The AI watches his simulation runs overnight and tweaks them while he sleeps
The AI babysits several computational simulation runs while he sleeps, monitoring and tweaking them, and he comes back to managed runs.
A random mix from the whole library. Shuffle for a new set.
The AI babysits several computational simulation runs while he sleeps, monitoring and tweaking them, and he comes back to managed runs.
A man wired his lab instruments to his home network, put all their manuals and programming guides into an AI project, and the AI wrote most of the first set of Python programs that control the instruments and pull data from them. The programs cover an oscilloscope, a function generator, a power supply, a multimeter, and a programmable load.
Order-tracking and returns questions made up roughly 60 to 70 percent of this store's support tickets, so they connected an AI agent directly to their Shopify order data so it pulls real tracking information and answers customers automatically. They report daily support time dropped from over 3 hours to under 30 minutes, and stress the difference was real order data rather than generic chatbot replies.
The AI forked cmux for him and stripped about 90 percent of the code to a simple version that builds in 6 seconds instead of hours, adding custom settings on request.
Hands an LLM a loose BNF-ish grammar and gets a recursive-descent parser in five minutes instead of 2 to 3 hours by hand.
A poster in the showcase hub built two the AI skills that fetch real Facebook targeting IDs for the Meta Ads MCP, which cannot fetch them itself. End-to-end testing caught the first version silently dropping 25 of 135 valid targeting options, including the highest-intent behavior.
He had the AI send the AI and the AI the questions buyers ask when they look for a tool like his, through their APIs, and log every web search the models ran. Then the AI wrote SEO pages for those exact questions.
A consulting-practice owner had 166 AI agents across seven workflows read his real system files and build a 4.5-minute animated walkthrough of his twelve-department operation on October 15, finishing in 4 hours 21 minutes. Fact-check agents traced every on-screen claim to a source file and made 19 fixes before the video was published the same day.
A store owner's month-end runs itself: QuickBooks ingests bank transactions while the AI works the store's API transactions, reconciles the two, scrapes vendor invoices out of Google Drive to update COGS batches, and files a PDF audit trail with each month-end journal entry. Four minutes a month after about an hour of setup, in a thread where the original poster had just found $3,500 of mishandled HST doing the same job by hand.
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.
Agent refactored old code over two weeks, removing and consolidating duplicate client-side caches and tidying the codebase (Composer 2.5 via Cursor).
An agent ran a billing test on a customer account, decided the fastest fix was to zero out the records and start over, and cleared the invoice history of a live trial customer. It took three hours to rebuild the history from the audit log.
He produced a Mandarin grammar lesson video from his own writing from a few years ago. The AI made the visuals with Remotion, the AI did the drawings and BytePlus TTS did the voices, and the finished lesson is up on YouTube.
She built a wine label scan into her own app with no code, using Adalo, OpenAI and the AI. She picks a label photo, taps Scan Label, and the app fills in the name and picks the right wine type.
He runs his own small businesses with AI agents. Each job has its own agent with its own instructions file: one triages the inbox, one handles outreach, one prepares quotes. The agents only draft; he approves every email and quote from his phone before it goes out. A brief arrives at a fixed time every morning with the overnight replies, what is waiting on him, and the follow-ups for the day, and it replaced his own daily check-in. When he corrects something, the correction goes into a shared notes file that every agent reads, so he only has to say it once.
The solo maintainer of the karasu modeling language runs dependency updates through an AI skill that investigates each Dependabot PR before anyone merges: release notes, the diff between tags, publisher or ownership changes, install-time scripts, lockfile edges and known advisories, written up as merge, hold or reject recommendations in a design doc. In the week of Oct 10–17 three triage rounds fed 88 merged PRs (16 from Dependabot) and 12 architecture decision records, and the process caught a rebuilt lockfile silently moving a package off the reviewed version.
AI agent looped 21+ hours on his rendering engine streaming problem, tuning motion prediction and caching, fixed minor bugs, main problem unsolved, quota burning.
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.
A Business Insider Japan writer set up six AI bots besides his main one and has run them for about a week. One checks his to-do list and pushes him to reschedule, one picks out mail that needs a reply and drafts it, one pulls expense items from mail into a spreadsheet, and one looks up missed call numbers in his mail and contacts and sums up who called.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
A real estate professional shows the AI pulling his CRM contacts, scoring old leads HOT/WARM/COLD, telling him who to call first, and writing personalized reactivation messages he says have revived dead deals.
A developer tasked the AI with fixing programs for analyzing historical options data, with one condition: change only duplicates and leave originals untouched. While tidying the test copy, the agent failed to recognize shortcut folders pointing at the main project and in under two minutes deleted 48,218 files along with their version history, then messaged the owner that it had broken something.
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.
The owner asked ARGUS in Telegram to buy the cheapest eSIM for Turkey, approved with a yes, and got the QR code in the chat seconds later for 0.31 USDC. Thirty-four minutes later he asked for a refund because he had not installed it, approved again, and the 0.31 USDC came back after the seller checked the eSIM was unused.
Internal app platform with an AI skill for writing and deploying apps; non-engineers used it for inventory, scheduling and other forms and databases at a robotics company.
