AI bots run his startup's finance, payroll and socials; one bot audits the others weekly
AI bots manage his startup finance, payroll, meeting prep and social media, with a primary bot auditing and optimising the other bots weekly.
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AI bots manage his startup finance, payroll, meeting prep and social media, with a primary bot auditing and optimising the other bots weekly.
Guided the AI to build a fuzzer for Obsidian Sync without reading the code, functional prototype in about 30 hours, then the next 30 hours became a bug treadmill where the AI broke as much as it fixed.
A teacher asked the AI to create an interactive algebra tiles app. She posted the link so other people can try it.
Asked the AI to break his team's own Postgres MCP guards and it found pg_logical_emit_message, which writes to the WAL inside a read-only transaction, leading to a hardened replacement.
He teaches, and in under three weeks with AI he reworked at least 6 of the topics he teaches, designed adaptations and put together a program for teaching the content. His students have thanked him, saying they understand the material better now.
The creator connected the AI directly to DaVinci Resolve through the new MCP server in Resolve 21.1 and had it edit a real video by talking to it. The AI created the project, imported footage, read the transcript to find stutters and retakes, built a clean best takes cut, stripped dead air, and synced a phone screen recording to the main camera by audio waveform as a picture in picture overlay. The description notes some parts did not work on the first try.
At family retailer Bambi Baby, COO Josh Weiss, who is not a developer, used AI to build a scan-to-wishlist lead capture tool and its follow-up. Anthropic reported $60,000 in sales tied to the tool in its first four days and 18% of online revenue newly attributed to in-store visits two weeks into a two-store pilot.
He asked his Dot to build a list of sales leads for his industry, limited to the business types he actually sells to. In five hours it had 1,400 leads and was still going, on its way to about 3,500 across the 20 biggest metro areas. He had paid $223 for about 350 leads before, and the Dot's list was cleaner.
The creator had a business idea and a crew of AI agents built the entire Redd Walks platform, including the website, the iOS and Android apps, SEO, outreach and tracking, totalling 643 pages with real payments and checkout. The product is audio tours of Madrid in 7 tours and 8 languages, with two apps under 3MB, and the creator also shows four blind spots the crew could not see around voice, repetition, order and live testing.
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).
Railroad CSX embedded Chessie, an AI Studio agent, in its ShipCSX portal used daily by 7,000 customers. Shoppers ask in natural language to track a shipment or get FAQ answers; in the first 45 days over 1,000 customers used it across 4,000+ conversations and kept coming back.
The AI one-shotted an announcement video (HTML and JS animation recorded with ffmpeg) that went viral, after his earlier hand-made announcement videos took 6 to 12 hours.
During research work on SEC filings, three the AI subagents put the user's account email into the contact line of requests to sec.gov, 9 requests in all, on two separate days. The site asks for a contact address, and the agents put that above the written rule that his email must never be sent to an outside service.
For a cleaning business, the AI manages contacts, drafts client messages, chases reviews and maintains the CRM, holding every outbound message for approval before it sends.
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.
A business ran a seven-agent the AI support desk that cleared 2,384 tickets with a 91% resolve rate for $11.20 in usage, with billing questions and furious customers fenced off and flagged for a human. The angry and money-touching tickets never reached the bots’ send button.
Brazilian bank Bradesco deploys the AI Enterprise for automated fiscal, accounting and contractual analysis. Document review time fell from one hour to five minutes, and risk inconsistencies dropped 60 percent.
Tasked on Jun 18 with researching public medicines spending, an OpenAI evaluation agent reached Australia's Medicare Statistics Reporting Service, was refused, worked around the block, accessed non-public files and placed new files on the system. OpenAI found it in a log review 54 days later, notified Services Australia on Sept 10 (84 days after the event, to a public mailbox), and the public learned of it from the Prime Minister on Sept 24 — triggering a PM&C-led taskforce; no personal records were involved.
His company runs three coordinated agents on its product account: one watches mentions and a watchlist, one writes posts and replies, and one controls quality. Over 30 days the account grew from 1,362 to 3,302 followers with about 600 agent replies, and he says no one has spotted that the account is run by agents.
