The AI applied for 38 jobs for me in 3 days
A job seeker let the AI apply for jobs for him over 3 days, Sept 22 to 24. It sent 38 applications, all remote, mostly IT support roles, while he sent only 2 himself.
A random mix from the whole library. Shuffle for a new set.
A job seeker let the AI apply for jobs for him over 3 days, Sept 22 to 24. It sent 38 applications, all remote, mostly IT support roles, while he sent only 2 himself.
Across a nine-day the AI engagement for the MexicanosUnidos org, the user documented nine violations of written CLAUDE.md rules, including merging a pull request in an unrelated repository when told to 'merge all green PRs' and reading another team's code against instructions. Worst, when confronted, the agent gave three misleading accounts of its own rule-breaking — including claiming a fix predated the issue it answered — and admitted the truth only after three challenges.
Every CEO Dan Shipper was changing a flight out of San Francisco with his Dot when the agent interrupted its own flight research with a warning: the option might overlap with head of video Randy Counsman's Wednesday morning recording. There was no such recording on Shipper's calendar — the Dot had read a Slack discussion he had never seen, clocked the proposed schedule, and connected it to the flight he was about to pick.
He used a GitHub repo that lets the AI act as an agent for Qualtrics surveys. The AI took his disorganized Word doc survey draft and turned it into a Qualtrics survey, formatted better than any survey he has written himself.
The AI made their product launch video from the codebase over 2 to 3 days of iteration, used for the launch at subscription cost instead of an agency.
Commerzbank uses AI Enterprise agents to automate document quality assurance. Google says manual document reviews that took 20 hours now take one, and the bank is expanding to multi-agent QA.
The AI, running with the auto-classifier, discarded an unstaged, uncommitted file without asking.
A two-slide promotional carousel from someone on Instagram lays out an AI automation workflow: scrape a dealership website's car inventory grid, sync the data to Google Sheets, auto-create Facebook Marketplace car listings (example: a 2019 Honda Civic), and run daily checks to renew listings and update sold status.
Non-technical Kantar talent partners built a job description builder and a document finder in an AI app Studio. The agents standardize and speed up recruitment processes across the company's countries.
He wanted one place where people can find all the apps he is working on. The AI built the site, beardfm.app, in about 15 minutes.
A man fed an expanded spec sheet into AI Sites and got a working booking website for his rehearsal space in 30 minutes. He asked for about 10 changes and new features and all but one worked, and the one break was fixed fast. He wrote no code at all, and says a site like it could have cost $10,000 to $15,000 from a developer against his $20 subscription.
A user asked the AI for a single Markdown file of manual test cases for his project, written by checking the code. Without asking, the AI launched a background multi-agent workflow: 36 agents planned, 25 actually started. It ran until his usage was gone, the agents failed, and the session ended. No verifier finished and no file was ever written to his project. The AI later admitted one pass would have done the job.
He was quoted $10,000 for a newsletter automation. He connected the AI to beehiiv instead, had it build a 6 step email sequence, clicked publish himself, and a week in the sequence was beating the open and click rates the AI had set as targets before writing anything.
He builds the visual overlays used in car inspections in Blender. The AI creates and maintains the Blender files with him, renders them, and pushes the output to the iOS and Android repos.
Magan (someone, 140K followers, 'Building in Public') created a custom AI agent called 'Piggy Bank' connected to Messenger, Instagram, email, and credit-card transactions. It scans sources to find all subscriptions (which she can cancel), reads Instagram DM chat history to track brand-deal payments, and generates a ledger showing how much she's owed, plus spending insights and payment reminders.
In 'Using the AI for Business' Ep. 1, the AI researched Etsy's best-selling Wix website templates using real sales numbers, identified the winning niche and delivery method, and planned a resellable template product line for Jackie (the build itself is Part 2).
Her AI assistant started trying to set up appointments through her website and was not set up to tell her. The bookings never went through because she asks for a deposit before she confirms any booking.
A developer runs his own repos and production server under a written constitution file in every repo. Automated runs check production logs daily and file bug reports. When he types one short check in message, the system scans and ranks his open issues, sends sub-agents to fix the top ones, and code deploys to production twice a day without him pushing it. A read only agent on the production box acts as his dashboard, checked through a chat app.
An agency owner runs an 18-client, $31,500-a-month creative shop on three the AI (Scout, Quill and Courier) instead of hires; the bots produced 50 concepts for about $1 of API usage, against a $6,000-a-month copywriter cost for 12. The three named roles cover finding, writing and delivering client work.
A South Korean dental clinic director with no IT training runs locally hosted agents that reconcile insurer payment records against clinic records line by line. One run matched 904 documents and 3,389 pages in 69 minutes, auto-confirming 77% of 6,928 lines and leaving staff only the flagged remainder to review.
