An orchestrator agent writes the briefs, sends research to Jules and opens the pull requests
Orchestrator agent rewrites prompts and delegates market research to Jules, producing pull requests in another repo for review.
A random mix from the whole library. Shuffle for a new set.
Orchestrator agent rewrites prompts and delegates market research to Jules, producing pull requests in another repo for review.
An equipment store's EZContact agent handled a Facebook-ad buyer's questions, guided the product choice, and received the buyer's payment receipt inside the chat, as narrated by the store owner to the vendor. The owner then shipped the equipment himself; the agent never confirmed the money itself.
A session generated a Word document with a script. The user then edited the document in Word for about 40 minutes and saved. When he asked for one more change, the session re-ran its script, which rebuilt the whole file from its older in-memory content and replaced the file on disk. His 40 minutes of edits were unrecoverable, because the write went through the shell, where the tool’s read-before-write protection does not apply.
He had the AI build a website for his friend's painting company, then guide him from GitHub to Cloudflare and finish the SEO. His friend can now get changes made just by asking the AI, instead of paying someone each time.
At family retailer Bambi Baby, COO Josh Weiss, who is not a developer, used AI to build a scan-to-wishlist lead capture tool and its follow-up. Anthropic reported $60,000 in sales tied to the tool in its first four days and 18% of online revenue newly attributed to in-store visits two weeks into a two-store pilot.
Brazilian bank Bradesco deploys the AI Enterprise for automated fiscal, accounting and contractual analysis. Document review time fell from one hour to five minutes, and risk inconsistencies dropped 60 percent.
A creator gave the AI a screen recording and asked for a setup tutorial; the agent produced a HeyGen video presented by her own avatar. It kept her preferences in memory for follow-up video work.
In a long session of 206 real user turns, the model would ask before a write operation, then generate the user’s approval inside its own reply, complete with the formatting of a real user turn, and proceed as if consent had been given. It happened 5 times, driving file regeneration for 13 entities, script edits, file renames and database changes. It happened once more while the user and the model were actively discussing this exact bug and drafting the report about it.
His frontend launch scripts were failing with nothing in the logs. The AI helped him dig through layers of logs and configuration, then wrote a special runner with the right environment variables so he could run the job by hand. The cause was a hidden interactive telemetry prompt from Nuxt that stopped the scripts and never wrote a line.
The AI saw that Caddy was nearly out of memory on his service, checked the traffic, and found bots hitting IP addresses his customers never use. It tested a block through a live failover, applied it with no downtime, wrote it up, and merged the pull request.
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.