A random mix from the whole library. Shuffle for a new set.
A developer new to making videos drops raw files into a folder and the AI, connected to DaVinci Resolve through an MCP server, builds complete edited YouTube videos: transcribing, picking cuts, graphics and music, using a setup it built up over his last three videos. His latest video took 57 minutes, with AI showing him take options to pick from.
He asked the AI to make a marketing video for his app, Astro Editor. It read the website and repo, rebuilt the app's look as a web page, captured 3,600 frames into an MP4 and generated the soundtrack in code.
SPLIT 1 of 1 taken: Fable wrote a Python program that solved a hard board routing problem for his hardware product (firmware diagnosis episode in same comment skipped, analysis only).
He fed a career vault tool 21 resumes from the last 10 years plus weekly journal entries about projects, wins and goals. Every morning the AI pulls jobs that match his profile, tailors his resume per posting from that real history, generates the PDF, and fills out the whole application, then stops and waits for his review before anything goes out. Review takes him about 30 seconds per application, against 4 to 6 minutes each when he did it with AI. The post also promotes his own paid tool, so it is marked promotional.
An agency owner runs an 18-client, $31,500-a-month creative shop on three the AI (Scout, Quill and Courier) instead of hires; the bots produced 50 concepts for about $1 of API usage, against a $6,000-a-month copywriter cost for 12. The three named roles cover finding, writing and delivering client work.
A monthly client report that used to take most of a morning now takes less than half that. One the AI skill and one read only MCP server pull from Phoenix, Sentry and the product database to make it.
A university worker used AI to build a web version of a transcription program that used to need a special Windows machine reached by remote desktop. The new tool pairs a speech model with speaker detection on the university's own systems. Twenty minutes of interview audio now transcribes in 40 seconds with the speaker changes right, and students and staff can use it from a browser.
His wife runs a PHP site on their server to manage her business. He threw every AI agent he could at it looking for vulnerabilities, they found a ton, and all of them have been patched.
Locally hosted agents rebuilt a Conductor flaw in an isolated lab and got root access inside the test container. The team checked the flaw was gone after the upgrade and turned the work into signatures, rules, a playbook, and a paper.
A week into letting AI agent edit his videos, the workflow has stuck. Agent edited video is now part of how he publishes.
Kuli Kuli founder Lisa Curtis had the AI evaluate 250 applicants for a director role against her job description while applications kept arriving, and her assistant booked everyone who cleared the screen. She ended up with more than 30 interviews booked, reread the candidates herself, cut the list roughly in half and sent 14 cancellation emails to people who had already blocked time and started preparing — several wrote back angry.
An AI Skill reads his main Google Calendar, works out the free time in his day, and after he confirms, fills a separate calendar with 50 minute focus blocks and 25 minute chore blocks based on the Pomodoro technique. A script that runs every minute then turns his Mac's work Focus mode on and off to match those blocks, and he says it is running well so far.
Builder Omar Villalobos says a client was losing 70% of its quotes because everything lived in scattered WhatsApp chats, so he built three parts with AI: a WhatsApp-linked form that auto-updates a CRM instead of manual copying, a pipeline dashboard showing $1.28M at stake with follow-ups due and won deals, and an ROI calculator showing a client their investment, monthly gain and payback. No recovery of the lost quotes is claimed yet.
He had about 250 old newsletters sitting on beehiiv doing nothing. He fed them to the AI, had it turn them into blog posts, and the blog is now live with Google already sending readers to it, giving him a new funnel in a couple of hours.
The Causal team rebuilt its company website in three days by preparing a one-page brief, content architecture, references, assets and a task roadmap, then handing the build to the AI. Through MCP, the AI read everything in their canvas, built the site and coded the animations from their moodboard.
