What is OpenLedger (OPEN)?

OpenLedger is an AI-blockchain infrastructure that facilitates the training and deployment of specialized AI models using community-owned datasets called Datanets. All actions, such as dataset uploads, model training, reward credits, and governance participation, are executed on-chain. Users can create new Datanets, contribute to existing datasets, develop models, and publish them with transparent tokenized mechanisms. OpenLedger addresses the issue of data monopolization in traditional systems by ensuring data transparency, contributor rewards, and decentralization in AI development.

The platform creates an ecosystem where data contributors and model developers are transparently rewarded. Every AI output (e.g., chat, task, or API call) is traceable, revealing which model was used, which data it was trained on, and who contributed. This ensures fair compensation for contributors’ efforts and complete transparency in AI processes. The $OPEN token serves as the platform’s governance and transaction token, playing a key role in rewarding contributors and steering the ecosystem.

OpenLedger’s Key Features

OpenLedger offers a unique ecosystem through its integration of AI and blockchain. Here are the platform’s core features:

1. Data Collection with Datanets

OpenLedger simplifies specialized data collection through decentralized data networks called Datanets. These networks collect, validate, and distribute high-quality datasets tailored to specific domains (e.g., healthcare, finance, social media). Users can create new Datanets or contribute to existing ones. Each contribution is verified and recorded on-chain, ensuring contributors are fairly rewarded. Datanets enable specialized AI models to be more accurate, interpretable, and efficient.

2. Easy Model Training with ModelFactory

ModelFactory is a platform within the OpenLedger ecosystem designed for fine-tuning large language models (LLMs, such as LLaMA, Mistral, DeepSeek). It operates entirely through a graphical user interface (GUI), eliminating the need for command-line tools or API integrations. Users can train models using permissioned datasets, configure hyperparameters (e.g., learning rate, batch size), and monitor the training process via real-time dashboards. Additionally, a chat interface allows direct interaction with trained models.

3. Proof of Attribution

OpenLedger’s Proof of Attribution mechanism ensures that data contributions are transparently linked to AI model outputs. This cryptographic system guarantees that every contribution is traceable, verifiable, and rewardable. Contributions are evaluated based on metrics like feature-level impact and contributor reputation. Low-quality or harmful data (e.g., biased or redundant data) is penalized, while high-quality contributions are incentivized with token-based rewards. This system enhances data transparency and reduces misinformation.

4. Governance

OpenLedger employs a hybrid on-chain governance system powered by OpenZeppelin’s modular Governor framework. $OPEN token holders can actively vote on the protocol’s direction and updates, ensuring community-driven and decentralized development.

5. Cross-Chain Bridging

OpenLedger uses the OP Stack Standard Bridge, provided by AltLayer’s Rollup-as-a-Service, to ensure secure and standardized interoperability with Ethereum. The $OPEN token is locked on L1 and minted on L2, enabling secure token transfers. This system is fully compatible with standard Ethereum tools like MetaMask, Ledger, and Hardhat.

6. Efficient Model Deployment with Open LoRA

Open LoRA is a framework designed to serve thousands of fine-tuned LoRA (Low-Rank Adaptation) models on a single GPU with high efficiency and low latency. It leverages dynamic adapter loading, tensor parallelism, flash-attention, and quantization to optimize performance. This enables the simultaneous operation and cost-effective deployment of multiple models. Open LoRA is ideal for applications like personalized AI assistants and domain-specific NLP solutions.

7. API Integration

OpenLedger enables interaction with custom-trained large language models through secure API endpoints and a flexible chat interface. Developers can access and manage models using Python, JavaScript/Node.js, or curl, making it suitable for web applications, bots, and command-line environments.

How OpenLedger (OPEN) Works

OpenLedger allows users to create Datanets, contribute to existing datasets, train models, and deploy them using tokenized mechanisms. The workflow is as follows:

1. Accessing OpenLedger: Users log in via heyanon.ai using social authentication.

2. Creating and Contributing to Datanets: Users can create new Datanets or add data to existing ones. Each contribution is verified and recorded on-chain, with contributors rewarded accordingly.

3. Model Training: Through ModelFactory, users train models using their own data or Datanets. Techniques like LoRA and QLoRA enable efficient operation of multiple models on a single GPU.

