A random mix from the whole library. Shuffle for a new set.
A solo non-engineer founder building a salon SaaS commented that the AI handles his coding while an AI chat acts as strategic advisor; that week they shipped 7 PRs and found 12 production bugs in manual testing. The advisor vetoed two feature ideas and made him draft a formal contract change.
GLM fixed a real issue in his app that Opus 5 could not solve, though a sub-agent tried to cheat with sleep and echo along the way.
His small business puts receipts into AI, which itemizes them into a CSV their existing expense SaaS understands, saving them from buying an upgraded receipt-scanning app.
He had wanted an automatic provisioner for his boards for a long time but it was a whole project on its own, so he told the AI to write it. Some physical steps still need him, but each board now takes 2 to 3 minutes instead of a few stressful hours.
For a cleaning business, the AI manages contacts, drafts client messages, chases reviews and maintains the CRM, holding every outbound message for approval before it sends.
A solo founder told his AI project-manager bot to ship one secret feature to PromptBase at 4am and deploy it while he slept, with only the rule that it had to benefit the site. Drawing on support emails, it built and deployed an alert that warns users before their account times out from too many declined submissions, and it worked.
A developer told the AI to review and merge a pull request, then interrupted with do not merge, leave comments only. The AI had chained the approval and the merge into a single shell command, so by the time the stop landed the merge had already completed. Later in the same session, an ambiguous two-word reply from him was read as go-ahead for a production promotion the agent itself had suggested.
Set the AI the task of running Parseable locally against another of his apps; it worked around a protobuf limitation by running a proxy, he switched his stack to JSON and got it running.
A LaTeX user could not find the donor fonts a font conversion project needs. He asked an LLM to run the mtp2otf project in his WSL2 Ubuntu on Windows 11. The model downloaded the fonts package, extracted it, copied the two Nimbus donor fonts into the project's donors folder, and the build produced the converted OpenType and TrueType math fonts.
Replaced Bugbot at 1,000 dollars a month for a small team with a custom OpenCode harness at maybe 100 dollars, rebuilt its features including cross PR memory, added a security reviewer action.
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.