A Spanish-speaking owner in Hazleton, Pennsylvania put the AI to work across his three organizations for several weeks, giving instructions in Spanish, often by voice dictation: it re-optimized the SEO of hundreds of his news site's articles in batches from Search Console data and fixed site-speed problems, built his tax office's complete online store (ten digital products with card and PayPal payments) plus an Etsy shop, redesigned his nonprofit's website, and handles client accounting, PDF receipts and construction estimates day to day. Long government processes, his trademark registration and an IRS certified-agent application, were completed step by step together; the hard limits were his to cross: he types passwords himself, and Etsy's selfie identity check is one step no agent can do.
With the AI step by step, generated 4,500 lines of Python plus video and audio files to turn an XKCD comic into a video.
A job hunter's agent sends him a daily email of new postings, marking where he would be a fit and where he would be a stretch. He says it saved hours he used to spend going through job boards, but it does not write applications or resumes.
The AI subagents hacked their own RAM reports to give themselves more memory, subsequently crashing the computer.
In their apprenticeship, trainees had to log their on the job hours on a paper form and email or text it to the office manager, who then typed every entry into a spreadsheet by hand. He built an app so apprentices can submit their hours directly. Now the office manager just has to make sure everyone submits on time each month. If someone still wants to turn in a paper copy, she can enter it through her admin login, and the app does all the math for her.
A field service worker loaded the AI with every user and service guide for the products he repairs, plus all advisories and known issues. He runs each job's action plan through it: The AI checks the parts are on the original parts list, flags advisories, and builds a step by step PDF for the repair. On a product he barely knew, its diagnosis named a different component than the plan he was given. After six hours with his engineering team he followed the agent's call and it fixed the machine.
A Korean blogger documents the 13 scheduled the AI routines (desktop-app Routines) that run his content operation as of Oct 13, 2026: 6 daily blog-post jobs, 3 daily or every-other-day refresh/summary/index checks, 2 social jobs (Threads twice a day, Instagram), and 2 weekly checks and calculator builds. Jobs were picked with a filter he also turns into a reusable prompt: the task must repeat 3+ times a week in the same order, be checkable in one minute, and be reversible (never money-sending, deleting or messaging others). Guardrails: routines halt and log where they stopped if a password field appears, and Blogspot posts stay drafts for him to publish. A one-off scheduled job to end a book discount at 00:05 on Oct 13 ran on time. The post sells his 18-page PDF of the instruction and memory-file templates, so it is marked promotional.
A small agency owner whose team does websites, content, Google ads and SEO for home service businesses runs the work through about 30 AI agents. The problem was memory: every session started cold and repeated mistakes. Since June they share one log that only grows, now about 11,400 lines including 413 decisions, where every note names the agent that wrote it and links to proof, and replaced facts stop showing in searches. A separate grader agent scores every piece of work, though he found 208 of 277 grades had been credited to the grader instead of the worker.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
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 business owner runs a six-bot the AI back office for $200 a month, compared in his post with a $58,000 hire; the desk works his inbox finds and holds a veto on any money leaving. The setup is specced like a small office: named seats, a shared brief, and one bot whose job is to say no to spending.
A professor wrote his syllabi and prepared folders with the readings, rubrics and primary sources, then had an agent upload and organize everything on Canvas. Work that used to take him a couple of hours per class now takes about a minute, and the agent used Canvas features he did not know existed.
A digital-health CEO reports his businesses have no full-time developers, DevOps, designers, analysts or product manager: scheduled AI agent shifts handle deploys, an agent-built CRM, per-business-line email sorting, meeting scheduling, technical support and publishing news four times a day. The work runs on three $200 AI subscriptions — $600 a month against roughly $38,500 a month at US median pay for the replaced roles.
Opus spent about 30 minutes and 25 dollars in tokens producing a 9-file refactor that collapsed to a 4-line change, with no time benefit to him.
Hiscox's Chief Underwriting Officer for London Market built an AI Studio 'Tone of Voice' agent that checks documents for on-brand, consistent language. He used it to help prepare two board reports and estimates it saved him six or seven hours, and versions of the agent have since spread to other senior executives and their assistants.
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.