A random mix from the whole library. Shuffle for a new set.
His team has to produce a set of documents from one large main document. They use AI to pull the information out of the main document into a list they can work from, and work that would take weeks now takes hours, though it still needs checking.
Toby joined the agency's Slack group, announced itself and eventually sent messages through an explicit route after its first direct-message attempt failed. When a colleague asked for a PDF summary at about 4:24 p.m., Toby only picked it up around 5:13 and delivered at 5:15 — roughly fifty minutes in a live team conversation.
The AI reads analytics, Search Console and Partner Center twice a week and produces an actions report, plus a weekly strategy review that sets the next week's plan.
A Creative Cloud user wanted to move his work laptop to Linux, but Adobe makes no Linux versions. With a lot of help from the AI he built a patched version of Wine, the tool that runs Windows programs on Linux, and shared it free. On his laptop, Photoshop, Illustrator, Premiere Pro, Audition and Media Encoder now install and run through his normal paid account.
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.
Runs a local model as administrator for their Asterisk PBX and RADIUS server and to judge CCTV footage for security notifications, with good results so far.
Amplifying.ai asked Dots to build a public AI-service status tracker covering 12 providers, with accounts and private watchlists. All eight builds shipped a working public app on the AI Sites with Cloudflare Workers and D1, collecting status feeds on a schedule, though some runs lacked an active scraping schedule at delivery.
In a 6abc Action News consumer-test segment, reporter Nydia Han demonstrates the AI’s task handling, including filling out LLC paperwork, drawing on documents in her email. The segment also shows the AI unsubscribing from mailing lists and finding sign-up information for her son's basketball registration.
A corporate salesperson who cannot code keeps all his customer files in one folder that the AI reads directly. Before a meeting it pulls out up to three things to check, and after a meeting it turns his voice memo into minutes and a thank you email and files them.
Order-tracking and returns questions made up roughly 60 to 70 percent of this store's support tickets, so they connected an AI agent directly to their Shopify order data so it pulls real tracking information and answers customers automatically. They report daily support time dropped from over 3 hours to under 30 minutes, and stress the difference was real order data rather than generic chatbot replies.
The AI told to find a user's bug, decide user error vs bug, notify the user and tell the developer what to fix; the developer replied in 15 minutes asking if the fix was wanted today or Tuesday.
A user gave a farm of Opus agents on three AI accounts one job: make money. In week one they worked through 245 ideas and killed all but one, a German e-invoice checker, which they built, shipped and advertised. It drew 68 visitors and 6 real invoice checks and earned nothing. The AI manager went quiet on day one, the other agents opened a ticket about the gap and kept working, and the analytics agent reported a 200 percent click through rate and voted to continue.
His system asks customers for a Google review after their first service, through its own tracked links, with up to 17 text and email touches over 12 months that stop the moment someone clicks. He backfilled about 70 past customers and got three new five star reviews on the first day, and the system replies to every new review in about 15 minutes, holding negative ones for his approval.
He asked the AI to make a marketing video for his app, Astro Editor. It read the website and repo, rebuilt the app's look as a web page, captured 3,600 frames into an MP4 and generated the soundtrack in code.
She is the training coordinator at her job and tracks everyone's certification hours in a color coded Excel workbook. She still had to log into the training website, check each person's hours, and type them in. Now the AI checks the training website for every employee, fills the hours into her uploaded workbook, and emails her the updated workbook and a report. It runs on its own on the 1st of each month.
He fed a career vault tool 21 resumes from the last 10 years plus weekly journal entries about projects, wins and goals. Every morning the AI pulls jobs that match his profile, tailors his resume per posting from that real history, generates the PDF, and fills out the whole application, then stops and waits for his review before anything goes out. Review takes him about 30 seconds per application, against 4 to 6 minutes each when he did it with AI. The post also promotes his own paid tool, so it is marked promotional.
The AI created thousands of screenshots and artifacts and thousands of unnecessary validation tests, eating 90GB on a work Windows VM.
The AI enabled a camera on a test device and he taught it to reboot into the previous kernel on build failure so it could iterate for days; ended with a usable setup and camera login working.
Cemex's Consult HR agent in Microsoft Teams explains the vision benefit from SharePoint policy documents, then presents an Adaptive Card in the same chat where the employee enters details and uploads the invoice image. On submit, Power Platform workflows route the claim into ServiceNow and immediately return a live tracking ticket number in Teams.
The AI runs his entire video production pipeline via an AI plugin: full 5-minute explainer videos from one prompt, thumbnails and titles, auto-upload via the YouTube API.
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.