A random mix from the whole library. Shuffle for a new set.
LLM wrote tests comparing his code to kernel syscall tables and wrote a tool tracing all kernel calls to generate a bare minimal hardened kernel config, saves him security review time.
In a long-running verification project, the model recommended buying a reference textbook, about $60 to $100, to fill a gap. The project record already contained a finding, written two weeks earlier, that the book could not answer the question that mattered. The model had even restated that disqualifying fact two paragraphs earlier in the same document before recommending the purchase anyway. It was one of four times in a single session that the model asserted or recommended without checking the record sitting in its own working folder. The user had already spent $100 that day on reference material.
His six publishing agents used to wake on timers about 20 times a day, and most wake ups had no work to do. Switching to waking only when a task arrives cut the daily bill from $5.86 to $1.21, about 79% less, and September's four long form articles cost $50.53 total.
A man with no coding experience used AI to generate a VBA script in Excel. The script fixed a manual data entry gap at his work and took the risk of typos and misreads out of the job.
A woman working in legal and regulatory compliance needed a small app, a workflow with a form and a validation process. Her CTO said they could not do it and priced it at 15 days of freelance work, and rejected it. The next day she spent a morning with AI through her company’s internal tooling and the app was done.
In CData's worked test, a Dot queried live Salesforce data through one plugin and tabled the five largest open opportunities closing this quarter, flagging all five with no logged activity in 30 days. One deal was still open six days past its close date; the account used test data, as the post discloses.
A GPT agent crunched through a famously hard Disobey conference hacker ticket challenge in an evening and a morning with occasional steering, a challenge unsolved in some years.
Added the AI rules and skills to split Terraform PRs into the smallest mergeable units, resulting in cases like 40 PRs with 38 approved and merged immediately.
A man spent a few weeks building a work AI agent for compliance checks. Today it worked through a 200 question information security audit bank and completed the 119 items that match answers to the correct standard procedures in 15 minutes. That section normally takes him a whole day.
Mis-prompted the AI on a toy project; it wasted all his tokens building a specialized Docker clone instead of building on Docker.
He did not know how to make Eclipse plugins, so he had the AI make one that highlights and type checks jOOQ’s own tag syntax. It took a single prompt, and the plugin already works.
He drops one raw podcast recording into AI. Four skills turn it into an mp3, a transcript, a description on his clipboard, and a pull request on spring.io, with no manual steps.
Answering a how are you making money thread, he says a piece of business software he used does one thing and does it badly. With the AI’s help he built a piece of software that does the same thing better, with the features he always wanted. It took a lot of bug testing and had many errors at first, but it was done in two weeks. His version costs him $50 a year in server costs instead of $150 a month, saving about $1,750 a year.
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.
Lets agents run deployments for his side projects; they read all the logs and debug fast, and deployments and CI became a breeze versus waiting days for DevOps.
He spent 35 minutes on a call with Dots trying to create a task on his remote PC. Dots kept saying its spoken context was not being sent, told him to type it himself, then created a task that created another task, while the call was still telling him it had failed.
The AI saw that Caddy was nearly out of memory on his service, checked the traffic, and found bots hitting IP addresses his customers never use. It tested a block through a live failover, applied it with no downtime, wrote it up, and merged the pull request.
László found Code Queue, the Linear-style board tracking Simmons's agent-run client work, on his Mac, checked its MCP connection and scheduled a status summary in Slack every weekday at 9 p.m. On a voice call it reported 137 issues in progress and 122 in review, then sent a proposed closure list to Slack about a minute after he hung up, closing only after approval.
An Instagram business operator uses AI as a sidekick: reading insights (reach, audience hours, why a reel flopped), publishing posts/reels/stories with drafted captions, competitive intelligence on rival accounts, scanning comments for discount-code/restock/shipping questions to hand over as warm leads, plus a scheduled daily report on what people say in the niche's comments.
He runs a five level system where the AI Design makes his Instagram carousels, from a basic prompt up to a full design system with AI generated images and a looping video cover. The first carousel made this way reached 4,500 views with 89 saves.
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.