Cheat on Content is an AI-assisted workflow for creators who want to make content performance measurable instead of relying on instinct alone. It turns every post into a structured experiment by asking creators to score ideas, make blind predictions, publish, review results after a defined time window, and evolve their own content rubric. Rather than generating posts for the creator, it focuses on sharpening judgment and helping users understand why certain content performs better. The project is built around the loop of score, predict, publish, retro, and improve. It is aimed at creators, marketers, and operators who want to build a repeatable system for learning from every published piece. Its value is strongest for people who already create consistently but need a better way to extract insight from their output.

Features

  • Content scoring workflow
  • Blind performance prediction
  • Post-publishing retrospectives
  • Evolving creator rubric
  • Templates and starter rubrics
  • Experiment-based content improvement system

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Categories

AI Agents

License

MIT License

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Additional Project Details

Programming Language

Python

Related Categories

Python AI Agents

Registered

2026-05-14