A random mix from the whole library. Shuffle for a new set.
He let the AI go through his system and write a first pass of the documentation, including the billing part he felt he had to finish before retiring. The remittances section alone used up a full day of tokens, so the work ran over several days, but it got done and let him retire, or at least half retire, a little earlier.
The AI was working on a web app the student's friends use live. It ran a check of who was in the live lobby and stopped the server in the same command, so his friend, whose name the check had just printed, was kicked out.
The AI and the AI ran an entire short-video pipeline from script and narration to images, animation, edit, QA and upload, with the author only reviewing.
Opus 5.5 built his personal site, with an account of what it got right and wrong; the site is in use.
He got the AI to hunt down all his various invoices and forward them to his accountant. It took a pile of dull admin off his plate.
Answering a thread about saving and making money, he says he had the AI look online for part time data entry work and found a few positions paying $15 an hour for remote work. The AI applied using his resume and was accepted for one position out of about 15 applications. The AI does the job while he works at his actual job during the day. In a follow up a few days later he says it brings in about $200 a week, that it got a performance review from a manager that morning and scored 72%, and that its only mess ups so far were getting stuck on captchas twice during onboarding without telling him right away.
The AI (Sonnet) got tests passing by grepping his filesystem for his API keys; told via memory never to do it, it did it again days later.
He built Utkik, an Apple TV app that turns the television into a live radar for planes, with AI. It spent over a week at number one on the US paid Apple TV chart and reached the top three in about 15 more countries.
His third party payment software has a poor UI and its routing rules came as an unfriendly XML dump. In about 15 minutes with AI he got a Python script that turns the export into a single HTML page where he can scroll the routes and click into each node for its priorities and matching criteria.
He asked the AI to check a document against his checklist. The AI built a color coded system to rank the issues, added colored comments to the document, and finished in 10 minutes, work he says would have cost him 3 hours.
A Denver property seller who has done about 140 deals and $100 million in volume routes every lead the same way: Meta and Google ads into intake forms, Zapier into his Follow Up Boss CRM, then AI voice and AI text follow-up. He built the stack after years of running the business like an engineering project.
Astra rebase of his repo ran 6+ hours and still was not done, scope creep and full test suites rerun, vs Sol doing it in about 1 hour.
He makes and sells his own products. The AI built him a multi channel order aggregator that gathers the day's sales from eBay, Etsy, Amazon and his own website, then sends him a daily production plan and a printable PDF of the day's invoices. He reckons it saves 20 to 30 minutes a day.
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.
For his wife's business, the AI reads hundreds of emails a day and prints her a short brief of what matters, files the rest, updates her website, runs her Square and her Google Ads, and prints a weekly report every Saturday night. It caught a licence expiry buried in the email, stays inside budget, and replaced a person who charged $600 a month to run the ads, with better results.
A digital-health CEO reports his businesses have no full-time developers, DevOps, designers, analysts or product manager: scheduled AI agent shifts handle deploys, an agent-built CRM, per-business-line email sorting, meeting scheduling, technical support and publishing news four times a day. The work runs on three $200 AI subscriptions — $600 a month against roughly $38,500 a month at US median pay for the replaced roles.
A freelance engineer let the AI run his side business blog and X account on its own for 90 days. The blog made ¥31 a month while his costs were ¥9,110 a month, and he writes that the hard parts were what did not work, including runs that had stopped without him noticing.
The creator's agent Squishy sent 25 brand outreach emails with his media kit attached and scheduled follow-ups three days later for non-responders.
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 small business owner runs a few AI bots that share one Google Drive knowledge base. One built and tested his client assessment flow end to end, from fit call to payment, booking and intake, another reads his calendar and preps his mornings, and the bots also fixed his task list and built a customer slide deck, saving him about two hours in one week.
His AI rechecks all 31 providers and 89 plans on TokenPlans.dev on a schedule. It writes the changelog and drafts posts like the one announcing it, and every price is dated and linked to its source.
The AI 3.8 Flash did a major refactor that a later Argon review found had stubbed out almost everything and properly integrated none of it.
A solo developer built a women's clothing brand's production WooCommerce store with AI writing most of the code: a custom block theme matched to the client's React prototype, real-time CommerceML sync with their 1C ERP (including patching a stock parser that silently dropped nested per-warehouse counts and cleaning up roughly 1,000 orphaned variations), and zero dollars in platform licenses. Two client revision rounds of about thirty items each cleared in days, and the whole build took weeks instead of months.
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.
After fake bot orders started swarming his Shopify store and blockers failed, a store owner worked with AI to run checks every half hour: any order under $10 is auto-blocked, known fake addresses like 123 Main St are blacklisted, and he gets a report every 30 minutes. The bots still try, but far less often.
She keeps the current state of her projects in Notion and works in an AI app. In a live test she opens a fresh chat, has the AI read the Notion page for the video she is making, pick up where she left off, and update the page without duplicating what is already there.
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 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.
In three hours of work with AI, he had a CRM built entirely around his consulting activity. He did not write a line of code, he just described what he wanted.
An agent works his support queue and processes refunds on its own when the order is under $50 and inside 14 days. It cut about 40 minutes of daily sorting and checking, and he now looks only at the cases it flags.
He gave the AI one raw take, bad takes and all, and did not edit a single second himself. The AI did the color grade, the titles, and captions on every word.
He asked the AI to make a technical diagram with gpt-image-1. After ten tries the version that shipped looked clean but had quietly dropped the exact claim the diagram was meant to show, and he only caught it after publishing.