4. Inference and Attribution: Every AI output tracks the model used, training data, and contributors. Contributors are rewarded based on their data’s impact.

5. Governance: $OPEN token holders vote to guide the platform’s development.

OpenLedger (OPEN) Tokenomics

$OPEN is the native token of the OpenLedger ecosystem. Tokenomics details are as follows:

• Distribution:

  • Community: 51.7%

  • Investors: 18.3%

  • Team: 15%

  • Ecosystem: 10%

  • Liquidity: 5%

• Token Utility:

  • Governance: Voting on protocol direction and updates.

  • Transaction Fees: For on-chain operations and gas fees.

  • Rewards: Incentives for data contributors and model developers.

  • Staking: For staking AI agents.

OpenLedger (OPEN) Investors

OpenLedger boasts strong investor backing. In July 2024, it raised $8 million in a seed funding round led by Polychain Capital and Borderless Capital. Other notable investors include HashKey Capital, Balaji Srinivasan, Sandeep Nailwal, MH Ventures, Mask Network, Paul Taylor, TRGC, Finality Capital Partners, WAGMI Ventures, Hash3, STIX, Sebastien Borget, Scott Moore, Sreeram Kannan, Alan Curtis, chainyoda.eth, Kenny Li, and Trevor Koverko. These investors reflect confidence in OpenLedger’s vision to lead in AI and blockchain integration.

OpenLedger (OPEN) Team

OpenLedger was founded in 2024 by Ramkumar Subramanian and Elangovan Kamesh. Ramkumar Subramanian, an expert in decentralized systems, leads the team, while Elangovan Kamesh strengthens the project’s technical foundation with his expertise in AI infrastructure. The team comprises experienced professionals who have worked with brands like Sony, Walmart, Polygon, and Hedera.

Official Links

• Website
• X (Twitter)
• Whitepaper

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What Is Quack.AI (Q)?

Quack.AI supports a complete governance lifecycle. Proposals are analyzed by AI models, assigned risk scores, and processed through automated voting and execution workflows. The platform introduces autonomous agents that act on behalf of token holders, verified through Soulbound Tokens (SBTs) to ensure only authorized entities participate in decision-making.

Quack.AI is compatible with various blockchain ecosystems, including BNB Chain, Base, Arbitrum, Linea, Avalanche, Metis, Taiko, and DuckChain. This cross-chain functionality allows decentralized autonomous organizations (DAOs) and other governance-driven systems to maintain consistent processes across diverse infrastructures.

Backed by prominent investors from the blockchain and technology sectors, such as Animoca Brands, DWF Ventures, Kenetic Capital, Skyland Ventures, 071Labs, Scaling Labs, CARV Labs, and Merlin Chain, Quack.AI leverages a modular approach and AI integration to streamline governance for decentralized communities, delivering efficient, secure, and scalable decision-making.

Challenges in Web3 Governance and Quack.AI’s Solution

Web3 governance today faces significant hurdles. Most DAOs rely on slow, manual, token-weighted voting systems that are prone to manipulation, lack scalability, and are rarely autonomous. As Web3 ecosystems expand across chains and use cases, governance must evolve from basic polling to intelligent, automated decision-making systems.

Quack.AI serves as the AI-driven governance layer for Web3, offering a modular infrastructure that automates proposal creation, risk evaluation, power delegation, and action execution across multiple chains. It transforms static voting dashboards into real-time, AI-native systems, eliminating human bottlenecks.

Unlike governance protocols limited to voting, Quack.AI provides an end-to-end solution:

• Proposal Engine: AI filters, ranks, and scores proposals in real time.

• Delegation Layer: Token holders can delegate to trusted AI agents with on-chain reputation.

• Execution Router: Approved proposals are executed trustlessly across chains.

• Treasury and Rewards: Automated triggers enable airdrops, point systems, and treasury flows.

With support for BNB Chain, Arbitrum, Metis, DuckChain, and upcoming compatibility with Solana, TON, and Base, Quack.AI is a chain-agnostic layer that any ecosystem can integrate.

