1. Contribute to Data Processing: Help process information and earn rewards.
2. Earn Points via Binance Web3 Wallet: Collaborate with DIN to accumulate points.
3. Join the Binance Square Campaign: Participate and benefit from the partnership between Binance and DIN.
If you're curious, here's a breakdown of the project:
Data Processing + AI + Blockchain = DIN
This innovative approach integrates technology to enhance data processing.
How It Works:
Contribute & Earn: Rewards are determined by the quality of contributions via smart contracts.
Quality Matters: Higher-quality contributions yield better rewards.
Empowering Users: DIN enables users to process data for AI applications while earning.
DIN and Binance Web3 Wallet Collaboration:
Over 127,000 Binance users are already participating and earning points.
To join:
1. Open your wallet.
2. Navigate to Web3.
3. Click Join Now.
Important Notes:
The content is for educational purposes only.
Information was sourced from DIN's official website.
No investments are recommended; focus on free activities.
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DIN Architecture: Phases of Data Processing
1. Data Collection
On-Chain Data: Includes transactions, wallet addresses, and smart contracts.
Off-Chain Data: Covers market sentiments, regulatory changes, and social media trends.
Data is contributed via analytics tools like xData, ensuring actionable insights.
2. Data Validation
Aims to enhance accuracy and reduce data manipulation risks.
Uses SUM models (S: Shared, U: Updatable, M: Models) to validate data.
3. Data Vectorization
Transforms raw data into structured formats for efficient AI model processing.
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DIN Protocol Overview:
1. Data Collection: Collectors gather diverse on-chain and off-chain data.
2. Validation Routing: Data is routed to validators using local models.
3. Verification: Validators verify data accuracy using computational resources.
4. Privacy Processing (Dataset): Validated data undergoes privacy enhancement via ZK processors.
5. Model Updates: Models are refined with the latest data across validators.
6. Vector Conversion: Validated data is converted into vectors for AI applications.
7. Privacy Processing (Vector): Vectors are further privacy-enhanced using ZK processors.
8. Data Finalization: Finalized datasets and vectors are stored on IPFS for third-party access.
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Tags to Use:
🔸 #DIN