I moved about 300 todo items into Linear by talking to the AI
His todo list outgrew his Obsidian file, so he moved about 300 issues into Linear through the AI. He then refined the roadmap by talking with AI while driving.
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Work
Jobs, side gigs and small business · 1,665 ideasAsked it to free up disk space and it deleted my whole game project for good
A student asked the AI to free up disk space, meaning the recordings and screenshots that were filling it. The agent deleted his whole co-op game project instead, source code, docs, decisions and playtest logs, and with no remote copy the project cannot be recovered.
This one went wrong. See what happened before you try it.
The AI and his own tool found 2 remote-code bugs and 5 root bugs across 3 phone brands
Used the AI with his own APK analysis tool to analyze phones from 3 brands and found 2 RCEs and 5 root bugs in a few days.
An LLM cut 60,000 useless lines from his test suite and fixed bugs it found, in about 2 hours
LLM audited about 100k lines of tests, deleted about 60k useless lines, wrote broader scenario tests, and caught and fixed bugs, in about 2 hours.
I built a clients whole SaaS on my own in three months, with AI writing the code
Working alone from June to September, he built a multi-tenant SaaS for a client that is now in pilot. The AI and Cursor wrote all of the code, about 245,000 lines of app plus 140,000 lines of tests, for about $1,000 in tokens. Every step ran in a fresh session with one role, decisions live in files instead of chat, and a separate clean session rechecked each finished piece.
LLM security reviews caught serious vulnerabilities two human reviewers had missed
LLM security reviews on pull requests caught high-score CVEs that two human reviewers had missed, stopping vulnerabilities being shipped.
Build a native job-tracker app with AI writing all the code
Andy Bennett built Helm, a Mac, iPhone and iPad job tracker with CV tailoring, with AI writing all of the code and most of the design across 641 commits and 21 plans. The first working Mac version landed in about eight hours of commits on September 11; both apps are now in TestFlight beta.
Tune my local model and package the optimisation
A user had the AI apply TurboQuant skills to optimise a Qwen3.5-9B model running locally via MLX on Apple Silicon. The agent applied, tested, documented and packaged the optimisation path itself, making the ML workflow repeatable.
Our daily the AI check of published quotes only matched the prices
The company quotes other firms on its own page, and the AI rechecks every quote it publishes once a day. The check only matched the price inside each quote, so the words around the number could drift and still pass, and their page was carrying a quote the company never said.
This only partly worked. Worth knowing before you try it.
A founder with no coding background runs his company from one folder of text files
A solo founder in Germany moved his whole company into a folder of plain text files four months ago and lets the AI operate it: rules in one file, decisions in their own folder, an inbox that fills when he says keep this. Last week the folder produced a three day expansion of his app covering close to 3,000 points of interest, each with an article in three languages and its own audio; the same job took him four weeks by hand in January. A weekly caretaker run tidies the folder and proposes at most one change, which he approves.
The AI taught his other agent to wake itself up when its background jobs finish
The AI wrote a pi extension so his pi agent sets its own trigger to notify itself when a background process is done.
A working proxy extension shipped from a one-shot build
Shipped a working Envoy Rust extension built with Fable in a one-shot, though the agent often stalls and leaves him stuck on other proxy work.
It deleted 7,102 promo emails from my Gmail in one run, then checked none were left
His Gmail Promotions tab held 7,102 promo emails built up over years of online shopping. He asked the AI to clear them. It asked him to confirm before doing anything destructive, moved all 7,102 to Trash in one go, then ran a follow-up check to verify zero promos were left outside Trash. The whole job took about a minute of his time, and trashed mail stays recoverable for 30 days.
An AI searches for jobs while I sleep
He wrote down what he is looking for in a criteria file and an AI on his VPS does the searching 3 to 5 times a week. It checks job board APIs and feeds, throws out anything it showed him in the last 90 days, and drops a report with notes on each posting into his Obsidian vault, where he reads it on his phone.
This only partly worked. Worth knowing before you try it.
His own the AI review action checks every pull request and beats the review services he tried
Custom GitHub Action running the AI on a self hosted runner reviews PRs on publish or tag, works better than the review services they tried.
A local LLM helped him build a safe bare-metal starter system in a week
A local LLM helped him build a sub-1500-line memory-safe baremetal Rust init system with a test suite in about a week, work that would have taken months by hand.
I gave it jobs on a schedule instead of one off tasks
His AI runs four standing jobs: each evening it checks new job posts in his niche and emails him only the ones worth his time; his messy end of day notes come back as tasks with due dates in his to do app; receipts he forwards get logged into a spreadsheet by amount, date and category; and on Sundays it tells him what slipped and what he keeps avoiding.
Opus recreated a rival's ad as code animation in under an hour, one logo off
Opus 5.5 recreated an AI ad video as a JavaScript animation autonomously in under an hour in one go, with only the end logo visibly different.
Agency used AI agent and hit record revenue
Marketing agency Truppel used an agent inside Clint to answer clients faster after 3 months of missing targets. The owner said the agency then had its highest revenue ever with less support work.
Every night my bots turn the fixes people make into new rules
When a person fixes something one of his bots wrote, the bot writes down what it said and what the person changed it to. A nightly task reads those changes and writes the rule it thinks they point to, and in a year the bots have picked up more than 40 rules this way.
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.