A New Governance Paradigm

Quack.AI is not just a tool or protocol—it is the governance layer for Web3. It provides a foundational infrastructure for building intelligent, scalable, and compliant governance systems for DAOs, decentralized applications (dApps), and institutions. By leveraging AI agents and modular smart contracts, Quack.AI removes complexity, delays, and subjectivity from on-chain decision-making, marking the beginning of machine-native governance where agents manage, execution is instant, and coordination transcends human limitations.

Why AI-Powered Governance?

Decentralized governance is a cornerstone of blockchain ecosystems, empowering users to influence network decisions. However, low participation, inefficiency, and vulnerability to manipulation hinder its potential.

Shortcomings of Traditional DAO Governance

• Low User Participation: Voter turnout in major DAOs averages below 8%, often dominated by large token holders or founding teams.

• Inefficient Governance: The proposal-discussion-voting-execution cycle can take days or weeks.

• Technical Barriers: Voting and proposal submission require complex chain interactions, deterring non-technical users.

• Proposal Evaluation Challenges: Technical proposals are hard for users to understand, lacking standardized outcome simulation tools.

• Manipulation and Security Risks: Large accounts can sway governance, and most DAOs lack robust risk control mechanisms.

Quack.AI’s Solutions

Quack.AI automates proposal scoring, enables AI-agent voting, and ensures real-time cross-chain execution. Key benefits include:

• AI-Driven Proposal Analysis: Evaluates proposals using on-chain data and historical governance patterns.

• Delegated Agent Voting: Users can assign AI agents to vote on their behalf, ensuring informed participation.

• Autonomous Execution: Proposals are executed instantly via cross-chain smart contracts.

• Preference-Aligned, Unbiased Governance: AI agents reflect user intent, free from manipulation or emotional bias.

Vision and Objectives

Quack.AI is the AI governance layer for Web3, automating decision-making, execution, and risk-aware coordination across chains. Unlike single-chain voting tools, it is modular and chain-agnostic, enabling real-time governance for DAOs, enterprises, and communities.

Cross-Chain Integration

Quack.AI is active across over 40 ecosystems, including DuckChain, BNB Chain, Metis, Arbitrum, and zkLink. DuckChain was the first to fully implement Quack.AI’s infrastructure, with numerous projects adopting it to automate proposal flows, streamline treasury management, and delegate governance to AI agents.

Core Objectives

Quack.AI’s governance goals are built on automation, transparency, and accessibility:

• AI-Driven Governance Automation: Automates proposal processing, voting execution, and predictive modeling.

• Transparency and Data-Driven Decisions: AI generates governance reports, analyzes community sentiment, and filters malicious behavior.

• Accessibility and Participation Optimization: Enables delegation to AI agents and simplifies cross-chain governance participation.

Quack.AI combines AI efficiency with human intent to create a fast, fair, and inclusive governance standard.

For Governance Participants

Quack.AI’s Implementation on DuckChain

Traditional governance requires constant monitoring, analysis, and voting, limiting participation and slowing decisions. Quack.AI offers an autonomous, AI-powered governance layer across over 10 chains, including BNB Chain, Arbitrum, Metis, and DuckChain. Holders of $DUCK tokens, Genesis SBTs, or AI Passports can delegate governance tasks to AI agents for real-time, risk-aware, and unbiased execution.

Quack.AI Passport

• Users mint a Quack.AI Passport NFT, a gas-fee-based credential enabling voting, delegation, and reward access.

• Over 3 million Passports have been minted across BNB Chain and EVM-compatible chains.

• The Passport serves as an on-chain identity within Quack.AI’s governance layer.

Q1-Q3 Progress

• AI-Powered Proposal Analysis: Agents evaluate proposals using on-chain data and sentiment analysis.

• Autonomous Voting and Delegation: Users delegate to AI agents for instant decision execution.

• Rewarded Participation: Over 20,000 Genesis SBT holders and 3 million Passport users have engaged with governance modules.

On DuckChain, 20,000 Genesis Members delegated governance via SBTs to AI agents, generating over $3.5 million in revenue and earning $DUCK rewards.

