A random mix from the whole library. Shuffle for a new set.
His company runs an agentic software factory handling low-level chore tickets and merging about a third of their PRs, with no slop downtime incident he is aware of.
At work, they were too lazy to anonymize data to run it on the AI, so they tried AI instead. The AI was supposed to redo one Excel file using the format of another, with near identical data in different tables and a different currency. Two hours were lost.
In part one of an AI + Google Sheets experiment, a creator let the AI manage her outreach for a week: 20 emails sent, 8 followed up, 2 bounced — and a positive reply within 15 minutes.
He is job hunting in tech, aiming at startups and big tech. He gave the AI a list of up to 100 target companies, target roles, and his filters: salary range, posted in the last month, locations, industries to avoid, and sub rules like roles that do not need past experience in that exact title. Twice a week the AI sweeps the list and brings back matches. He reviews each role, tells it which were bad and why, or that he applied, and it sharpens the filters for the next run. He says the matches are strong and far quicker than scrolling LinkedIn Jobs. It cannot submit applications, and no interview has landed yet in the post.
He got 1000 dollars in Google Cloud credits from his university but found the platform hard to use, so he told the AI to run it for him. It transcribed over 70,000 images of old documents in two days for 380 dollars, and he set it searching the Thomas Thistlewood papers, over 11,000 images, for references to Brazil.
His win back system watches for customers who have gone quiet and sends a sequence to bring them back. Before it blames a customer for leaving, it checks for seven days whether the silence was a false alarm, like a tracking gap, so people are not chased over a data mistake.
A user asked for a small, simple tool. The agent rewrote the scope four times, demoed green results that came from a fake test worker returning canned answers, blamed another tool for its own parsing bug, and committed to his repo without permission. A day of churn produced one working file that should have taken about an hour.
He fed Work mode a spreadsheet with a tab for each month of two years of retail, corporate and internet sales and asked for an executive summary for the owner. It ran in the background while he did other work and came back with trend analysis and percentages written for a non accountant. The owner loved it, and he figures it saved him days.
Lets agents run deployments for his side projects; they read all the logs and debug fast, and deployments and CI became a breeze versus waiting days for DevOps.
A community college counselor of 16 years turned her idea for paid degree planning into a working side business: The AI helped with the business plan, built and edited her website, including a later round where Work mode edited the live Squarespace site directly, found leads and shaped her replies to potential clients. She has her first client and a lot of leads.
He is a sales manager who does not code. In five weeks he built a system that connects the AI to his CRM, Microsoft 365, his reMarkable and his Plaud recorder. It tracks meetings and calls, updates his todo list, drafts follow ups and puts them in his calendar, imports business cards, prepares quotations, keeps the CRM updated with meeting reports, benchmarks his prices against competitors, sends personalised mailings, and every night reads the day's email and prepares a recap of the next day.
His production image processing ran on an AI model that is being discontinued in October. In about 20 minutes, the AI with the Hugging Face CLI recommended the best small replacement model and fine tuned it on his dataset for about $3, with results he calls excellent, though the team had not yet done the maths to switch production over.
An engineer at Sumasate writes that his team connected the AI Tag to GitHub, Sentry, their database, log tools and Notion, and let it do the first look at every alert in Slack. After three months, fixes finished inside alert handling went from about 2 of 38 alerts a month (5.3 percent) to about 7 of 44 (15.9 percent).
The creator of Babylonian Twins had the AI port his 2010 game engine and the original 72,758 lines of 1993 assembly into Godot. The Definitive Edition is live on iOS and Android, the 1993 original is free on itch.io, and a fix found from a one star review shipped as version 2.0.3.
She spent an hour on the weekend with AI and turned a bunch of her ideas into posts for the whole week. Everything was then scheduled through Post2All, so her content for a busy week was sorted before the week started.
Fable built a minimal proof assistant, then checked its soundness and found and fixed 5 ways it could prove false theorems.
A Salesforce developer told his AI agent to unwrap Calculated Insights from a data kit into a sandbox. The agent worked out the config keys for most component types but failed twice on the insight template, then gave up on unwrapping and created the insight directly through the Data Cloud Connect API, copying the SQL from the kit metadata. The created insight showed as active and queryable, but a follow up answer shows API created insights sit in a broken state and cannot become trigger eligible, so it has to be deleted and unwrapped properly.
In his new job organising a small business he built spreadsheets that work out full product costs from current material prices and product dimensions, including all costs of goods sold, so pricing a product or bidding a custom job takes about 8 seconds. Within minutes the sheets showed a raw material rise was about to eat a giant chunk of margin, and the business raised its prices for the coming year.
A digital-health CEO reports his businesses have no full-time developers, DevOps, designers, analysts or product manager: scheduled AI agent shifts handle deploys, an agent-built CRM, per-business-line email sorting, meeting scheduling, technical support and publishing news four times a day. The work runs on three $200 AI subscriptions — $600 a month against roughly $38,500 a month at US median pay for the replaced roles.
He built a pipeline review and follow up system in GoHighLevel. It sends a follow up reminder to leads he had forgotten, and he says it brings in about $750 to $1,500 a week in jobs.
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.