Why choose BitX

1. UGC-Driven: Predict Anything, Created by Anyone

BitX allows any user to create prediction markets in a permissionless manner, covering diverse areas such as crypto, sports, business, entertainment, and esports. It breaks down information silos, turning individual insights into public assets.

2. Socialized Engagement: Value Resonance Among Users, KOLs, and Institution

BitX builds a social prediction network where individual users, KOL content creators, and institutions co-participate. Judgments are no longer just opinions—they become channels for influence and value monetization. KOLs can publish expert insights through prediction columns, and institutions can leverage data trends for decision-making support.

3. Dual Incentive Mechanism: Monetizing Cognition with Long-Term Rewards

Users can earn USDT profit-sharing rewards by creating topics, sharing, predicting, raising disputes, or participating in resolution—earning up to 60% of the platform's trading fees. Additionally, BitX has designed identity tags and a point system to empower long-term contributors with future token incentives and platform dividends.

4. DAO Governance + Native Oracle Ensuring Fairness

The platform adopts a decentralized autonomous governance mechanism. Topic resolutions, disputes, and rulings are completed through community voting and bound by staking mechanisms. This ensures transparency and credibility throughout the process, preventing manipulation.

5. Multi-Layered Security: Content Compliance + Smart Contract Audits

BitX employs an AI risk control system to filter sensitive topics, ensuring legal compliance. All smart contracts on the platform are audited by SlowMist, with asset custody protected by Matrixport’s institutional-grade multi-signature and cold wallet solutions.

6. Optimal Application Scenario for AI: High-Frequency Predictions + Data Feedback

BitX is an innovative platform integrating the cognitive economy with AI. It generates a large volume of high-quality prediction data daily, which continuously feeds back into AI models to enhance decision-making capabilities—realizing a closed loop of “high-frequency usage + data feedback.”