A random mix from the whole library. Shuffle for a new set.
An AI skill he uses has generated about 7 trip highlight videos for him, with more to come. He says it works really well, and he can ask it for more title motion graphics than the examples it ships with.
Cemex's Consult HR agent in Microsoft Teams explains the vision benefit from SharePoint policy documents, then presents an Adaptive Card in the same chat where the employee enters details and uploads the invoice image. On submit, Power Platform workflows route the claim into ServiceNow and immediately return a live tracking ticket number in Teams.
He uses AI Desktop as his social media manager and mailing list manager. Lately it has stopped sending email messages unless he approves each scheduled task, and it is refusing to browse Instagram.
A solo developer is building an independent Rust blockchain from a white paper. AI coding agents write most of the code under his direction, and AI models review it. An agent implemented the fix for a stake counting overflow in the consensus check, and the developer decides what merges and runs the tests.
A corporate salesperson who cannot code keeps all his customer files in one folder that the AI reads directly. Before a meeting it pulls out up to three things to check, and after a meeting it turns his voice memo into minutes and a thank you email and files them.
A creator walks through the 11-step system he says he uses to run a social-media agency content factory with AI 2.0 and its Cue agent, using his own VGC brand from setup to reporting. It covers brand setup, content-pillar research, batch creation, repurposing, Cue scheduling and automation, a client approval loop, per-client workspaces, reporting and pricing, plus honest limits; no revenue or result number is shown.
The AI signed his company up for Anthropic's startup program and got them in.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
He gave the AI 45 podcast files, each about an hour and a half long. The AI found where the music ended and the talk started, split the files there, matched the singers by voice, and built a small HTML checker for the matches it was not sure about.
His mom runs a wedding venue in southern Italy and got 15 to 20 inquiry emails a day, each taking 20 to 30 minutes, which was 70 to 80% of her week. The AI coworker he built now works the inquiries inside her 10 year old booking system, which has no API, and she approves only special quotes. After three weeks the typical inquiry is processed within 5 minutes and the system saves her over 120 hours a month for about $350 a month.
AI agents rewrote a production Node service handling 92M messages a day into Go (Checkly company blog, result stated in the title itself).
His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
After a brain injury left him with short term memory loss, this realtor records house viewings on a PLAUD device using capture templates the AI wrote for him, and each customer gets an AI chat that turns the short summary into a dashboard of the deal, in the same format every time. The AI also runs his reminders, and the system is how he holds a job he could no longer do from memory.
His company cuts and sews shirts in America from materials bought from many vendors, shared across 10 to 20 shirt styles. Working out the true cost of a finished shirt by hand was a mess. Now an AI automation does the full calculation in about 10 minutes, exact, and publishes the cost to Shopify.
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 has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
Opus 5.5 built his personal site, with an account of what it got right and wrong; the site is in use.
Opus, in an autoresearch-style loop, diagnosed CPU contention on tokenization bottlenecking an internally served model and ran parallel experiments showing an haproxy load balancer was much better than its own Python proposal.
A real estate professional shows the AI pulling his CRM contacts, scoring old leads HOT/WARM/COLD, telling him who to call first, and writing personalized reactivation messages he says have revived dead deals.
Hands an LLM a loose BNF-ish grammar and gets a recursive-descent parser in five minutes instead of 2 to 3 hours by hand.
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.