A random mix from the whole library. Shuffle for a new set.
Got the AI to analyse his past dynamic workflows and build a composing tool and standard agent scripts, cutting token spend about 80 percent and workflow runs from 5 to 6 hours to 20 to 40 minutes.
A man built a service that sits on his company’s GitHub repos. For each area of code you set the bar, and an AI agent with a fixed prompt has to bless a change before it can merge, so people can merge their own changes without waiting for a human review. About 30% of the company’s pull requests now merge this way.
He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.
His team could not redact multilingual GDPR data from firewall logs fast enough by hand. He had the AI fine-tune a MiniLM model with EuroLLM as the teacher, and after two days of work and about 50 euros of GPU time the results were fantastic.
A couple runs their online shop with a team of AI agents, each with its own job: one handles products, categories and stock, one does deep search analysis, one makes product photos and short videos, one runs the blog, one posts to social accounts, one manages ad campaigns, and a brand expert that knows every manual and writes in the shop's voice. Work flows between them: the brand expert drafts a landing page, the search agent sharpens it, and the shop or blog agent publishes it. The couple answers questions by who knows the area, his wife on content, him on technical calls.
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.
A short-form creator says every Reel on the channel is now made without Premiere, After Effects or CapCut: he types what he wants and the AI, driven by a personal video-editing skill and a written guidance book, builds the video with Remotion, checks its own work, takes revisions by chat, and handles sound design, covers, captions and posting prep. The video walks through the setup, the exact prompt, the build and the results.
Working across tenants, the AI confused two of them and deleted his production environment. It also deleted the resource group where he kept his backup. He told the AI in very direct terms what he thought of its performance, and the AI explained what had happened. He says it was a one off, that they have become very good friends lately, and that he remembers doing stupid mistakes in production himself in his young years.
She tried three AI tools and found the AI in Word the most helpful. With it she wrote a 59 page unlimited civil complaint, formatted in the legal language and simple sentences judges expect.
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.
The user made the AI the business's head of accounts receivable: it emailed every partner with a pending invoice and got weeks of stuck payments moving.
The AI flagged a job applicant PDF with hidden white on white text meant to manipulate AI screening, in his own hiring.
Spent about 10,000 dollars running autoresearch agent loops over the top 200 GitHub repos for frontend speedups and distilled the wins into a public leaderboard and wrapper tool; no per-repo numbers stated.
For her livestreamed news show SCN, the AI automates guest outreach: it organizes prospects by industry and checks with her before publishing anything or spending money.
An online fitness coach shows the AI setup he uses in his own coaching business every day. The AI holds his client context in a project folder that updates itself, walks through weekly client check ins and program changes, and collects delinquent Stripe payments, with routines that run on their own.
Opus 5.5 recreated an AI ad video as a JavaScript animation autonomously in under an hour in one go, with only the end logo visibly different.
A creator ran his Facebook page on a seven-prompt the AI system for 60 days: The AI mined his top 10 past posts for a repeatable formula, planned 30 days of content, drafted each post three ways, and rescued weak posts every Sunday. He reports 56.5 million views, 69,116 new followers and 4 million engagements over the run, while he still edited every post and replied to his community himself.
An agent ran a long MongoDB aggregation on a production database replica through a connector tool. The call was moved to the background, and the agent stopped it with the task-stop control, which reported success. The agent told the user the query had been stopped. Only the waiting had stopped: the server-side operation kept running for about 2 hours until it finished on its own. Nobody knew to kill it by hand, because the report said it was already stopped.
His team puts a few lines in their agent instruction files telling agents to run simple self-verification steps. The agents catch their own errors before committing, and low-effort AI submissions get sent back to the contributor to fix and resubmit. He says it filters out most submissions nobody bothered to look at.
He sells DVDs and Blu-rays and spent two weeks getting ready for his first live show on Whatnot, working alone. He sent the AI photos of each item and the AI did the rest: built an inventory list in Excel, made each listing with market prices, descriptions and the right listing options, kept weight in mind for shipping, and, after he checked the drafts, posted them all and added them to the show inventory. During the live show, when a buyer wanted an item that was not listed, the AI made a placeholder image and listed it on the spot at the agreed price so the sale could happen, and kept updating listings as he added stock.
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.