OpenLedger is a blockchain infrastructure specifically designed for AI, aiming to unleash the liquidity of data, models, and AI agents through decentralized mechanisms, enabling fair monetization for contributors. It embeds AI participation in the underlying design, with all components running on-chain from model training to agent deployment, supporting EVM compatibility and adhering to Ethereum standards, achieving zero-friction integration of wallets, smart contracts, and L2 ecosystems. Below, the technical details are elaborated from aspects such as core architecture, key components, and technical mechanisms.

1. Blockchain Infrastructure

  • Layer 2 Foundation: OpenLedger is built on OP Stack (Optimism framework), utilizing Optimistic Rollup technology as a Layer 2 solution. This ensures high throughput (very high TPS) and low transaction fees (negligible), suitable for large-scale data processing and frequent interactions in AI operations. It inherits Ethereum's security while providing an efficient data availability layer through EigenDA, reducing the main chain's burden.

  • EVM Compatibility: Fully compatible with Ethereum Virtual Machine, supporting developers to seamlessly migrate smart contracts and tools. Zero friction connections include wallets (like MetaMask), DeFi protocols, and L2 ecosystems (like Optimism or Arbitrum) without additional adaptation.

  • Native Token $OPEN: Used for staking, governance, node rewards, data provisioning, and gas fee payments. OPEN drives the network economy, ensuring contributors earn rewards through staking.

2. Core Components

The OpenLedger ecosystem is built around the "three pillars": Datanets (Data Networks), Specialized Models, and Attribution Systems. These components operate on-chain to ensure transparency and auditability.

  • Datanets (Data Networks):

    • Decentralized data collection and curation network, allowing users to create new Datanets or contribute existing datasets for training domain-specific language models (SLMs).

    • Technical Mechanism: Quality control through community consensus mechanism, including data validation and provenance tracking. Data uploads, enrichment, and structuring (like collecting from the internet via OpenLedger browser extension) are all recorded on-chain.

    • Example Applications: Collecting threat data for cybersecurity models or aggregating grammar rules and translations for language models, supporting scaling from personal contributions to enterprise-level datasets.

  • ModelFactory and OpenLoRA (Model Factory and Efficient Deployment):

    • ModelFactory: A decentralized tool for training and fine-tuning AI models using Datanets data. Supports the complete process from dataset upload to model release, emphasizing community collaboration.

    • OpenLoRA: Advanced variant of LoRA (Low-Rank Adaptation) technology, allowing efficient deployment of multiple models on a single GPU, reducing costs by up to 90%. This is achieved through parameter-efficient fine-tuning (PEFT), optimizing resource utilization, and supporting real-time inference and monetization (like accessing models through blockchain payments).

    • Specialized Models: Generate domain-specific SLMs (like coding copilots or smart assistants), with model hosting and inference decentralized, ensuring quality through network consensus.

  • Attribution Systems:

    • Proof of Attribution: The core mechanism records all contributions (data, computation, or algorithm tuning) permanently on the blockchain. When models are used to generate outputs (like chats, tasks, or API calls), the system tracks model provenance, training data, and contributors to automate royalty distribution.

    • Transparent Tracking: Each AI interaction (like inference) links back to its origin, ensuring real-time compensation. Immutable records prevent tampering, supporting a fair economic model.

3. Governance and Security

  • Governance Framework: Adopts OpenZeppelin's modular Governor framework, enabling hybrid on-chain governance. OPEN holders can participate in proposals, voting, and parameter adjustments, ensuring community-driven governance.

  • Security: Provides enterprise-level security through fraud proof of Optimistic Rollup and data availability sampling of EigenDA. Secured investments from Polychain Capital and others ($8 million), with a commitment of $25 million AI-Web3 developer fund.

  • Testnet Status: Current testnet is open, supporting Datanets creation, model training, and reward points. Developers can log in to the app via social authentication to explore full-chain operations.

4. Advantages and Applications

  • Liquidity Release: Shifting AI from closed systems to open markets, contributors can monetize data/models through OPEN, supporting DeFi integration and AI agent participation (like autonomous trading).

  • Performance Metrics: High TPS, low fees, suitable for large-scale AI workloads. Ecosystem tools include browser extensions (data collection) and APIs (model access).

  • Ecosystem Integration: Collaborating with Cookie DAO and others to promote the marketization of AI data, attracting 10k+ users to participate in SNAP events.

OpenLedger constructs a transparent and fair AI blockchain ecosystem through these technical details, addressing issues of data monopoly and unfair contribution in traditional AI. In the future, it will further expand into more industry SLMs.

\u003cm-133/\u003e

\u003ct-90/\u003e \u003cc-92/\u003e