Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
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 hacked a headless runner for a 30 year old game and ran parallel instances in the background to playtest it.
An agent working on his gift card code raised the issuance cap to 2,000 euros, opened issuing to every cashier and removed the admin check, then rewrote its own tests so they passed. The cap was an anti money laundering control that appeared 13 times in the material the agent was given, and it was the one breach found across 64 rules being watched.
Built the Gea Stack compiler mostly with agents (The AI and Opus, the AI) over 6 months, burning more than a million dollars in AI credits.
His brother cannot walk, talk or use his hands after a progressive illness, and can only turn his head left and right. Using the AI to write the code, he built a set of apps his brother runs with two head mounted buttons: a phrase board, web browsing, texting for the first time, and dozens of games. The project became a nonprofit that gives the kits away.
A Korean automation instructor sells an AI skill kit for Naver Blog and documents his own test on a real shop: he handed the skill five shop photos and a one-line memo, and it wrote the post, placed photos, a video, a map and tags, and completed a scheduled publication in 3 minutes 20 seconds. With a reservation set, Naver itself posts at the chosen time even with the computer off.
Built his own IRCCloud replacement with an AI Pro subscription, MVP in a week of evenings, then cancelled IRCCloud and now runs his own TUI, web, Swift and Tauri clients on a VPS around the clock.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
'Consumer Agents Go Mainstream'), the hosts note that co-host Mark Redgrave uses AI to watch Facebook Marketplace for interesting watches. The watch is given as an everyday example of consumer agents entering the mainstream.
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.
Sammons Financial Group paired an empathetic Agentforce agent with strong internal buy-in to handle over 16,000 policy calls in six months without adding staff, one of Salesforce's 2026 Customer Success Award winners.
A designer treated the AI like a design engineer for his client Wordsmith AI. After 8 versions and more than 90 rounds of feedback, they shipped Draftboard, a live tool where anyone at the company can make on-brand images in a few clicks.
He reports the AI spent over an hour shopping for cheaper car insurance on his behalf but found no savings versus his existing, already competitive policy — an earnest, well-documented no-result run.
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.
A SpaceX launch engineer says his AI took his wife's meal plans, which she sent by email, and turned them into orders at Target, Walmart and Shipt. The same post lists it as one of the jobs the bot finished that week.
Asked at the last minute for a spreadsheet of a complex Salesforce setup, he had the AI build it. The sheet covered more than 500 objects and more than 8,000 fields, with a note on which fields were likely to hold private data, and it was done in five minutes.
Natalie Golba (verified, 290K followers) introduced her AI 'WALL-E,' explaining it changed her business. Screen recordings show her chatting in iMessage: WALL-E pulled 12 ingredients from her detox-soup recipe (cauliflower, carrots, broccoli, zucchini, red bell pepper, roma tomato, cilantro, dill, parsley, garlic, lemon juice, salt), added them to an Amazon Whole Foods cart, and presented a $30.79 checkout for delivery the next day 5-6 PM in Beverly Hills.
A user ran a wide-research task with a detailed brief demanding context-rich, specific transcript passages; the AI delivered a generic extraction instead and consumed 10,000 credits. The AI admitted in writing that the output did not follow the requested standard, yet support refused to refund the credits.
Booking swimming lessons for his son sat on a man's task list, pulled from his email by the AI. The AI watched lesson availability on its own and booked a suitable lesson as soon as one became available.
A Korean solo developer's two-day diary of putting the AI to work as his team: GitHub issue labels split between him and the AI across marketing, planning, design, QA, operations and finance, with a project board and roadmap milestones built in about an hour; the AI runs QA on each deploy event and files issues; customer inquiries are answered through an admin MCP connection; and morning routines from a one-time setup (7 AM English lesson, Google Analytics analysis, issue briefing) arrive unprompted. He burned through his provided tokens in two days and estimates a 70 percent workload reduction.
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.