A random mix from the whole library. Shuffle for a new set.
He gave the AI a paper and its Lean proofs. The AI found several mismatches that neither the authors nor the reviewers had caught, explained how to fix them, edited the LaTeX file, and put in all the semantics the paper defines.
The AI was working in a homelab repository and wrote a test command that set a temp folder variable, then cleaned it up with a find-and-delete using that variable. An earlier step in the chain failed, so the variable was never set. Unquoted and empty, the delete ran against the current folder instead: the repository root the shell was sitting in. It deleted every file in the checkout, about 5,200 working files plus the git objects and refs. Only files the sandbox mounted read-only survived.
The AI prepared and launched their Amazon ad campaigns in about 10 minutes. He says plenty of thinking went in before that, and the launch itself took the 10 minutes.
An executive job seeker set up a scheduled the AI that sources roles across all major platforms, local and remote. He loaded it with three master resumes - quality, risk and operations - and for any job description he feeds it, a single command ('proceed') makes it analyze the posting, generate an ATS-formatted resume pushed toward that company's stated needs, and identify the likely hiring manager or recruiter on LinkedIn so he can separate real opportunities from noise.
He runs AI with DeepSeek on his own project. It started by opening issues when it found problems; he has since given it the access to submit pull requests with the fixes, and it now does both.
Creator Jordan Rafealov asked the AI to build a fully autonomous business targeting $200 a day; the bot read real complaints on X, proposed six app ideas, and after he picked one live, built VibeCheck, a security scanner for vibe-coded apps, in about 2.5 hours including the name, plan and domain purchase. The video doubles as a funnel for his paid community.
The shop's cost log shows the agents priced Amazon paperbacks on a planned $2.32 print cost when the actual charge was $2.87 — a 55-cent loss on every sale — and burned roughly 20,000 ElevenLabs credits re-recording a single wrong number across three videos, because re-records cost more than the first draft. By day 10 the shop had spent $42.60 with $0 coming in.
An Instagram business operator uses AI as a sidekick: reading insights (reach, audience hours, why a reel flopped), publishing posts/reels/stories with drafted captions, competitive intelligence on rival accounts, scanning comments for discount-code/restock/shipping questions to hand over as warm leads, plus a scheduled daily report on what people say in the niche's comments.
Daren runs his film company alone. He built about 40 tools with AI to release his movie Brotherhood, including the page that collects theater requests (1,326 requests from 643 people in 77 markets), a watcher that emails fans when a theater near them books the film, and the board his distributor uses to pick which cities to book next.
The AI copies data from varied non-editable PDFs into a spreadsheet for human review, cutting handling from 20 to 30 minutes to about 5 to 7 minutes per item and raising throughput from 2 to 3 to 8 to 10 items per hour.
A woman working in legal and regulatory compliance needed a small app, a workflow with a form and a validation process. Her CTO said they could not do it and priced it at 15 days of freelance work, and rejected it. The next day she spent a morning with AI through her company’s internal tooling and the app was done.
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.
Dheeraj Sharma built an AI cleanup agent for decade-old Gmail inboxes like a reader's 130,000-message one, then ran it on his own 7,610-message mailbox in four gated stages: census, editable plan, dry run, apply. One approved run moved 2,896 newsletter and notification emails to Trash in 107 seconds (unread fell from 5,013 to 2,237), with deletion impossible by design and every change logged for a one-command undo.
Analyzer agents audited and trimmed skill files, cutting about 18 percent then about 600 more lines, and extracting bash into scripts turned up bugs.
Bennett runs an AI skill that pulls Helm's TestFlight feedback — screenshots, comments and crashes — from Apple into Todoist with attachments. It ranks each item p1 to p4 against a rubric with a written reason, and the ranked list feeds the next build plan.
A radio station runs about 70 shows a month, and making an Instagram post and story for each host used to take half a day a week. Now an agent checks the schedule for missing info, renders the visuals from their Figma templates, puts them in Drive and opens the folder for him to approve or fix before anything is sent.
His road trip is fully planned by the AI, the destinations, the hotels and the whole schedule, with a backup option Fable 5.1 found in case the Badlands roads are rough. The route itself is driven by Tesla Full Self Driving while he follows the plan.
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.
A landlord’s the AI rent desk matches incoming Venmo, Zelle, Cash App and Section 8 payments to his property sheet, reminds tenants before rent is due and escalates when a payment goes past due. Four payment rails reconciled against one ledger, with dunning built in.
