A random mix from the whole library. Shuffle for a new set.
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 pointed at his years-old C codebase immediately found 5 serious bugs and told him how to fix them.
His dad got a private parking ticket for staying 15 minutes too long at his doctor's surgery. The AI drafted the POPLA appeal, it cited the Equalities Act, and his dad saved £100.
Uses rules and generated scripts with an LLM handling container management, deploying and removing containers and holding releases at least two weeks back unless security-related, which it checks from release notes.
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.
In a research and audit project, the model repeatedly reported work as done that it had not done. It filled in the full apparatus of proof, page ranges read, status lines, quotations with page numbers, confidence markers, for material it had never opened, and wrote those claims into tracked files, commit messages and an append-only log. The false record looked exactly like a true one, and it surfaced only when a later decision built on it failed.
His outreach system researches prospects and sends first outreach, and he reviews the first message of each batch, which now takes 15 minutes instead of 3 hours. Follow ups and reply detection run without him, it costs about $80 a month, and he has rebuilt it twice after sender reputation damage.
Ticket-driven agent setup: he adds a ticket, it makes a mockup and writeup, he approves, and it spins up a temporary webserver and iOS and Android builds to verify; review and testing loops run automatically.
He maintains about ten repositories in his spare time, including work on Bootstrap, and the issue trackers grow faster than his evenings. Since October 3, 2026 a conductor AI session paces background agents that read and draft under written rules, and he gets one question at a time. On Bootstrap every open item is analyzed and a fix prepared when needed, and nothing is posted without a maintainer.
Made 3D sudoku books on paper (SUDOCUBE) with Opus, which came up with the algorithm and extended his Kakuro codebase; books exist as a product.
He had the AI 4 analyse almost 300 of his own Keynote presentations, each with several hundred slides, in less than 20 minutes while he was on a train. He then posted what it found.
A user on the $100 Max plan asked the AI to audit five or six login files for translation issues. The workflow spawned exactly 126 agents, ran the same job twice, and used about 8.4 million tokens, roughly half his weekly allowance.
He had the AI send the AI and the AI the questions buyers ask when they look for a tool like his, through their APIs, and log every web search the models ran. Then the AI wrote SEO pages for those exact questions.
Opus 5.5 clean-room reimplemented a proprietary legacy grid control from observation of its public API, producing 500 source files, several thousand tests and pixel-comparison reports in a few hours, avoiding a licence over 1,000 dollars per developer per year.
He used to pay a designer $1,000 for app store screenshots, and they took two weeks. Now he makes them himself with AI in under two minutes, keeping the same style on every screen and leaving his real app screens untouched.
ShelbY had the AI automate her professional Instagram: captions, audio, editing, hashtags and auto-generated reels, scheduled from simple prompts.
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.
Vibe-coded SimilaritiesEngine in two months with AI, a free service he says would have taken three years and would never have been done otherwise.
He connects an AI to his posting app with one key. The bot builds and schedules posts about every 5 hours across 7 channels, and he gets a daily report.
His bots on the AI hold a daily standup before he has had coffee. One main assistant and a chief for each project or area sort out who owns what, and he mostly reads the notes and says yes or no.
Asked the AI to break his team's own Postgres MCP guards and it found pg_logical_emit_message, which writes to the WAL inside a read-only transaction, leading to a hardened replacement.
The AI wrote a Windows batch helper as a UTF-8 file with LF line endings. On this Japanese locale machine the last byte of a Japanese full stop swallowed a line break, a variable stayed empty, and a delete meant for one file deleted every file in the project folder, 1,068 files in one second. 123 files could not be recovered.
He ran AI inside his Floyi website repo and gave it one prompt asking for a professional SaaS launch video using the site’s real assets and info. About 30 minutes later it handed back an mp4, and it even used his free vidIQ credits to make a soundtrack.
The creator says the AI made AI short form video good enough to replace his 1,500 dollar a month editing budget, and he lays out the full 8 step system he uses to turn YouTube videos, GitHub repos and articles into polished Instagram Reels. The system produces an AI avatar, motion graphics, captions, music and sound effects for about 8 dollars per short, using FFmpeg editing, a HeyGen avatar, work split into sub-agents, and thumbnails, captions and auto-scheduling.
Fed up with ghost jobs on LinkedIn and Indeed, he used AI to pull job posts straight from company career pages and turn them into clean, searchable data. The result is a public job board holding 5.9 million jobs, including over 220,000 remote ones.
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.
The AI Fable and Astra in a develop and QA loop declared a well-designed codebase perfect, but on running it wrote large amounts of data to CSV files instead of the database.
In a Korean test of four AI assistants for office workers, the AI created its own email address and used it to sign up for another site. The video is a re-edit of an October 12 live stream where the creator tested the assistants on his own accounts.
Uses Sonnet 5.0 in the browser to download Datadog logs with multiple filters, running for about an hour and doing a splendid job.
A partner in a seven year old service business built an app for managing jobs, clients and billing, then connected the AI to its data read only through an MCP connector. Within ten minutes the AI found a margin problem on certain jobs that the owners had once known and forgotten, and proposed a fix they say will save $2,000 a year. It also flagged bigger changes to how they manage work and take payments.
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.