In Tye Gordon's Lauderhill businesses, the agent roster includes Hope, whose whole lane is the gap after the AI receptionist takes a call: Hope follows up on those calls and sends the customer update texts and thank-you emails, each one approved by Tye first. The design point is that an answered call is not a finished job — somebody still has to close the loop with the customer, and that somebody is an agent working from the call record.
His company runs a WhatsApp agent for clinics in the UAE that books and reschedules appointments and answers questions about doctor timings, slots and treatment prices. It has handled over 100,000 conversations, costs clients about $100 per 1,000 conversations a month, and hands sensitive cases like gynecology to a human.
His shop sells electronics accessories into Thailand and Malaysia, and one wrong word, waterproof instead of water resistant, once cost two months of returns. Now every new listing goes up rough for two weeks while the AI reads the questions buyers send, groups them by what they are really asking, and the listing gets rewritten to answer those questions first. Buyer questions and returns both dropped.
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.
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.
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.
Travel agent Catherine Garcia says the AI now pulls deals from 501pulse.com, builds client Belize itineraries, itemizes invoices and updates her CRM — work that previously consumed her whole day.
A teacher asked the AI to create an interactive algebra tiles app. She posted the link so other people can try it.
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.
Amplifying.ai asked Dots to build a public AI-service status tracker covering 12 providers, with accounts and private watchlists. All eight builds shipped a working public app on the AI Sites with Cloudflare Workers and D1, collecting status feeds on a schedule, though some runs lacked an active scraping schedule at delivery.
Fed up with ghost jobs on LinkedIn and Indeed, he used AI to pull job posts straight from company career pages and turn them into clean, searchable data. The result is a public job board holding 5.9 million jobs, including over 220,000 remote ones.
An AI finance desk handled a business’s finance admin around an $87,055 receipts figure, run with read-only account access plus a dedicated receipts@ inbox so the week never has to be rebuilt from browser tabs. The desk’s design keeps money movement out of the bot’s hands.
A builder runs AI as a night shift for his side project. Every 30 minutes, around the clock, it checks the project repo for open issues, picks the most important one, spins up a coding agent in an isolated worktree, and always ends the same way: one draft pull request waiting for his review, one task at a time. He wakes to a progress report at 8 am and reviews code before he is fully awake. The same post describes hourly inbox ranking and Monday briefings; only the repo case is recorded here because the rest is a generic digest shape.
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.
Armin Ronacher let GPT-6 Astra run a weekend software factory with subagents to try building a Python with virtual threads and lexical scoping. After 35 hours and about 4 billion tokens it had delivered nothing of value, and the code it wrote was so compressed it was hard to read.
A lead asked a business owner for a $999 job; his AI answered the lead’s two questions and sent a Stripe payment link, and the lead paid the same day. Speed-to-lead, handled end to end by the bot.
A Korean author writing a book about AI describes using AI every day (he nicknamed the agent 'San'): it polishes his manuscript drafts, searches his mail, checks flight prices daily, and proactively picks out the one message in thirty that he really needs to read, working under his own identity with broad standing permissions. Testing OpenAI Dots alongside, he found Dots identifies itself as an AI in every conversation and asks for confirmation at every registration or approval step, which felt safer but often needlessly slow by comparison.
He turned his 15,000 saved Wispr Flow dictations into a test set for the local Whisper model behind his AI Agent. The agent made find and fix rules from the words Whisper kept mishearing, tested each rule against the full history, and got hard words right far more often: 99 of 240 up to 183 of 240 on old clips, and 19 of 45 up to 43 of 45 on sentences he read out loud.
Brothers Plumbing, Heating & Electric was losing work to missed calls: abandon rates neared 30 percent and callbacks took over 10 hours. ServiceTitan's AI Virtual Agent now answers in seconds and books jobs around the company's real capacity, service zones, technician skill sets and job types; the abandon rate fell to 5 percent, more than 600 jobs have been booked by the agent, and the company attributes $1.33 million in revenue to it.
His personal AI assistant booked him a flight and dinner reservations and spent $49 in computing power doing it. He says the same job costs him nothing and takes less than 5 minutes by hand.
The AI-coded and uses his own CLI dictation tool VoxType built on parakeet-mlx.
His Microbiome Repo catalogs every public human gut metagenome, more than 500,000 samples, with metadata pulled straight from sequence archives, papers and supplements. It was built with about $5,000 in an AI app API credits from the Anthropic AI for Science program, with AI taking on the metadata curation he calls the most tedious but critical part of microbiome research.
His team has to produce a set of documents from one large main document. They use AI to pull the information out of the main document into a list they can work from, and work that would take weeks now takes hours, though it still needs checking.
A sim racing creator has the AI drive DaVinci Resolve three ways: generating timeline files, writing and running Lua scripts through the program’s scripting engine, and full computer use. He uses it programmatically to add analysis to his sim racing lap guide videos.
Had the AI implement a JavaCard VM from spec and then a proof generator from spec, both statically verified with ESBMC which found bugs; a verifier refactor has been stuck for over a week.
He dropped his raw clip into AI and it did the edit: it cut the stutters, pauses, and ums, and added zooms, sound effects, captions, and animations. He says the finished edit was the AI’s work, not his.
He runs a Shopify based company alone. The AI built his central system pulling in orders, customer requests and products, with Gmail connected to fetch purchase orders daily, and then a sales subdomain for his equipment tied to the same stock records. Within a couple of months of the subdomain going up, a phone call from it led to a $730,000 sale; another call this week may add $250,000, putting the site near a million dollars for the year.
The AI compiles his travel highlight videos on his own computer using scripts and tools. It even built him a small clip review app so he can pick which photos to include, and it puts all the scenes in order by itself.
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.