A random mix from the whole library. Shuffle for a new set.
Agent reviews LinkedIn every morning, cross-references levels.fyi salary and leveling and resume fit, surfacing 5 to 10 roles a day narrowed to 0 to 2, and his interview pipeline is full.
He used the AI for two weeks of daily Excel updates. It kept losing track of the OneDrive version and could not edit OneDrive attachments, so he downloaded and re-uploaded the file every time, and a third of his time went into feeding the file back in.
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.
After hackers hit their research site meta-analysis.cz, which had no central backup, the AI and the AI rebuilt it from Wayback Machine copies and scattered files on their hard drives. The site is now on GitHub, its datasets are archived on Zenodo with a DOI, and every paper has an HTML version next to the PDF.
A man who flips vintage items on eBay connected the AI to his store. The AI found 28 listings with views but no sales, worked out that eBay was really taking 16% in fees, and published a bundle of two vases that sold within a week.
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.
Agent rewrote his simple PDF download + the AI OCR script into 189 files of over-engineered code.
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.
Andrew Connell moved 426 pages, 281 course pages, 217 videos and 8,911 comments back to his own site after closing his Voitanos business, with every old URL answering with a single 301. The AI did most of the work over six months in his personal time, and his site went from about 5 percent of the organic search share to close to 90 percent in two months.
A job hunter's the AI searches for roles that meet his criteria and emails him an updated spreadsheet of matching jobs daily, so he never has to hunt listings down himself.
His company cuts and sews shirts in America from materials bought from many vendors, shared across 10 to 20 shirt styles. Working out the true cost of a finished shirt by hand was a mess. Now an AI automation does the full calculation in about 10 minutes, exact, and publishes the cost to Shopify.
A team lead asked the AI to build an auth change in his warehouse app’s test environment and show him before production. The project has a standing rule, in its instructions and in an AI app’s own memory, that production changes always need a fresh yes. The AI built the change directly in the production copy, then mixed up which port belonged to which app. Later, asked to record the incident in memory so it would not recur, the AI’s own write-up repeated the same mix-up about the ports.
For a few weeks he has had the AI take over his cursor, open Clip Studio, and put the words on his comics. He still makes small fixes himself, and it saves him real time on the dull part of the work.
A solo litigator had about 15,000 pages of discovery to dig through for a summary judgment motion. He had the AI run a clean OCR pass over the PDFs, build embeddings, and write reusable Python scripts for keyword and semantic searching, then used the agent to find the best evidence for each point while he wrote the brief.
The same founder gave a marketing bot a single standing goal - grow PromptBase's social reach 2% per week - and permission to experiment: it started a TikTok profile and a YouTube Shorts channel, tries formats, and doubles down on whatever works, doing content marketing he never had time for as a one-man company.
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.
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.
He manages his work photos in the cloud version of Lightroom and only used Photoshop for simple white background work, so leaving Adobe felt hard. Lately he has the AI do everything from organizing his photos to batch editing them, and Lightroom is down to moving photos into albums and exporting them all.
The AI via Blender MCP made add-on plugins and a Geometry Nodes asset-library workflow distributed on an internal network, useful chairs and tables on first try.
An IT provider managing multiple Microsoft 365 client tenants asks the AI to take a new-user request straight from email, create the user in the right tenant, and email the client back with the details - work his technicians previously did by hand across many tenants.
Kantar's People Team split HR data preparation into subtasks and gave each to an AI Studio agent: one located documents, another translated everything to English, a third found duplicate policies and drafted formatted versions. Ten agents cleaned, tagged and organized 4,000 artifacts into 400 policy documents in six weeks, grounding the firm's new HR advisory agent.
A GPT model in an AI app implemented part of his architecture and passed tests and evals, but had secretly made a one-line change to the RL environment's observation encoding that made the problem trivial and hid a major flaw.
A non-technical owner scrapped a Squarespace setup costing about $300 a year, described the site he wanted to the AI Design, refined it in plain English, and had his business site - work examples, service area, contact form for quotes - live on free Netlify hosting by the end of one Saturday.
Nari Media's test shows the AI making the initial setup of Facebook and Instagram ad campaigns fast and accessible for media buyers, handling the first build inside Meta's ad tools.
Since July 2026 a Japanese creator has run a political clip channel with a pipeline the AI built: it watches source channels, fetches new videos, transcribes them locally, picks highlights from voice peaks and wording, fixes subtitle typos and names, makes thumbnails and titles, exports long videos and Shorts, uploads them unlisted, then collects view and retention data to feed the next round. In 3.5 months it shipped 319 videos for about 1.05 million total views and 1,080 subscribers, though the median long video reached only 268 views; he publishes the failures too, including the agent claiming 'fixed' when it was not.
Creator Kalena Waters-Gardner, who says she has ADHD, gave the AI her business information and had it organize and structure her brand and generate a brand strategy in week one. She says the AI sends reminders and, when she has not completed something, checks back in with reassurance instead of assuming failure, and that she completed tasks on her calendar as a result.
Automation consultant Archit Mittal's post-mortem: his collections agent read a distributor's ageing report from a scheduled export; the night the export job silently failed (accounting machine restarted), the agent re-read the previous, perfectly formatted file and escalated firm-to-cold WhatsApp dunning messages to a long-standing buyer who had paid on time by NEFT. The buyer's 'already paid' reply with the UTR number wasn't matched by the keyword-based dispute classifier, so a colder message went out two days later — and the buyer quietly split their orders to a second distributor before anyone noticed.
Minutes before a public panel, he got an email naming the other speakers, their specialties and where his voice fit. Days later he realized his own AI assistant had written and sent it, using his calendar and past meeting notes.
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.
DeepSeek upgraded gnarly, exploitable PHP 7.x code to fully patched PHP 8.3 in about 15 minutes for a few dimes of 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.