Technical Architecture
EveryAI's technical architecture is designed around three core principles: "Decentralization", "Privacy Security", and "Intelligent Collaboration", deeply integrating blockchain and AI technologies to build a low-threshold, secure, and scalable AI service network.
1. Blockchain Layer
Core Functions:
Recording and Verification: All AI tasks (such as image generation, data analysis) are recorded and validated on the blockchain, ensuring transparent and tamper-proof processes.
Token Incentives: Users receive automatic token rewards via smart contracts for contributing idle computing power or data, eliminating dependence on centralized platform accounting.
Cross-Chain Compatibility: Supports multi-chain payments (e.g., ETH, SOL), allowing users to pay service fees with various cryptocurrencies and breaking ecosystem barriers.
Technical Characteristics:
Low-Barrier Entry: Developed on BSC (Binance Smart Chain), offering fast transaction speeds and low fees.
Censorship Resistance: No centralized servers, with distributed task data storage to avoid single points of failure and policy blockades.
2. DePIN Layer
Integrates idle computing resources from personal computers, mobile phones, and IoT devices into a distributed AI computing pool, dramatically reducing service costs.
Technical Principles:
Dynamic Matching: Automatically allocates tasks to optimal devices based on requirements (such as computing intensity, response speed).
Example: High-definition video rendering tasks prioritize high-performance GPUs, while text processing tasks can be distributed to ordinary mobile phones.
Redundant Verification: The same task is processed in parallel by multiple devices, using consensus mechanisms to ensure result accuracy and prevent malicious node manipulation.
Technical Characteristics:
Heterogeneous Compatibility: Supports mixed computing across different chip brands (e.g., NVIDIA GPUs, Huawei Ascent, mobile chipsets).
Green Energy Efficiency: Utilizes existing device idle resources, avoiding repeated construction of high-energy data centers.
3. AI Agent
Serves as an intelligent intermediary between users and the decentralized network, simplifying complex technical processes to "one-sentence requirements" for seamless experience.
Technical Principles:
Natural Language Interaction: Users can directly input "Help me generate a popular science article about carbon neutrality", and the Agent automatically breaks down tasks, calls models, and allocates computing power.
Context Synchronization: Uses encrypted identifiers (like CID) to record user historical operations, automatically synchronizing progress and preferences across devices.
Privacy Custody: Sensitive data (such as medical records) is processed only locally, with the Agent uploading only encrypted task features, not raw data.
Technical Characteristics:
Zero Learning Curve: No need to understand blockchain or distributed computing, enabling easy use for novice users.
Personalized Service: Recommends models based on user habits (e.g., prioritizing open-source models to reduce costs).
4. Privacy Computing
Ensures data remains "usable but invisible" during task execution, comprehensively solving privacy leakage risks.
Key Technologies:
Federated Learning:
Principle: Data remains on local devices, only model parameter updates are shared (like "learning notes"), not raw data.
Case: 10 hospitals jointly train an AI diagnostic model without sharing patient privacy data.
Homomorphic Encryption:
Principle: Data remains encrypted during computation, with results decrypting to match plaintext processing.
Case: Encrypted financial data directly generates statistical reports without decryption.
Technical Characteristics:
Compliance: Meets strict privacy law requirements like GDPR and HIPAA.
Trustless Collaboration: Different organizations can use AI capabilities without data exposure.
5. Developer Ecosystem
Attracts developers and open-source communities to contribute models, creating a flywheel effect where "the more people use it, the more powerful the network becomes".
Technical Principles:
Model as a Service (MaaS): Developers upload AI models (like DeepSeek, Llama 3) to the EveryAI network, sharing revenue based on usage.
Federated Fine-Tuning: Developers can optimize models in encrypted environments using local user data, improving accuracy without touching original data.
Technical Characteristics:
Low Deployment Barrier: Supports mainstream model frameworks (like PyTorch, TensorFlow) for one-click network deployment.
Long-Tail Innovation: Niche requirement models (like dialect recognition, ancient text restoration) can receive community funding.
Architecture Diagram
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