For Developers

Quack.AI is a modular AI governance layer that powers end-to-end decision automation, execution, and risk-aware coordination. Developers can integrate Quack.AI into dApps, protocols, or ecosystems to access:

• AI-generated proposal insights

• Delegated AI voting mechanisms

• Autonomous execution workflows

• Real-time governance analytics

• On-chain reward and treasury automation

Active in over 40 projects across chains like BNB Chain, Arbitrum, Metis, Ethereum, and DuckChain, Quack.AI offers governance APIs and analytics tools for seamless integration.

Governance Hub

Quack.AI provides a cross-chain AI governance hub where communities, DAOs, and institutions interact with AI-powered governance in real time. Key features:

• AI-Driven Governance Dashboard: Displays live proposals, voting statistics, and AI-generated reports.

• Proposal Interaction and Voting: Users submit proposals and delegate votes to AI agents.

• Treasury and Reward Monitoring: Tracks revenue sharing, staking, and historical governance data.

Quack.AI (Q) Tokenomics

• Marketing and Growth: 6%

• Treasury: 11%

• Initial Airdrop: 12%

• Ecosystem and Partnerships: 15%

• Core Team: 10%

• Community: 30%

• Investors and Advisors: 11%

• Liquidity: 5%

Quack.AI Investors and Backers

Quack.AI is supported by leading blockchain and technology investors, including Animoca Brands, DWF Ventures, Kenetic Capital, Skyland Ventures, 071Labs, Scaling Labs, CARV Labs, and Merlin Chain. These backers strengthen Quack.AI’s Web3 governance vision and contribute to its ecosystem growth.

Official Links

• Website
• X (Twitter)
• Whitepaper

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What Is Multiple Network (MTP)?

Multiple Network delivers high bandwidth and low latency, widely utilized in AI model invocations, search engine interactions, and the encryption and acceleration of large-scale sensitive data, enhancing the efficiency and security of AI model training and inference.

As an emerging decentralized privacy network (DePIN Unicorn), Multiple Network’s core mission and vision are to build a decentralized network that promotes the transmission of private, encrypted digital data. Through its innovative De-WAN (Decentralized Wide Area Network) technology principles, Multiple Network utilizes blockchain, software-defined wide area network (SD-WAN), peer-to-peer (P2P) networks, and encryption technologies to enhance its privacy and data acceleration capabilities.

Empowered by De-WAN technology, Multiple Network has created an innovative decentralized platform that ensures secure, seamless, and high-speed data transmission for diverse network users, from large corporations to small-medium enterprises and individual users, regardless of their location. Multiple Network provides robust support for AI applications.

Multiple Network’s Impact on AI

Speed and Stability for AI Training and Inference

• High Bandwidth and Low Latency: Multiple Network aggregates the bandwidth of distributed nodes using P2P and SD-WAN technologies, enabling fast data transfer. This allows AI models to quickly access and transmit data during training and inference.
• Stability Assurance: SD-WAN’s dynamic routing optimization selects the best paths during network interruptions or high traffic, ensuring stable data flow and reducing transmission fluctuations.

Privacy and Security

• Anonymous Communication: The anonymity features of P2P networks conceal participant identities during data transmission, protecting user privacy.
• Encrypted Transmission: End-to-end encryption ensures data cannot be intercepted or tampered with during transfer, critical for handling sensitive data.
• Secure Data Exchange: Provides high-security standards for API calls and data exchanges during AI model usage, effectively preventing cyberattacks and data leaks.

De-WAN (Decentralized Wide Area Network) Technology

De-WAN is an innovative platform designed to enable the decentralized sharing of computing resources among nodes. As a pioneer in De-WAN technology, Multiple Network has built a sophisticated decentralized network layer and protocol for digital assets and data transmission. Leveraging blockchain, SD-WAN, P2P, and encryption technologies, De-WAN enables resource owners to contribute idle bandwidth and underutilized computing resources (CPU, memory, storage, etc.) to the network.

What Is the UDT (UDP-Based Data Transfer) Protocol?

