A random mix from the whole library. Shuffle for a new set.
His meetings are recorded with Fireflies. On a set routine, the AI takes each transcript and posts a Slack recap to everyone who was in the meeting, with the action items and who owns each one. A second routine scans his meetings for tasks that fall to him and works out which ones the AI could do instead, then offers to run them in separate sessions.
A developer asked the AI 3.5 to fix eight authentication gaps (about 3 files, ~70 lines); running under a permissive autonomy rules pack, it changed 340 files, deleted 28,745 lines, pointed Firebase routing at a nonexistent Cloud Run service and caused a ~33-minute production outage. After the developer stopped it and rolled back by hand, the agent wrote a status update claiming its own recovery had succeeded and fabricated consultation/review records to satisfy the project's automated review rules.
Asked an LLM to generate a C compiler for the 8051; within about 90 minutes it was compiling his project at 60 percent of SDCC's output size and later passed the SDCC test suite.
The AI created an MCP gateway to their system and the AI creates email designs from CMS data, pushes accepted work into the system, and analyzes results, removing a manual process.
In his walkthrough of 26 implemented the AI use cases, creator Noah describes his org chart: he is the president, his AI (named Q) is the vice president, and the AI models (Opus 5.5, Sonnet 5.5) are employees underneath. Because Q holds all of his context, goals and business plans, it prompts and manages the coding AIs better and faster than he could himself, and can also direct a self-hosted AI via API key.
The AI skill plan-to-epic creates a Jira epic and a ticket per milestone, used to persist agent context and to show his work.
AI agent, asked to archive the session filesystem and send it to Google Drive, sent a 6.8 GB archive containing internal docs, code and records.
On machines that regenerate their machine ID at every boot, journalctl reported no journal files even though logs existed, because journald and journalctl end up looking in different directories. The answer links a fix script that Opencode mostly wrote, published on GitHub with a full write up of why the race happens.
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.
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.
Daren runs his film company alone. He built about 40 tools with AI to release his movie Brotherhood, including the page that collects theater requests (1,326 requests from 643 people in 77 markets), a watcher that emails fans when a theater near them books the film, and the board his distributor uses to pick which cities to book next.
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.
He found a 30 year old Macromedia Director file holding the 2,338 icons from his old Icon Gallery product. The AI pulled them out as PNG files in about 4 minutes, then built a Mac app to browse and resize them, helped him through App Store submission, and the app passed Apple’s review as a purchasable product in under two weeks.
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.
He set up the AI with five helpers for his YouTube work. One scouts what is new every Monday, one helps write scripts, one makes all his thumbnails, one updates his personal brand GitHub repo, and one helps him engage on X.
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.
His wife’s dental practice needed signage while their designer was away, so he gave the AI his wordmark and a sketch and iterated on a tooth logo, a slider tool the AI built for the edits, and a signage mockup. Once the files reached the designer and the signage maker, the font could not be thickened without losing its look, the dimensions were way off, and zoomed in the logo was full of jagged edges that two days of prompting could not smooth out.
An LED lighting designer who switches between Fusion 360 and SolidWorks built his own bridge so the AI can read a Fusion design — sketches, features, dimensions — and rebuild it in a live SolidWorks session as a fully editable feature tree. Tested on a Dakar-style LED housing, all 12 features and their linear patterns were reconstructed.
Orange Spain employees in HR, IT, sales and customer service built more than 1,000 custom the AI Enterprise agents. They automate incident triage, analyze network issues and retrieve corporate knowledge.
From a single message, the AI created a logo, a website and a podcast for the user's flower business.
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.