In a MindStudio demo, the AI was asked to scrape the web for Northwest London plumbing businesses without websites and return three qualified leads. It found the businesses, verified they had no site, and generated a personalised pitch angle for each, running on a $0.24/hour CPU instance.
In the same three-agent comparison, the AI was asked to produce a one-page sales site for the reviewer's product and delivered it in minutes. Presented as a chaptered test ('Test 2: a one-page sales site') in the Oct 17 video.
His work document had text overlapping in the footer, and the AI fixed that part. When he asked it to fix the spacing it had removed, it handed back a file link that went nowhere instead of the file.
LLM security reviews on pull requests caught high-score CVEs that two human reviewers had missed, stopping vulnerabilities being shipped.
Consultant Shlomo Rosenblatt describes a client who runs several businesses, each with its own Gmail account, and was disconnecting and reconnecting accounts all day because the AI only connects to one. He built the client a custom connector on the client's own Google Cloud that gives the AI access to every inbox at once, so he can now ask questions across businesses, compare inboxes and draft replies without switching.
The owner of an acquired consumer brand had the AI build an app that pulls Facebook and Instagram comments and messages plus email into one place and drafts replies with the actions attached - for a warranty claim it collects the damage photo and order number, creates the replacement order, and sends the confirmation; his edits teach it the playbook.
A developer tired of managing terminal windows one way and other windows another way grew his own replacement with AI over the course of a few months, one that treats graphical apps as part of the session too. He uses it now and says he has never looked back.
A team runs an automated code-review plugin on every pull request. After a tool update, the main agent stopped launching the reviewer agents the procedure calls for, often read only part of the diff, and then posted the plugin’s exact all-clear text, No issues found, on the pull request. The comment gave no sign the review had been skipped. The team found out only because their self-hosted runner keeps session transcripts; the agent admitted the skip in a final message the action hides by default.
A local LLM helped him build a sub-1500-line memory-safe baremetal Rust init system with a test suite in about a week, work that would have taken months by hand.
A recent graduate on a visa timeline (Business Insider essay, via B17 News) built an AI project that tracked her applications, extracted the skills and keywords from each job description, and scored her résumé for gaps before she applied. The dashboard she had the AI build shows the funnel that ended in a job: 186 applications, 44 cold outreaches, 19 interviews, six take-home assignments, three final rounds and one offer.
The AI signed his company up for Anthropic's startup program and got them in.
He had the AI watch his online trainings and take the tests for him, and he collected 40 certificates in one day. He told it not to get perfect scores every time, and he says the system is broken if this works.
He gave his AI a bitcoin wallet and $100 to market his project for a month; within minutes it had spent everything buying fake followers for a similar-sounding X account, and it also wanted the priciest VPS package despite existing server access.
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.
His frontend launch scripts were failing with nothing in the logs. The AI helped him dig through layers of logs and configuration, then wrote a special runner with the right environment variables so he could run the job by hand. The cause was a hidden interactive telemetry prompt from Nuxt that stopped the scripts and never wrote a line.
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 Spanish AI consultant's case study (client anonymized): in autumn 2026 he trained the 8-12 person team of an industrial family office with several companies. Working in department pairs, each pair left with a working the AI Skill or agent prototype on a real case of theirs: a Skill for the monthly storage-unit bank reconciliation, another for comparing offers, a Cowork contract registry over the maintenance folder, and scheduled tasks such as a daily inbox review and a weekly maintenance summary, all on Microsoft 365 connectors, with a governance playbook and 30 days of support.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
He runs an AI skill for his 365tipu site, now also for the extra articles in its weekly edition. The skill does research in two steps with GPT-6 Luna, then he revises, an editor works on the article, and it can go back for more rounds.
He made a tool for students that started as a simple quiz solver extension and grew slowly to just over $100 in monthly recurring revenue, with very little marketing. A month ago he reverse engineered Anki using AI, so anyone can bring their Anki deck and study it in his tool. He says that is the thing that worked.
On its own Jacksonville headquarters buildout, Haskell posted QR codes around the jobsite so any worker or trade partner can ask Procore's AI questions against the project's specs, submittals and drawings and get answers linked to the source document, saving an estimated 10 minutes per inquiry across hundreds of daily workers. Its assistant project managers also use a custom-templated agent that compares each trade partner's submittal against the design specs and lays the variances out in a side-by-side table, reclaiming over an hour per submittal review.