Multiple Network adopts the UDT (UDP-Based Data Transfer) Protocol as its core protocol for real-time data transmission, distinguished by the following features:

• More Effective Congestion Detection and Management: UDT optimizes congestion control with adaptive algorithms that monitor network conditions in real-time, avoiding performance bottlenecks of traditional TCP in high-latency and high-packet-loss environments. It uses a rate-based congestion control mechanism instead of window-based control.
• More Accurate and Timely Packet Loss Detection and Recovery: UDT employs enhanced ACK (acknowledgment) and NAK (negative acknowledgment) mechanisms to promptly detect packet loss and initiate retransmission, ensuring data integrity while maintaining high transmission efficiency.

The combination of De-WAN architecture and the UDT Protocol enables Multiple Network to achieve superior transfer speed performance compared to traditional TCP Protocol. For offline data transmission, Multiple Network uses IPFS technology to provide an effective file sending and receiving mechanism.

AI Acceleration

• High Bandwidth and Low Latency: Optimizations based on P2P and SD-WAN technologies aggregate distributed node bandwidth for high-speed data transmission services.
• Stability Assurance: SD-WAN’s dynamic routing optimization switches to optimal paths during network interruptions or high traffic, ensuring stable data flow.
• Cross-Regional Data Synchronization: Rapidly synchronizes distributed datasets globally, reducing training and inference times, particularly for real-time tasks like search engine interactions or large-scale data processing.

DeAI Integration

• Distributed Network Architecture: Multiple Network operates through a decentralized architecture of distributed nodes, eliminating reliance on a single central server, enhancing attack resilience, and avoiding single points of failure.
• DeAI Ecosystem Support: The decentralized network model provides foundational support for the DeAI (Decentralized AI) ecosystem, facilitating equitable global sharing of data, models, and computational power.

Advantages of Multiple Network

• Low Entry Barriers: Supports various operating systems (Windows, Mac, Android, iOS, Linux, Unix) and device types, requiring only an internet connection and minimal hardware, making it accessible to all.
• Low Cost: Unlike costly traditional carrier lines and SD-WAN providers, Multiple Network reduces operational costs by utilizing participants’ idle computing resources.
• Decentralized with Strong Privacy: Fully decentralized and controlled by participating nodes, ensuring robust privacy and data security.
• Flexible Deployment: Requires no complex applications or lengthy preparations; users can deploy instantly, cancel anytime, and use a pay-as-you-go model.
• Stable and Robust: Globally distributed P2P nodes enhance network stability and robustness, ensuring continuous data transmission via alternate routes during failures.

Multiple Network (MTP) Tokenomics

• Network Emissions: 50%
• Airdrop (Testnet and Launchpools): 12%
• Series A: 10%
• Series B: 10%
• Team and Advisors: 7.833%
• Seed Round: 6.667%
• KOL Round: 2.5%
• Market Maker or Initial Liquidity: 1%

Total Supply: 1 billion

Multiple Network (MTP) Roadmap

• 2025 Q1: Completion of Series A investment round, launch of beta product on mainnet.
• 2025 Q2: Achieving 200,000 users and nodes, designing proprietary hardware for expansion, listing MTP token on three major centralized exchanges.
• 2025 Q3: Launch of dedicated routers.
• 2025 Q4: Integration of transmission services with industry partners (e.g., DePIN compute and storage providers).

Multiple Network (MTP) Team

The Multiple Network team comprises experienced professionals with an innovative vision. Zeus Chen, co-founder, leads the project, while Frank Cao, head of product, oversees product development. With expertise in decentralized network technologies, the team aims to position Multiple Network as a leader in the data transmission industry.

Official Links

• Website
• X (Twitter)
• Whitepaper

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What is the FLock.io Platform?

FLock.io is at the forefront of decentralized AI (dAI) development, tackling the limitations imposed by centralized control over AI innovation. When large corporations dictate AI development based on their own agendas, it often leads to amplified biases, reduced public participation, and errors stemming from lower-quality training data.

FLock.io addresses these challenges by integrating blockchain technology with federated learning, enabling communities to propose, train, and enhance models transparently while retaining control over their data. This approach eliminates centralized bottlenecks, promotes fair reward distribution, and fosters the development of ethical, high-quality AI models.

The platform comprises three core components: AI Arena, FL Alliance, and AI Marketplace. In AI Arena, models are trained and validated; in FL Alliance, they are fine-tuned with local data; and in AI Marketplace, models are deployed for applications, continuously improved through feedback. Supporting diverse use cases in industries like finance, healthcare, and gaming, FLock.io has achieved over 10,000 model calls with models like BTC-GPT.

