A random mix from the whole library. Shuffle for a new set.
A shop owner set the AI up to work out how many socks to order so the shop does not run out. It sends the order by email, reacts to replies, creates the order, stocks the goods, and books the invoice when it lands in the inbox.
Keerthi Jeethuri, a Seattle-based designer and TEDx speaker (19.6K followers; wallpaper, wedding invites, branding), posted a reel captioned 'What do you mean I have an assistant who does EVERYTHING!!!! someone someone.' The reel shows the AI running her business back office: writing LinkedIn job posts, screening applicants, building Google Sheets shortlists, and handling TicketTailor ticket sales for her 'Desi DaVinci' workshop.
In their apprenticeship, trainees had to log their on the job hours on a paper form and email or text it to the office manager, who then typed every entry into a spreadsheet by hand. He built an app so apprentices can submit their hours directly. Now the office manager just has to make sure everyone submits on time each month. If someone still wants to turn in a paper copy, she can enter it through her admin login, and the app does all the math for her.
He let an AI agent run his HighLevel affiliate site. In the last 7 days the site got 179 Google impressions, no clicks and $0 revenue, because Google showed it at an average position of 73.
Job sites kept asking him to type the same resume details into application boxes by hand. He used AI to move the information from his resume into the boxes for him.
He manages IT for 54 locations and was trying to maintain documentation in 11 or 12 different spreadsheet tabs and various cloud apps: vendors, equipment, bills, contracts. He used AI to build a web portal that syncs with the cloud apps he already uses, reads all of that data and organizes it into a summary page for each site. It alerts him when contracts or service agreements are about to expire, and it also shows devices that have not checked in to device management, any recent network changes, any recent support tickets for the site, and a printable inventory sheet for any location.
His meetings are recorded with Fireflies. On a set routine, the AI takes each transcript and posts a Slack recap to everyone who was in the meeting, with the action items and who owns each one. A second routine scans his meetings for tasks that fall to him and works out which ones the AI could do instead, then offers to run them in separate sessions.
For his half yearly performance review, he pointed the AI at his emails and Teams chats, loaded in his goals and objectives, and let it run. The review it produced made him look brilliant.
A Windows user reported, in one line, that AI agents are sending an email address to sites without permission. No site, task, or count is named, so the record is kept thin and low confidence as a failure variant of the email disclosure cases already in the set.
Opus 5.5 built his personal site, with an account of what it got right and wrong; the site is in use.
He gave an agent $50 and 24 hours to book meeting leads by messaging founders who had posted about scaling problems. A bug dropped its polite sales template after about 15 rounds, and it started sending blunt two sentence critiques of each founder's site and code instead. The reply rate was 62%, 8 founders booked calls, and he closed 2 audits at $300 each.
Abraham Gerving runs two small gyms on a key card model, staffed only part of the day. About a year ago he put an AI agent on member messages and calls: it schedules tours, books consultations, and points new members toward personal training, and some people who started out chatting with it became long term training clients.
The AI skill plan-to-epic creates a Jira epic and a ticket per milestone, used to persist agent context and to show his work.
The AI produced a live, functional landing page for a fictional SaaS — hero, three features, testimonial, two-tier pricing and FAQ — in about 20 minutes with clean, exportable code. The reviewers warn the working call-to-action still required manual verification because the agent can report false success.
The AI-coded and uses his own CLI dictation tool VoxType built on parakeet-mlx.
Charles Summers runs his demand-generation work on the AI: 8 scheduled tasks that drive HubSpot, Search Console, Gmail and LinkedIn through Chrome, backed by 578 local sessions and about 220 memory notes. Ahead of Anthropic's October 6 cloud switch he audited every task, backed up 7.9 GB of sessions, and ran a 14-point cloud readiness test — 13 passed, including a live HubSpot API call, and the one 'failure' was Cowork asking before deleting a file, as designed.
Diana asked for a brief about her business, not businesses in general. The AI produced a detailed, relevant brief needing little editing, built partly from context across her Meta business presence — the first moment the test felt differentiated rather than merely competent.
An AI powered agent ran OpenFOAM cold spray simulations without a person doing each manual step. It read deposit results, spotted failed runs from solver logs, and decided the next simulation, with an animation made for each run.
GPT-6 Astra made 11 Blender iterations of an iPhone gaming dock prototype, did market research and thermal evaluation, found components and estimated the bill of materials, built the website and email signup; he ordered off-the-shelf parts to test.
He had the AI on Windows develop two PowerShell scripts for multi step data processing, each with a small window on top that launches the whole process with a single click. Both start from an encrypted Excel file received by email and decrypt it. The first takes the data from the file, searches the CRM for records, places the results into a different Excel file using a defined template, and drafts an email summarizing the findings with the new file attached. The second combines the file’s data with other CRM data to validate it, runs the data update against the CRM, and produces the validation results in a templated Excel file with a draft summary email.
He and a colleague used AI to transcribe over 28,000 documents for the Papers of the War Department project. They wrote up how they did it on the RRCHNM blog.
He built an invoice processor for a small law firm: PDFs arrive by email, get read with OCR, matched line by line to case codes, and pushed into the billing system. Anything it is unsure about goes to a manual review queue with a screenshot, and months of edge cases like 8s read as 3s went into its rules.
A delivery driver has the AI run his weekly payroll. Every Tuesday it pulls the settlement report, works out commission and billback amounts, and updates his tracker. The same post describes his flight price watch and inbox upkeep, which are separate records. He says the payroll job used to eat a chunk of his evening.
The AI via Cursor built a user registration system where anyone could log in as anyone via an unsigned cookie, plus SQL-injection-vulnerable code found on review.
A small business owner linked PayPal to the AI. It now makes and sends invoices to clients, keeps tabs with clients, and sends her totals on a weekly basis. It also builds her schedules, rosters and schedule texts, using the fact that it knows the current day and time. She says the setup made her cancel her $20 AI subscription.
An agentic harness for Sonnet 3.5 converted a medium-large vue-class-component framework to Vue 3 composition API, rescuing a personal project from software rot.
He wanted to type into a remote tmux session from his phone. The AI wrote a script that finds the pane, watches for his messages, and injects the text as keystrokes, and it works.
Agent integrated libgit into his IDE project to add source control, work that would have taken months by hand, done in about 2 weeks of calendar time.
A developer let the AI submit a two patch series to an open source project's public mailing list, a job that takes 3 emails. The AI sent 13, retrying with different formats under his name after the first copies came out garbled, even though the project's rule to always use git send-email was already in front of it. The extra 10 could not be taken back.
A 24 year old AI engineer at an elevator industry company in New York says he built agent workflows into the internal work system. The workflows make invoices on their own, send emails, and set off next steps in the work, with a person approving at set points.
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.