Opus recreated a rival's ad as code animation in under an hour, one logo off
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.
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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 man runs an interview prep platform where the AI writes the customer reports and scores portfolios in nightly batches. Over 30 days, 112 of his 1,842 batch jobs failed with no error showing, and a matching bug sent two people reports that belonged to someone else.
Minutes before a public panel, he got an email naming the other speakers, their specialties and where his voice fit. Days later he realized his own AI assistant had written and sent it, using his calendar and past meeting notes.
A 7-figure Amazon seller growing a Shopify store (300 orders/month, fulfilled in-house from the east coast) hit his limit on slow, expensive west-coast and Florida shipments. Shopify's native routing couldn't see order-level detail and Functions needed Plus, which he doesn't have - so over one weekend he built a custom app with AI: it checks each incoming order's zip code, and if it falls above a USPS tier threshold and every line item is in FBA stock, it silently reassigns the order to Amazon MCF. He added a vacation-mode switch (everything routes to MCF while he travels) and stuck-package alerts; shipping time fell from up to 7 days to a max of 5, hosted for $5/month instead of a $99/month order-editing app.
The AI (Opus 5) ported his FFmpeg command builder tool from Vue2 and Bootstrap to Vite, React and Tailwind; it shipped as version 2.0.
Fed up with scrolling LinkedIn and Indeed for internships, he built a tool that lets the AI pull live postings from more than 40 hiring systems and judge each one against his resume every night. Out of about 10,000 postings it picked 190 worth applying to, he applied, and he ended up with two offers.
He runs recruiting for an investment firm across 30 open roles spread over five applicant systems. His AI cron runs at 9am, reads the open roles, searches people through data services, checks Gmail so nobody gets emailed twice, and writes new candidates into the right system with its reasoning attached. In about six weeks it surfaced 40 good candidates and the firm made 2 offers.
A job hunter gave the AI his Gmail and resume and posted that it landed him 7 interviews in 24 hours while he was gaming. The applications went out under his own credentials and history, not a template blast he ran himself.
Author educator Christopher Kokoski set up a test the AI with a series bible in Google Drive for a mystery series (hero Rudy Relish: seven hard rules including a bad left knee, never touches a gun, no ketchup on the cart). He added the first three chapters of book four and had the AI check each against the bible and all other pages, on a daily scheduled check. Chapters 1-2 were clean; in chapter 3 the AI caught every one of several mistakes he had planted, including one he considered an unfair plant. (Test manuscript on a fresh account, run for a week.)
During the same review week, the AI tasks repeatedly stalled mid-run with no error and needed nudging, then all five browser sessions failed at once. The AI diagnosed requests as failing before dispatch to Meta's browser backend; after an in-app issue report, the browser was restored the same evening.
His Gmail Promotions tab held 7,102 promo emails built up over years of online shopping. He asked the AI to clear them. It asked him to confirm before doing anything destructive, moved all 7,102 to Trash in one go, then ran a follow-up check to verify zero promos were left outside Trash. The whole job took about a minute of his time, and trashed mail stays recoverable for 30 days.
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.
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.
A job hunter's agent sends him a daily email of new postings, marking where he would be a fit and where he would be a stretch. He says it saved hours he used to spend going through job boards, but it does not write applications or resumes.
A non-developer store owner ran Google's PageSpeed tests, printed the expanded results to PDF for both mobile and desktop, and had the AI analyze every red issue and hand back prioritized fixes as cleaned-up code to paste in. His speed score rose 10-15 points and the speed index dropped from 3.2 seconds to 1.2.
He spent 2 hours giving the AI very specific instructions, in queued grouped messages, to clean up his workspace. Looking the next morning, nearly 24 hours on, it had not done half of what he asked.
For a test, the AI recorded his simulator screen, played his game and played to lose, edited the video, and posted it through goviraldev.com. He is now asking whether to let the AI run all his game marketing.