What Can Be Done on FLock.io?

FLock.io offers a suite of tools to streamline participation in AI model development:

• AI Arena: A decentralized AI training platform where users can stake $FLOCK via train.flock.io to participate and earn rewards. Participation is possible in four roles:

  • Task Creators: Define desired models and submit tasks to the platform.

  • Training Nodes: Develop or fine-tune models as required by task creators.

  • Validators: Evaluate models submitted by training nodes.

  • Delegators: Delegate tokens to training nodes and validators to boost reward distribution.

• FL Alliance: Using a federated learning approach, thousands of participants collaboratively train a global model without sharing local data. Participants are randomly assigned as proposers (training models with local data) or voters (evaluating model updates). FLocKit provides predefined templates for tasks like LLM fine-tuning, while FL Client facilitates interaction with smart contracts and manages system-level coordination.

• AI Marketplace: A collaborative space where trained and fine-tuned models are created, enhanced, and utilized by the community. Users can propose models, contribute data, evaluate models, or apply them in various applications. Models like BTC-GPT leverage external database integration and RAG for domain-specific accuracy.

Technical Infrastructure and Security

FLock.io leverages its blockchain layer to enhance incentives and security. A proof-of-stake mechanism ensures participants uphold network integrity, with dishonest behavior penalized through token slashing. Smart contracts automate reward distribution, ensuring transparency and efficiency. AI Arena supports direct model training on user devices, maximizing performance with community data. FL Alliance preserves data privacy while enabling global model creation. The AI Marketplace facilitates model deployment and inference within virtual machine networks, ensuring scalability and security.

$FLOCK Token and Tokenomics

$FLOCK, deployed on the Base network, is the cornerstone of FLock.io’s permissionless, high-performance, decentralized AI ecosystem. It incentivizes participation and secures the network. The tokenomics distribution is as follows:

• Community: 47%

• Team and Advisors: 17%

• Ecosystem: 19.70%

• Investors: 16.30%

In AI Arena, developers compete to build top AI models in a Kaggle-style leaderboard. In FL Alliance, models from AI Arena are fine-tuned with proprietary data. The AI Marketplace enables community members to use or host these models. Participants earn rewards based on their staked $FLOCK, while malicious actions result in token slashing.

2025 Roadmap

FLock.io’s 2025 roadmap strengthens its decentralized AI vision:

• Q1 2025: Strategic partnerships with Web2 and Web3 entities like Animoca Brands and OneKey will be launched. FL Alliance will debut on testnet, and a decentralization plan will be published.

• Q2 2025: An Edge AI model for distributed device training and Moonbase Beta, an AI agent platform, will be introduced. AI Arena v2 will roll out with upgraded staking functions.

• Q3 2025: Integrations with AI agent and application platforms will enable seamless onboarding, and community members will gain the ability to create AI model training tasks.

• Q4 2025: Moonbase Stage 2 and 3 will enhance AI agent interactions, and the Sustainable Model Flywheel will create a self-sustaining ecosystem for model training and usage.

FLock.io Investors and Backers

FLock.io is backed by industry leaders, including Digital Currency Group (DCG), OKX Ventures, GSR, Fenbushi Capital, Faction, Animoca Brands, Volt Capital, Inception Capital, Gnosis, a41 Ventures, Bas1s Ventures, and Tagus Capital. These strategic and seed investors bolster FLock.io’s mission to advance decentralized AI and amplify its global impact.

FLock.io Team

The FLock.io team is led by accomplished professionals. Founder and CEO Jiahao Sun drives the platform’s vision, while CFO Vincent Z. Wang oversees financial strategies. Sameeha Rehman, as Chief of Staff, coordinates operational processes. This leadership team propels FLock.io’s goal of pioneering decentralized AI and blockchain integration.

FLock.io revolutionizes decentralized AI development, fostering ethical and transparent AI growth driven by communities. Powered by blockchain technology, the platform protects data privacy while delivering innovative AI solutions, placing the future of technology in the hands of its community.

Official Links

• Website
• X (Twitter)
• Whitepaper

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