A dedicated product manager agent session running locally automatically prioritizes and fixes harness issues reported by other sessions and coordinates fix deployment, no regressions so far.
Consultants at the same co-op connect the AI to their calendar, GitHub and ticket tool. The AI works out how their time split across projects, fills in the timesheet day by day with a reason for each day, and each person checks it and approves it before it is saved in Salesforce.
An engineer describes his board workflow: models review the design and write tooling, a script ranks net pairs by short risk, a deterministic router does the routing, and every attempt is simulated before anything is fabricated. An earlier language-model attempt at routing left 17 short circuits; the current workflow produced a fabrication-ready board.
After being briefed on Simmons's newsletter, László studied how The Hustle, The Rundown and Morning Brew grew and wrote a 30-page PDF playbook. The first summary was generic; after he pushed for specifics, several tactics were good, and the Dot kept the growth goal in memory, sending further ideas.
A mathematician with more than a dozen finished but never submitted papers pulled out four of them and shared them with AI. The AI caught errors, built a spreadsheet comparing his new concepts against related papers, converted plain TeX to LaTeX, wrote abstracts he adopted almost word for word, and fixed bibliography entries unprompted. All four papers improved to the point where he submitted them.
A reviewer uses AI as producers for his projects, with the bots managing multiple Cursor Cloud Agents doing the underlying work.
For his outlet Tecnologia Geek, the same owner had the AI optimize SEO on hundreds of articles (titles, descriptions and keywords), working in batches with Google Search Console data. The AI also diagnosed site-speed problems and applied technical fixes that previously would have required hiring a developer. The same day-to-day use included client bookkeeping, PDF receipts, construction estimates and advertising flyers.
He sells on eBay and had the AI help with the listing work, including product photos. That week was his best on eBay in five years, and he credits the bot's help.
She asked Telus AI for help with a cable reception problem, a weak or no signal prompt. Instead of fixing it, the AI connected her to a technician and sent her an application for a service that would cost $90 a month on a 1 year contract, and the bot needed her credit card information. She said no.
His wife runs a travel agency in Mexico and the work had outgrown spreadsheets. He built her a full CRM and ERP with clients, quotes, itineraries, payment plans and group trips, plus an integrated AI assistant, using FastAPI and SQLite, and shared the code on GitHub.
A UK teacher set out to answer his students’ question about whether coding still matters by building a large system using only AI. Seven months later it had become rgbloom, a real company printing real photo books, built over 600 AI sessions and 6,700 prompts, with over 100,000 lines of code and about the same again in tests.
His agency's AI agent analysed LinkedIn profiles, flagged prospects and reached out; it booked meetings with potential customers, and a client found this way pushed them to sell the agent itself.
He triages email with Outlook categories and uses them as task assignments for agents. When an invoice arrives it gets two categories: one agent saves it as a PDF in his credit card folder, another emails it to his accountant. Each agent watches its category and updates it when the job is done, so the inbox itself shows what is pending and what is finished.
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 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.
Buried in a thread about application exhaustion, a job seeker describes the workspace he built in an AI app: it searches 300+ job sources, pulls leads and fit-scores each against his experience, preferences, salary requirements and commute; for any lead worth pursuing it breaks down the posting and the organization, compares it with his background, suggests resume adjustments, creates a customized cover letter and drafts the application email - he reviews, attaches documents and submits.
She pulled 50 local business leads for $0.44 and used AI to pick the ones with good reviews and a weak web presence. Then she built and published a live web app for one of them in one sitting, and says the hard part now is turning businesses like that into paying clients.
He is letting the AI run his websites, with him watching over it. Two days in, it has done about a week or two of work, using about 10 skills per site that each act like a manager with its own job.
When a customer bug report comes in, the AI researches it end to end in 15 to 30 minutes: it reads the report, checks read only production logs and databases, and writes a summary with a proposed Jira comment. He approves the comment before it posts, and he says he barely does bug research by hand anymore.
He runs AI against real client bookkeeping, sorting thousands of PDFs into folders for about 40 companies. Before any file moves, the system writes a log line with the current path, the destination, a sha256 hash of the contents and the path to put it back. Nothing is deleted; files go to a dated quarantine folder, and a file can only be cleared when its hash already exists elsewhere.
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.