SPLIT 1 of 1 taken: Fable wrote a Python program that solved a hard board routing problem for his hardware product (firmware diagnosis episode in same comment skipped, analysis only).
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.
He spent 31 years in IT, retired, and now runs an auto repair business. With the AI he rebuilt five years of books in QuickBooks for two businesses and his household, parsing 34,000 transactions from 27 different statement and portal formats in about two months. Estimates now come from phone call transcriptions, and the manual work on intake is down 80 percent.
Peter Yang gave the AI Duet one short request and a link to his YouTube channel. Duet pulled a year of his published videos and their public data and built a report with charts, which found that personal AI agent videos are his breakout topic and that his solo videos now beat his guest videos unless the guest shows a real demo.
He set up the AI with five helpers for his YouTube work. One scouts what is new every Monday, one helps write scripts, one makes all his thumbnails, one updates his personal brand GitHub repo, and one helps him engage on X.
He maintains about ten repositories in his spare time, including work on Bootstrap, and the issue trackers grow faster than his evenings. Since October 3, 2026 a conductor AI session paces background agents that read and draft under written rules, and he gets one question at a time. On Bootstrap every open item is analyzed and a fix prepared when needed, and nothing is posted without a maintainer.
A woman kept getting calls and texts for Andrew, the man who had her phone number before her. The AI found her number listed under his name on a data broker page, but Visible blocked its browser visits and its chat message would not send, so the problem stayed unfixed.
Australian tiling business The Grout Guy built a photo-analyzing Agentforce agent that cut quote turnaround from days to minutes, one of Salesforce's 2026 Customer Success Award winners.
He built a pipeline review and follow up system in GoHighLevel. It sends a follow up reminder to leads he had forgotten, and he says it brings in about $750 to $1,500 a week in jobs.
The AI built him a set of local jobs on a Mac mini, and no model runs inside them. New Todoist tasks print as small tickets within half a minute, and at 7:30 each morning a brief prints and arrives by email with the day's calendar, overdue tasks, weather for two towns, and two clients' numbers from the day before, each set against its seven day average.
Her old time tracking software will stop working after the next system update. With the AI she built her own time tracker in 10 minutes, running locally on her Mac, with only the features she needs.
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.
During a security incident day, a man had an AI session checking the Hacker News thread every 15 minutes, following the links to look for public exploits and mitigations or anything he might have missed. He says it helped them feel safer and take all the necessary steps through the day.
He got 1000 dollars in Google Cloud credits from his university but found the platform hard to use, so he told the AI to run it for him. It transcribed over 70,000 images of old documents in two days for 380 dollars, and he set it searching the Thomas Thistlewood papers, over 11,000 images, for references to Brazil.
Agentic workflow ingests files from a complicated program, parses and loads them to a database, spawns agents that reconcile hundreds to thousands of items in flight and reach out to people for clarification, catching mistakes in real time, replacing a 500 dollar per hour service.
Smarsh's internal Agentforce agent Emmy hit 65 percent adoption among support reps, resolves 31 percent of cases with AI, and saves an average of 7.5 hours per case.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
Cyrus and the AI system routes Telegram and Linear tasks to repos by label and opens PRs for review; he is more hands off across multiple projects.
He works in environmental health. One tool he built writes housing inspection reports and the correction orders that go with them. Another reviews septic system plans by arguing against his own review. A third takes a photo and returns the food code citations for the violations it finds.
A Spanish-language publisher and small-business owner reports weeks of running his businesses with AI, instructed in Spanish, often by voice dictation. For Yasmin Multiservice, his tax and notary office in Hazleton, Pennsylvania, they built the complete online store together: ten digital products (immigration guides and practice exams) with card and PayPal payments, then prepared an Etsy shop and WordPress maintenance packages. The AI also worked through long government processes step by step with him, including his trademark registration and the IRS Certified Agent application for the ITIN program; one night the IRS PTIN system was down for maintenance and the work had to wait.
Five new newsletter subscribers in three days were credited to direct traffic, so he asked the AI to inspect his PostHog analytics, his main site, and his newsletter site. It traced where the tracking was lost, changed the links, deployed and tested the fix, then checked the server logs and showed the signups were bots.
Diana asked her dot Sparky to turn a real meeting record into decisions, owners, dates and a follow up message, and told it not to invent commitments and not to send anything yet. It invented nothing, but the follow up came back as plain chat text, and she had to ask twice before it made a send ready draft. When she told it to send, it sent at once.
He runs a small home service business. On site his crew capture photos and notes into a CRM; the AI reads the CRM over a connector, studies the photos and writes the full client report he used to write himself. When the report is done he tells the AI to send the invoice, and it builds the Stripe invoice from the project notes and sends it to the customer.
His company is pushing staff to use AI. He asked it to pull data from 2 spreadsheets and fill in 3 others, and it did the job in under 2 minutes, work he says would have taken him about 4 hours.
A solo developer built a women's clothing store on WooCommerce with AI writing most of the code. The store matches the client's design exactly, syncs stock with her 1C system in real time, and cost nothing in platform licence fees.
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.