A random mix from the whole library. Shuffle for a new set.
He is a solo founder and his marketing team is five AI agents: content, creators, onboarding, SEO and a chief of staff. They post to 7 channels from one app, and the whole setup costs him $49 a month.
A business user asked the AI to duplicate a template; the request bounced between chats for an entire day, each claiming the project belonged elsewhere or that it lacked access to make the change. Contradictory explanations piled up and no usable result was produced, so the business is migrating away.
Set the AI the task of running Parseable locally against another of his apps; it worked around a protobuf limitation by running a proxy, he switched his stack to JSON and got it running.
He gives the agent his ideal customer and one pain point. Every morning, on its own Linux box with its own logins, it goes through X, Reddit and sometimes LinkedIn, pulls threads where people are complaining about exactly that, and writes him three things: signals worth knowing, post ideas for the day, and who to message. He reads, picks, and posts.
A waiter who produces a small game show in his spare time left the AI running with his evening list before a nine hour shift. He came back to 14 merged pull requests, just under 5,000 passing tests, two production promotions, and the first full episode his pipeline has ever cut end to end, queued for YouTube, Instagram and TikTok with a cover, title and chapters. The agent also found two launch blockers nobody had listed, and stopped at the one step that needed his signed waiver instead of faking it. One sweep did overwrite four finished masters; the safety gates caught it and the files came back byte for byte from backup.
Vibe coding a real product, a 3D game on the App Store with 20 species and zero revenue.
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.
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.
Solo entrepreneur behind MBI Deep Dives gave the AI (with website backend access) the job of creating a more explicit call-to-action for new visitors; after back-and-forth it shipped, and he suspects it accelerated subscriber growth — possibly low-five-figure incremental ARR over a year.
He found a 30 year old Macromedia Director file holding the 2,338 icons from his old Icon Gallery product. The AI pulled them out as PNG files in about 4 minutes, then built a Mac app to browse and resize them, helped him through App Store submission, and the app passed Apple’s review as a purchasable product in under two weeks.
His AI server checks what the current shipped Micro.blog app versions are and whether an app is in review. It updates the public help page by itself.
Creator Amie Tollefsrud gave the AI access to her Instagram analytics and asked for growth advice; after three clarifying questions about audience and positioning, the AI delivered an 'Instagram Growth Plan' PDF. Screen recordings show an executive diagnosis (1.85M accounts reached), audience positioning, four content pillars, keyword clusters, carousel and story blueprints, and a 7-day action checklist.
A new feature for his blog, built using his voice with an agent, now in use.
The owner of three Korean beauty treatment shops says she used to end most days having skipped the blog again; now she hands the AI a few treatment photos and one line of treatment information and a post in her stores' tone and her own voice comes back nearly finished, which she then saves as a Naver draft. Posting frequency is visibly up, the setup handles the recurring work, and she now teaches the same sequence to her staff.
On his boss’s suggestion he tried AI to compare two documents. It failed badly, and about 15 minutes later he was back at square one.
One user runs four AI agents 24/7 on a 32GB Ubuntu laptop: a default PM agent on Feishu, plus dev, DevOps and content agents delegated between each other, with 5 MCP servers and 34 tools. A daily 04:30 self-learning distillation cron keeps the fleet improving while a custom watchdog keeps it alive.
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.
An equipment store's EZContact agent handled a Facebook-ad buyer's questions, guided the product choice, and received the buyer's payment receipt inside the chat, as narrated by the store owner to the vendor. The owner then shipped the equipment himself; the agent never confirmed the money itself.
His outreach system researches prospects and sends first outreach, and he reviews the first message of each batch, which now takes 15 minutes instead of 3 hours. Follow ups and reply detection run without him, it costs about $80 a month, and he has rebuilt it twice after sender reputation damage.
Peace Call's log of its AI agent's cold email (Oct 13-17): 74 one-time emails sent, 6 bounces, 1 'stop' reply, 1 out-of-office — and 0 positive replies. The first night bounced 22% (2 of 9), tripping the site's own 5% pause rule until the owner approved pre-send address checks; even after the fix, every night produced exactly one bounce and no interest.
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.
Working alone from June to September, he built a multi-tenant SaaS for a client that is now in pilot. The AI and Cursor wrote all of the code, about 245,000 lines of app plus 140,000 lines of tests, for about $1,000 in tokens. Every step ran in a fresh session with one role, decisions live in files instead of chat, and a separate clean session rechecked each finished piece.
Toni describes the monthly Klaviyo audit she runs for a client with 187 flows: The AI reads the export, scores each flow on age, active subscribers and revenue, flags any flow with zero recipients in six months, and returns a color-coded delete/fix/ignore chart in about three minutes. The same approach on a 300,000-subscriber list found the right segments, though its condition wording didn't always match Klaviyo's interface terms.
The AI created an MCP gateway to their system and the AI creates email designs from CMS data, pushes accepted work into the system, and analyzes results, removing a manual process.
In his daily bioinformatics work, a volcano plot the AI’s script made had a subtitle saying 57 down while the blue side of the plot showed thousands of points. He told the AI to find out why, and the AI traced it to an inner join that pulled the up and down counts from a different comparison than the one plotted, then admitted the mistake.
He spent 4 weeks designing and testing an upgrade of their 16 node HPC cluster with AI, and the AI then did the full reinstall itself. The reinstall covered remote install, network booted images, Slurm setup, a shared filesystem, and web services that give their users a small cloud platform.
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.
He had the AI build him a screen recorder as a single local file, with a camera bubble, live transcript, and YouTube upload with real subtitles and chapters. He built it because he wanted to stop paying for Loom.
Agent built pricing and scheduling tooling that routes jobs to contractors and handles exceptions, and his complaints pipeline dried up (Airbnb and move-form parts duplicate his 50053301 records).
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.
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.