Train to Earn

Our system uses data-driven techniques to map and interpret cryptonative activities, and its performance will be impacted by the breadth and quality of training data that is used in our primary ML models and fine-tuned LLM's.

In order to maximize the impact of our algorithms, we will enlist the help of our power-users and motivated cryptonative researchers who seek to mutually benefit from improving the algorithm's quality and timeliness at providing relevant information.

Since 2022, OzDAO has benefited from gathering quality ML training data from a small group of its own power users that became motivated by the prospect of improving the product they are using, showing that a positive feedback loop can generate quality data and results for a decentralized, data-driven organization.

WandBot will take this further with incentivizing a larger group of power users to submit data feedback and labels designed with maximizing the performance of our underlying data-driven models in a train-to-earn system, by rewarding data labelers proportionally to the quality of their contribution. Primarily, the feedback will be used in fine-tuning our LLM Agent Layer.

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