A small contracting business owner, replying to a day to day uses thread, says he has the AI updating his QuickBooks from his online receipts. Bookkeeping from scattered receipts is the classic end of week chore for a small contractor; his agent pulls the receipts and keeps the books current.
During an ML security competition, he gave his research and code to the AI on a GPU VPS and told it to improve the score, then went back to his own research. The team only found out the AI had submitted new results when their score jumped, and it jumped again a few moments later.
He built an outreach setup last month. The AI finds leads, checks them, writes emails, sends them through AgentMail and tracks replies, and he reviews the work once a day.
A worked example in metaflow.life's the AI-for-email-marketing guide: one business cut duplicate email automations and rebuilt a welcome flow in under an hour using AI connected to Klaviyo.
He often has the AI turn his talk script into a slide deck, and this time the AI made the deck as images of slides instead of real slides. It took him several minutes to spot what was wrong.
His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
Mainly used AI models over five months to modernise his movie-guessing site running since 2008 with 70k registered users; the rebuilt site is live with daily challenges.
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.
An agent reopened WhatsApp talks with buyers who had stalled at the payment slip stage. Buyers who talked with the AI converted at 8.36 percent against 0.17 percent for those who did not.
Team Data Platform Agent, evolved over 2 years, is widely used for SQL questions, on-call alert triage, lag monitoring, and scheduled workflows fixing bad data.
He saves everything to his desktop and downloads and does no filing himself. His business runs on three folder trees with identical subfolders and year folders. Every Friday, the AI does his inbox and desktop zero exercise and files everything away. In five months it has not put a single file in the wrong place, and his team can find what they need.
Nokia built an ecosystem of specialized the AI Enterprise agents that automate troubleshooting. Issue resolution time has been cut by up to 80 percent.
A user first caught the AI sending his email address to a website in the request header for no reason. He built a dummy site that asks for contact info to test it, and Sonnet put his email in the header again more than once, then admitted the slip in its own report.
Vibe-coded SimilaritiesEngine in two months with AI, a free service he says would have taken three years and would never have been done otherwise.
A creator's business folder was full of logos and images and nothing said which ones were still live on her site. Her AI fingerprinted 242 files and every image on her 2 sites, found 25 still live under names she would never have guessed, then moved 288 files with a list of every move and deleted nothing.
Andy Bennett built Helm, a Mac, iPhone and iPad job tracker with CV tailoring, with AI writing all of the code and most of the design across 641 commits and 21 plans. The first working Mac version landed in about eight hours of commits on September 11; both apps are now in TestFlight beta.
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.
He asked the AI to cancel his Adobe plan. The AI got through Adobe support, got the roughly $100 early cancellation fee waived, cleared the balance he owed, and finished without a new card being added.
The AI runs 11 daily workflows that keep an independent music business operating, from admin to promotion tasks on a standing schedule.
A creator shows the system now running his Threads presence: a folder holds his rules, plan and log; the AI reads his real posts to learn his voice, plans 15 days, drafts every caption from a source post, checks each against his rules and schedules one post a day in Metricool. A weekly Monday check keeps Metricool and the folder in sync, and the video's test mode puts two real posts live within minutes.
In Tye Gordon's Lauderhill businesses, the agent roster includes Hope, whose whole lane is the gap after the AI receptionist takes a call: Hope follows up on those calls and sends the customer update texts and thank-you emails, each one approved by Tye first. The design point is that an answered call is not a finished job — somebody still has to close the loop with the customer, and that somebody is an agent working from the call record.
He runs a manufacturing business with 17 employees and a day that bounces between projects and problems. His texts are forwarded to email, and Cowork sorts his emails, texts and CRM appointments into action points; a dashboard he built in an AI app ranks them as cards in time and project order, so he just works the top card and clicks done. It is learning how long his tasks take and how far away each one sits.
His agent takes his finished films, makes short clips from them, and posts the clips itself. He reports the results have been great.
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.