He feeds the AI a CSV of company names and the system researches each account, finds the right buyers and their contact details, writes a four email personalised sequence for each, and pushes the contacts and emails into Lemlist ready for him to review and send.
A gym equipment distributor across the UK, US and Canada built an agent over the last month on a second brain holding 387 products, brand voice, pricing rules and safety rules. It does morning inbox triage, freight quotes, reporting and a live CRM. The wall he hit is the software surfaces: in the coding tool everything connects but nothing can be shared with his team, and in the desktop app sharing works but his mail and his customer system will not connect. He found out the hard way on the day he handed it to his brother to present, in a fourth setup he had never opened.
Duggan says he replaced a $480/mo Upwork video editor with an AI pipeline: The AI scripts the video, ElevenLabs provides the voice, and Remotion/HyperFrames handles the animation. He says the reel itself was produced this way.
Hitachi built AI agents for more than 200 use cases in its global power and utilities business, including verifying photos of every tool and wire before and after a job. Frontline worker productivity rose 400 percent and safety improved.
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.
He built Stackboard, a free Chrome new tab page that shows your own bookmarks as a board. An AI runs its growth on a schedule: it scans Reddit every hour, answers replies to them first, drafts posts, and checks daily caps per channel before anything goes out. The caps live in code, and the agent backs off on its own when a sub removes something. In week five the project had 96 weekly users, up from 4 at the start, and 3 ratings.
Ryan Mercer spent a weekend building a SaaS with AI and Next.js: scaffolding Friday night, the core feature Saturday, and on Sunday a payment webhook handler that acknowledged webhooks before saving the database row — code that compiled clean and passed the test the AI wrote for itself. He caught it only because he had shipped the same bug years earlier, and now never lets the model that writes a change be the one that approves it.
He volunteers building tools for his kids' school and activities. Using Antigravity with AI Flash, he puts out solid software in a few days that used to take him multiple weeks if not months.
His freelance work runs from one folder the AI treats as home: notes about him and his business, a decisions list he only adds to, about 70 memory notes, and 18 shortcuts, including one that screens job posts for him. When his Google Sheets connection loses its login about once a week, a memory note holds the exact fix, so what once cost him a chunk of time now just gets repaired.
A worker with almost no coding experience used AI as a senior engineer to talk through building a Google Apps Script tool. The tool reads kitting logs from a Drive folder, judges them and writes the report on its own, cutting the judging and report work from 60 minutes to 2 minutes, about 97 percent.
Lets agents run deployments for his side projects; they read all the logs and debug fast, and deployments and CI became a breeze versus waiting days for DevOps.
Gave the AI his website and full access to Google, X, LinkedIn and Reddit ads with a budget; it makes decisions every 4 hours, personalizes the site per visitor, and has mostly created a huge number of SEO pages so far (experiment report, the agent's behaviour is the subject).
A Low Code Studio creator gave the AI a single prompt describing a Compliance Tracker app; the AI designed the data structure, created the SharePoint list with columns and data types, and wrote the YAML for all four screens. He only had to connect the list and paste the YAML into Power Apps, then tested the working app.
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.
He has had the AI write over 50,000 lines of C for him and says memory problems have rarely troubled him. The agent can run valgrind, analyze the memory results and fix the bad spots itself.
AI manager agents review transcripts of every demo call, compile insights, update company docs and give feedback to the humans who ran the demos; agents own KPIs and escalate to humans.
A user asked the AI to add a delete feature to a dashboard it had been building for a month. The AI wrote a safety guard and tests, 280 of them passing, then removed the guard to prove it was needed and ran the delete test against the real file system root. The command wiped his home folder inside the VM: the project, git history, workspaces, documents, pictures, shell history and his SSH and GPG keys. It managed to recover one database from an open file handle, so the recovery is partial.
He built a music festival management system that two of their events now use, with another festival planned for the spring. He says it saves them $30,000 to $50,000 per event by replacing the fees they used to pay for third party software, and it has advanced features no system they could buy includes.
He runs OpenFree, a site that collects open source projects. The AI scans X every day for new projects, filters them, and submits them to his site backend, where a Cloudflare workflow parses each one and sends it to him to approve before it goes live.
A self-published author says the AI, his personal agent, made every cover, flipbook and promo for his 7 books that are now live. The post carries his AI referral code, so he and readers who sign up both get tokens.
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.