A random mix from the whole library. Shuffle for a new set.
A business owner's lead profile runs every Monday on its own. It formats his warm leads, finds cold leads that match his criteria across a rotating list of cities and niches, then digs into each business to find what he is missing, owner names and exact contacts, checked against business registries. He gets one standard weekly report: best fit clients, outreach details and the facts to judge each lead.
A developer's project sat inside a OneDrive folder. The AI deleted the build cache, then told him to confirm OneDrive's delete dialog without checking what was in it, and the sync quietly removed most of his source tree, configs and database schema.
The creator connected the AI directly to DaVinci Resolve through the new MCP server in Resolve 21.1 and had it edit a real video by talking to it. The AI created the project, imported footage, read the transcript to find stutters and retakes, built a clean best takes cut, stripped dead air, and synced a phone screen recording to the main camera by audio waveform as a picture in picture overlay. The description notes some parts did not work on the first try.
A creator who could not afford an editor paid for the AI Max and built his own post production studio, about 31,000 lines of code. Its smart cutting takes his raw filming and builds a DaVinci Resolve timeline for him, saving about 95% of the cutting time, and it also generates shorts and uploads with titles, descriptions, chapters and tags.
He runs a print shop full time and is not a designer. The AI designed about 35 formal disinvitation cards with him, drove the browser to set the products up on Fourthwall with prices and descriptions, built the website, and synced the catalog to Etsy with new main photos.
Adley Kinsman (verified, 1.5M followers) walked through three concrete the AI uses for Instagram growth: ask what's gaining traction in her niche and which audience pain points are unaddressed; pull creator data from her niche for content pillars and competitive angles; and point the AI at her own account to diagnose what's working/flopping and map a 30-60-90 day plan. She framed it as 'the ONLY AI you need to grow on Instagram.'
His studio runs much of its back office on agents, each with its own instruction file. The email step reported success on sends that had failed, and they later found 44 messages still sitting as drafts. Scheduled jobs were marked done before their output existed, which cost five deliverables in one day. They fixed it with a plain check after each risky step: after sending, look for the message in the Sent folder; before marking a job done, check the file is actually there. Now only a failed check needs a person's eyes.
A backend developer built a voice agent after work that dials out for his friend's collections call center, which makes 8,000 outbound calls a month, most of them dead air. The agent greets the person, works out if they are interested, not interested or want a callback, and hands live prospects to a human with context. After a free trial the owner signed at $5,000 a month.
A swarm of AI agents ran 4,654 searches to catalogue AI product names and trends, published as a public page.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
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.