SQUADBOOM is a decentralized protocol designed for Web3 social interaction and AI-powered collaboration. By introducing a closed-loop mechanism of "Content Training – Intelligent Feedback – Social Incentives," it redefines the value relationship between users and AI models.
Users are not required to build social networks within the platform. Instead, they contribute by uploading content or links from external social media sources (such as tweets, videos, blogs, or discussions) to feed the platform’s AI models for semantic learning and cognitive training. The AI then returns the extracted cognitive value to users with relevant needs, fostering a win-win ecosystem where “training equals contribution, and cognition equals rewards.”
2.1 The “Fuel Crisis” in AI Model Training
With the rapid advancement of large model technologies (such as GPT, Claude, and Gemini), the global demand for high-quality training data is growing explosively. However, current data acquisition methods remain heavily dependent on centralized platforms and face several challenges:
2.2 The Value of Social Data Remains Untapped
Web2 social platforms have accumulated massive volumes of human behavioral data, yet users have never truly been rewarded. While user behavior drives platform value, users receive none of the benefits from AI training.
2.3 Web3 Users Are Seeking “New Incentive Models”
Generation Z users are placing increasing importance on data sovereignty, participation-based rewards, and controllability of AI. There is a growing demand for AI participation models that both protect privacy and generate cognitive value.
Vision
To build a user-driven decentralized cognitive network protocol, where every act of social expression fuels AI intelligence, and every content contribution earns cognitive rewards.
Mission