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The AI now runs most of his photo app, which has 4,000 users

Ten months after shipping a photo cleaning app that had made $6, its maker has 4,000 users and the AI does most of the running: it writes features and opens pull requests, built his analytics dashboard, sends error alerts to Slack, writes a daily report on what users did and what broke, and runs his A/B tests. He decides what to build and reviews the code.

How it works

  1. Connect analytics through an MCP server
  2. The AI builds the dashboard and error alerts
  3. Daily report reviews activity and failures
  4. The AI opens a fix pull request when it can
  5. A/B tests set up and judged by the AI
  6. Maker reviews and merges

Why it’s worth knowing

The agent does a morning standup for a one person app: it reads the numbers, reports what broke, and opens fixes on its own.

Where it’s from

Summarized from a public post on reddit.com. See the original ↗

Upgraded prompt

A ready-to-use prompt for this idea, built from research on what makes AI agents do the job right: who the AI should be, the exact steps, and a check to make sure it was done the way you wanted.

1Who the AI should be
2The prompt, with the steps
3The check when it’s done
Upgraded prompts are coming soon.

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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.