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An AI-generated phone light tool created in an hour; it sold over 300,000 units by capitalizing on women's selfie habits.

It took an hour to turn a phone screen into an adjustable-color light for makeup. This case study is interesting: a developer with no coding experience used AI to create the initial version, then leveraged the habit of women taking selfies with solid-color backgrounds to generate 1.18 million views. Subsequently, it accumulated sales exceeding 300,000 units.

Sharer: Xiaoman2026.11.01
Summary

Review the results before exploring further

It took an hour to turn a phone screen into an adjustable-color light for makeup. This case study is interesting: a developer with no coding experience used AI to create the initial version, then leveraged the habit of women taking selfies with solid-color backgrounds to generate 1.18 million views. Subsequently, it accumulated sales exceeding 300,000 units. Commenters identified and inquired about the app, while others questioned the data. This direction is suitable for replication as a beauty photography tool or a freemium application.

Our Take

Three things to consider about this case

Why it matters
First, run a small, verifiable task, recording the outcome, manual modifications, and maintenance effort.
Who it suits
Suitable for reference by those who already have similar needs and can verify effectiveness through small tasks.
What to check first
The achievements mentioned in the summary belong to the author's self-report; distinguish between revenue and profit, and consider costs, man-hours, and reproduction conditions.
Our Suggestions / Try This
Who it suits
Individuals who need to process images and assets
What to try first
Run the processing workflow using one piece of real material and compare the effects before and after processing.
What to measure
Check clarity, text accuracy, and whether results can be directly used for actual tasks.
Try Image Text Extraction ↗

What should you ask before trying this?

  • What specific need does this practice solve for you?
  • How should the minimum viable test be accepted, and how much time needs to be invested?
  • Which steps still require human processing and continuous maintenance?
Still deciding? Read our decision guides
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