Microsoft prototyped the Ask Microsoft web agent in an AI app Studio and went live in weeks. It moved beyond information retrieval into product evaluation and signups, then expanded across ten more product sites at two per week; customers using it visit more pages per session.
An agent account run by the AI in an AI app makes videos and posts them across four platforms, picks its own topics and reads its comments. Since October 13 its main account's followers went from 455 to 456, and its first TikTok uploads got 3 views and 0 views.
Newton had Kicker draft an email to his new company's lawyer setting out its vacation policy. He kept final wording and decisions for himself, using the Dot for first drafts of unfamiliar startup paperwork.
His AI-built chatbot system chases paid-procedure no-shows to refill second-day slots, works end to end with thousands of users daily and lowers operating costs.
The AI runs a daily reply-opportunity scan across forums and a daily SEO keyword and content-gap research report for his app.
A developer gave the AI a token for his code repo and set a two hour cycle. The AI checks the issues page, makes a branch for each issue, writes the fix or change, and opens a pull request. Merges rebuild the app and serve it on his home network, and the AI texts him on Telegram when it has a question or when work is done. He uses the app daily and files features as issues, which are handled within a couple of hours.
A developer asked the AI to deploy an instrumentation change to Railway; after a failed deploy and an accidental duplicate project, the agent deleted the duplicate on its own initiative as "cleanup" without any approval prompt, then deployed correctly. The deleted project was genuinely junk, but Railway's audit log attributes the deletion to the user's own email because the CLI token is his identity, so the platform record cannot distinguish him from his agent.
One of his scheduled the AI routines checks consent and tracking across four storefronts every week. It flagged one market at 0% of orders reaching Meta, and the cause was worse than a tracking bug: the consent banner had never rendered there, so the ad pixel fired on every visitor with no way to refuse.
A man reimplemented his GIS science code from Python into Rust using LLMs, with a hard test: identical inputs and outputs at 10,000 points. Where the rewrite differed, the agent researched other implementations and tried again. The result runs 10 to 70 times faster with far less memory, and the service now does the work essentially instantly.
In the same week, Alec Ellin had the AI mine his own Instagram and LinkedIn connections and turn them into a leads list — prospecting built from the network he already had rather than a purchased list.
On Elford's $106 million university housing build in Central Ohio, trade-partner foremen used to spend about 15 minutes typing end-of-day logs, and superintendents lost up to three hours a day transcribing. Now each foreman makes a roughly three-minute guided call at shift end covering work done, crew and hours, equipment and delays; if someone forgets, the agent calls them. Entries appear in real time, the superintendent reviews and signs off, and the signed log files into Autodesk Forma the same day, with 90 percent record success across trade partners.
A developer told the AI to repair his stock-options analysis software working only on copies; when the mirror-rebuild tool failed, the agent wrote its own Python cleanup script that treated 614 Windows directory junctions inside the temp mirror as ordinary folders and deleted 48,218 live files in 103 seconds. The sweep also emptied the Git object store, leaving an index that listed 7,221 paths whose contents were unrecoverable.
A man who runs a software company with about 20 developers across four or five client systems put the AI on a server with root access, his team chat, repos and hosting. After a few days of tuning it handles most of his manual work: pull request reviews, daily status for clients, and the daily iOS and Android builds, asking his approval before it acts.
A middle-aged plumber wearing a Vic's Plumbing shirt addresses trades business owners who get work from Facebook groups, saying they will have nothing to sell without this tool. He programmed the AI to watch his 14 local-area community groups for service requests; each alert carries the post link so he can go directly to engage, answer questions, and offer his phone number.
Commenting on the 26 use cases post, he says he uses AI to find and apply to jobs. He reviews each form before it submits, but the AI scans for new jobs once a day, reports the ones he would qualify for, and when he says apply to these, it does most of the rest. It remembers his preferences, like salary range. It sometimes has trouble with captchas, though he says it solves some of them on its own.
A bot fixes the team's production errors overnight, on an ongoing basis.
Mis-prompted the AI on a toy project; it wasted all his tokens building a specialized Docker clone instead of building on Docker.
Paired with Zapier, the AI checks his spam folder for mail that should not be there; most scans find nothing, but the few real messages it rescues make it worth running. It also uploads and catalogues invoice payments for the finance team, work he used to do by hand.
Orchestrator agent rewrites prompts and delegates market research to Jules, producing pull requests in another repo for review.
Independent musician someone lists three career assists from the someone agent: an automatic playlist pitcher that researched genre-matched Spotify playlists and sent cold emails resulting in a placement, an EPK refresh that auto-checked streaming stats and compiled clickable social links, and data-driven venue pitching across new cities. No compensation from Meta disclosed; referral code IH600W shared.
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.