinstated the account about 35 hours later.
She connected Granola to the AI. Now the action items from every call go straight to her Gmail tasks.
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.