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After years of selling virtual products, I started using Codex to turn selection experience into a systematic process

Codify the experience of finding keywords, viewing products, and analyzing stores into rules for Agents to execute continuously.

Summary

Review the results before exploring further

Yuan He worked on virtual products for years and found that selection methods were plentiful, but consistent execution and recording were difficult. He began breaking down research paths between keywords, products, and stores into rules, attempting to let Codex and browser automation handle repetitive searches while retaining demand judgment himself. The original post focused on methods and system building, disclosing no sales or income generated by this system.

Our Take

Three things to consider about this case

Why it matters
The author aims to automate existing demand research methods, not have models randomly recommend products.
Who it suits
Suitable for those with product selection experience who are being slowed down by repetitive searching and organizing work.
What to check first
Currently a review of methods and systems; the original post did not provide operating results for this system.
Our Suggestions / Try This
Who it suits
Content operators and e-commerce practitioners
What to try first
Select one product or set of materials for a small-scale test, recording publication and feedback.
What to measure
Compare genuine inquiries, clicks, and transaction feedback, recording costs and revenue separately.
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What does the original post cover?

  • How to break down years of product selection experience into rules executable by Agents?
  • Where should the boundary between automatic search and human judgment be placed?
  • How to avoid finding a batch of products every day without accumulating reusable research results?
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