A random mix from the whole library. Shuffle for a new set.
He has let an AI agent run his startup for 12 days. For 8 days it waited for his OK before posting, then yesterday he stopped making it wait, and by evening it had posted on TikTok, Instagram and YouTube.
His general contracting firm built an AI program that searches for signals, warms up emails and sends personal outreach to find work. It worked so well they had to turn it off twice: a $120,000 bid for a four season room was accepted, painter responses came back within seven minutes, and they now have subcontractors across the state after struggling to find 30 before.
Another commenter in the same Facebook group thread described telling the AI to review their existing listings to learn their writing style, then uploading item photos so the AI could draft new marketplace listings and pricing for approval — which finally got long-stored closet items listed.
The Superpowers the AI plugin turned a small feature change into 4 plans and 4 parallel agents plus a verifier agent, burned many tokens and took longer; he uninstalled it.
In the same interview, he describes building a seller campaign and a matching buyer campaign with AI, live, sitting next to a title rep. The campaigns feed the intake stack that his AI follow-up then works.
Used a work MCP server with an LLM to find why a tag was lost in traces, found in minutes what would have taken him hours of going through lengthy traces.
A women's clothing brand needed an online store synced to its 1C stock system and matched pixel for pixel to its React design, on a budget that ruled out the usual platforms. He built it solo on WordPress and WooCommerce while the AI wrote most of the code, and the store shipped with real time stock sync and zero dollars in platform licenses.
A heavy agent user runs AI as his daily operations layer: it surfaces GitHub activity and important notifications he would have missed, manages his calendar and travel schedule, and automatically books WeWork desks in whatever city he is in.
His weekly the AI routine was meant to pull Search Console data and check whether a big content change worked. It needed a Google service account key that only he could create, so it failed three weeks running in exactly the same way, and the three week comparison it was meant to take can never be taken now.
Camille, the AI assigned to DGM Flowers, spent the week building the shop's wholesale price list, a recipe sheet that works out the retail price of each arrangement, a proposal template and a rental inventory, then handled first replies to wedding leads. Every quote the shop sends now starts from real costs instead of a guess.
Agent refactored old code over two weeks, removing and consolidating duplicate client-side caches and tidying the codebase (Composer 2.5 via Cursor).
Agent built pricing and scheduling tooling that routes jobs to contractors and handles exceptions, and his complaints pipeline dried up (Airbnb and move-form parts duplicate his 50053301 records).
After fake bot orders started swarming his Shopify store and blockers failed, a store owner worked with AI to run checks every half hour: any order under $10 is auto-blocked, known fake addresses like 123 Main St are blacklisted, and he gets a report every 30 minutes. The bots still try, but far less often.
During a long pull-request review session, the AI wrote a Python script to replace one function in a large test file, bounding the replacement at the next top-level 'def' instead of the function's real end. The span swallowed 17 unrelated test functions, which were deleted silently; the model noticed only afterwards and rebuilt the file from git.
Opus 5.5 built his personal site, with an account of what it got right and wrong; the site is in use.
The AI-coded and uses his own CLI dictation tool VoxType built on parakeet-mlx.
His third party payment software has a poor UI and its routing rules came as an unfriendly XML dump. In about 15 minutes with AI he got a Python script that turns the export into a single HTML page where he can scroll the routes and click into each node for its priorities and matching criteria.
They found 24 Android vulnerabilities using their open source AI security agent.
The maker of a free bookmark extension handed the AI the CEO job: a scheduled run every night at 9pm reads the store dashboard, checks comments, scans forums for people who need the tool, plans posts, and works on the product, then files a report in Notion. Nothing public goes out until he replies go. In week one it caught a rival extension with nearly the same name and told him to rename before launch, fixed store text that no longer matched the code, and built the next version, now in store review. The honest numbers: 4 weekly users, 14 installs in 30 days, no reviews, nothing spent.
The AI researches a topic and posts a LinkedIn post twice a week on a recurring basis, with comments and replies handled in kind.
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.