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BINANCE + DIN
♻️ You have 3 ways to make profits with DIN :
🔸 Help process information while getting rewards .
🔸 Adding points in Binance Web3 Wallet in collaboration with DIN .
🔸 Participating in the Binance Square campaign in collaboration with DIN .
If you are interested, I recommend reading the entire article 📖 ... let's start by first understanding what the project consists of.
Data Processing + AI + Blockchain = DIN
This combination leverages technology to improve data processing.
Contribution = Getting rewards
The smart contract determines the rewards for all participants taking into account the value contribution Higher quality of contribution will be a better reward
DIN is designed to empower everyone to cook data for Al and get paid
Many Binance users had their first contact with DIN thanks to the collaboration with 🔶 Binance Web 3 Wallet .
More than 127,000 Binance users are participating and earning points .
• How to participate? :
🔸 Go to wallet
🔸Go to Web 3
🔸Select ( Join Now )
• All content in this article is for educational purposes 📖 .• The information in this article was obtained from Din's official website 🌐.
This article is sponsored by :
🔸@DIN Data Intelligence Network
🔸@Binance Square Official
🔸@币安广场
If you also want to participate, use the event # :
🔸 #DIN
🔸 #GODINDataForAI
🔸 #BinanceWeb3Airdrop
• At no time is investment recommended .• He advised to participate in the free activities .
🌐 DIN Architecture :
Basically consisting of 3 main phases regarding data processing :
🔸 Data Collectors 📊
🔸 Data Validators 📊
🔸 Data Vectorizers 📊
Participants will be rewarded according to their contribution to the network.
📊 Data Collectors :
The collected information is divided into 2 forms :
🔸 On-chain Data :
TransactionsWallet addressesSmart contracts
🔸 Off-chain Data :
Market sentimentsregulatory changesSocial media trends
This strategy empowers a broad spectrum of users, from casual enthusiasts to professional analysts, across sectors like crypto, medical, academic, and industrial.
• To add data is through :
🔸 Analytics
🔸 xData
This ensures access to actionable information.
📊 Data Validators :
To improve the accuracy of the and reduce the risks of data manipulation, (SUM) is used.
S : SharedU : UpdatableM : Models
📊 Data Vectorizers :
To improve the accuracy and scalability of the models, a vector conversion is performed.
🔸This means that :
Raw data is transformed into a structured format that AI models can process efficiently
🖼️ In this image you can see the entire DIN protocol :
🔸 Data Collection: Collectors gather on-chain and off-chain data from diverse sources.
🔸 Validation Routing: Data is forwarded to selected validators based on their locally deployed models .
🔸 Verification: Validators employ computational resources to predict and ascertain data accuracy.
🔸 Privacy Processing (Dataset): Validated data undergoes privacy enhancement via the ZK processor.
🔸 Model Update: The relevant model is refined with the latest data and updated across validators.
🔸 Vector Conversion: Computation nodes transform the validated data into vectors.
🔸 Privacy Processing (Vector): Vectors are processed through the ZK processor for privacy.
🔸 Data Finalization: The finalized dataset and vectors are stored on IPFS, making them accessible to third parties.
🔸Thank you very much for all the support. I hope this article has been useful. 🤝🐯🧡
🔸 If you have any suggestions, you can leave them in the comments. 🗨️
🔸 Help process information while getting rewards .
🔸 Adding points in Binance Web3 Wallet in collaboration with DIN .
🔸 Participating in the Binance Square campaign in collaboration with DIN .
If you are interested, I recommend reading the entire article 📖 ... let's start by first understanding what the project consists of.
Data Processing + AI + Blockchain = DIN
This combination leverages technology to improve data processing.
Contribution = Getting rewards
The smart contract determines the rewards for all participants taking into account the value contribution Higher quality of contribution will be a better reward
DIN is designed to empower everyone to cook data for Al and get paid
Many Binance users had their first contact with DIN thanks to the collaboration with 🔶 Binance Web 3 Wallet .
More than 127,000 Binance users are participating and earning points .
• How to participate? :
🔸 Go to wallet
🔸Go to Web 3
🔸Select ( Join Now )
• All content in this article is for educational purposes 📖 .• The information in this article was obtained from Din's official website 🌐.
This article is sponsored by :
🔸@DIN Data Intelligence Network
🔸@Binance Square Official
🔸@币安广场
If you also want to participate, use the event # :
🔸 #DIN
🔸 #GODINDataForAI
🔸 #BinanceWeb3Airdrop
• At no time is investment recommended .• He advised to participate in the free activities .
🌐 DIN Architecture :
Basically consisting of 3 main phases regarding data processing :
🔸 Data Collectors 📊
🔸 Data Validators 📊
🔸 Data Vectorizers 📊
Participants will be rewarded according to their contribution to the network.
📊 Data Collectors :
The collected information is divided into 2 forms :
🔸 On-chain Data :
TransactionsWallet addressesSmart contracts
🔸 Off-chain Data :
Market sentimentsregulatory changesSocial media trends
This strategy empowers a broad spectrum of users, from casual enthusiasts to professional analysts, across sectors like crypto, medical, academic, and industrial.
• To add data is through :
🔸 Analytics
🔸 xData
This ensures access to actionable information.
📊 Data Validators :
To improve the accuracy of the and reduce the risks of data manipulation, (SUM) is used.
S : SharedU : UpdatableM : Models
📊 Data Vectorizers :
To improve the accuracy and scalability of the models, a vector conversion is performed.
🔸This means that :
Raw data is transformed into a structured format that AI models can process efficiently
🖼️ In this image you can see the entire DIN protocol :
🔸 Data Collection: Collectors gather on-chain and off-chain data from diverse sources.
🔸 Validation Routing: Data is forwarded to selected validators based on their locally deployed models .
🔸 Verification: Validators employ computational resources to predict and ascertain data accuracy.
🔸 Privacy Processing (Dataset): Validated data undergoes privacy enhancement via the ZK processor.
🔸 Model Update: The relevant model is refined with the latest data and updated across validators.
🔸 Vector Conversion: Computation nodes transform the validated data into vectors.
🔸 Privacy Processing (Vector): Vectors are processed through the ZK processor for privacy.
🔸 Data Finalization: The finalized dataset and vectors are stored on IPFS, making them accessible to third parties.
🔸Thank you very much for all the support. I hope this article has been useful. 🤝🐯🧡
🔸 If you have any suggestions, you can leave them in the comments. 🗨️
Disclaimer: Includes third-party opinions. No financial advice. May include sponsored content. See T&Cs.
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