History of AGI

The History of AGI: From SingularityNET to Token Evolution

Origins and Development of AGI

AGI was initially introduced as the native utility token of SingularityNET, a decentralized AI marketplace aiming to enable artificial intelligence interoperability through blockchain infrastructure. Launched to facilitate transactions within the network, AGI was envisioned as a key component in fostering an open marketplace for AI services. The project drew significant attention due to its ambitious goal of decentralizing artificial intelligence and eliminating centralized control over AI applications.

The token was distributed through an initial token sale, which garnered considerable interest from both blockchain and AI communities. Following its launch, AGI was structured as an essential medium of exchange within the SingularityNET ecosystem, allowing developers to monetize AI models and users to access a variety of AI-driven services.

Challenges and Technical Adjustments

Despite the strong conceptual framework, adoption and usage of AGI faced several hurdles. Liquidity constraints, adoption barriers, and scalability concerns were persistent challenges during the early phases. Additionally, dependency on Ethereum’s network posed issues related to transaction fees and speed, especially during periods of network congestion. These limitations led to discussions about potential technical improvements, including exploring interoperability with other blockchain ecosystems.

Over time, development teams proposed updates aimed at refining both the token’s utility and broader ecosystem functionality. Governance discussions surrounding the future of AGI played a critical role in shaping decisions around tokenomics and network upgrades.

Token Migration and Evolution

One of the most significant milestones in AGI’s history was its token migration. The need for improved functionality and ecosystem expansion led to an overhaul of the original token model. This migration process required token holders to convert their AGI holdings to a new asset, incorporating modifications designed to enhance network governance, cross-chain compatibility, and overall scalability.

The transition was met with both support and criticism. While proponents viewed it as a necessary step for long-term viability, detractors raised concerns about execution, centralization risks, and onboarding complexities associated with the migration process. The shift also led to temporary disruptions in liquidity and exchange support, which further complicated the transition period for holders.

Ongoing Impact on the Crypto Space

AGI’s journey reflects both the challenges and opportunities tied to crypto assets designed for AI integration. The token’s evolution underscores the difficulties of balancing decentralization with practical adoption while navigating the rapid technological advancements in both blockchain and AI industries. As the landscape continues to shift, AGI’s history remains a case study in the complexities of building decentralized AI ecosystems on blockchain infrastructure.

How AGI Works

How AGI Works: Architecture and Mechanisms

Decentralized AI Infrastructure

AGI operates on a decentralized blockchain network designed to facilitate artificial intelligence computations without reliance on centralized entities. The system enables AI models to interact, train, and evolve across independent nodes, ensuring a distributed approach to machine learning and inference. This decentralization aims to mitigate concerns related to data monopolization, censorship, and single points of failure. However, the complexity of coordination across an open network introduces challenges in latency, security, and computational efficiency.

Tokenized AI Resource Allocation

The AGI token functions as the primary medium for accessing AI-related services within the ecosystem. Users spend tokens to deploy machine learning models, request computations, or utilize trained AI agents. Node operators, in turn, receive AGI tokens as incentives for providing processing power, storage, or AI functionalities. This creates a marketplace where computing resources meet AI demand dynamically, but also exposes the system to token liquidity risks that can impact accessibility and pricing stability.

Smart Contracts and AI Service Execution

AI services within the AGI network are executed via smart contracts, ensuring that operations such as data processing, model training, and inference requests are verifiable and automated. These smart contracts govern the interactions between AI service providers and requesters, minimizing the need for intermediary control. However, smart contract limitations, such as gas fees or scalability constraints, can restrict complex AI operations, especially those requiring real-time processing or large-scale neural network computations.

Interoperability Between AI Agents

A key element of AGI’s design is the ability for independent AI agents to collaborate and communicate on-chain. Agents can contribute partial computations, share training data, or refine models iteratively. This allows the emergence of more sophisticated AI capabilities over time. However, the challenge remains in ensuring efficient coordination between AI agents while maintaining trust, consensus, and data integrity. The risk of adversarial attacks, such as malicious model poisoning, is an ongoing security concern.

Computational Efficiency and Network Constraints

Running AI models on a blockchain introduces performance trade-offs. While the trustless execution environment ensures transparency and verifiability, the computational overhead can limit the efficiency of more advanced AI workloads. Hybrid approaches, which offload significant processing to specialized hardware or sidechains, attempt to balance decentralization with performance. However, such solutions may compromise decentralization by introducing semi-trusted intermediaries.

Use Cases

Use Cases of AGI Token in Decentralized AI

AI Model Training and Payments

AGI tokens are primarily used as a medium of exchange within decentralized artificial intelligence (AI) ecosystems. Developers seeking to train AI models can use AGI to compensate computing nodes for processing power, storage, and algorithm execution. This enables a marketplace where individuals and organizations can monetize idle computing resources while AI developers gain decentralized access to computational power. However, scalability challenges arise when transaction fees or network congestion impact the efficiency of AI model training.

AI Service Marketplace

Decentralized AI marketplaces allow developers and enterprises to buy and sell AI services using AGI tokens. These services range from machine learning algorithms, data annotation, and predictive analytics to more advanced AI-driven automation tools. The decentralized nature provides an alternative to centralized AI service providers, reducing dependence on major corporations. A key drawback is the challenge of convincing enterprises to shift from well-established AI services to a decentralized marketplace with limited liquidity and adoption barriers.

Autonomous AI Agents Transactions

Some AI ecosystems enable autonomous agents to interact with one another, making payments in AGI to access information, process tasks, or license certain AI functionalities. In theory, this allows an automated AI economy to form, where programs make real-time decisions and transactions independently. However, adoption of such a system depends on the maturity of AI self-governance, security mechanisms, and interoperability with traditional financial systems.

AI Data Sharing and Decentralized Collaborative Learning

A significant use of AGI tokens is incentivizing data sharing for AI training. Contributors can be rewarded with AGI for providing high-quality datasets—essential for improving AI model accuracy. This decentralized data economy offers an alternative to the centralized data oligopoly controlled by tech giants. Still, issues such as data authenticity, privacy concerns, and regulatory uncertainty create hurdles for broader integration.

Governance and Protocol Decisions

Holders of AGI tokens often have governance rights, influencing protocol upgrades, funding allocations, and AI-related ethical decisions. While this democratizes AI decision-making, governance participation rates tend to be low in token-driven ecosystems, often centralizing control in the hands of a few large stakeholders rather than achieving true decentralization.

Conclusion

AGI's utility within AI marketplaces, autonomous transactions, data sharing, and governance positions it as a key player in decentralized artificial intelligence. However, adoption challenges, network limitations, and governance inefficiencies remain significant constraints to widespread use.

AGI Tokenomics

AGI Tokenomics: Supply, Utility, and Distribution

Token Supply and Emission Model

AGI’s token supply structure is a critical aspect of its tokenomics. The total supply is predetermined, with a fixed or inflationary issuance model depending on the network's governance decisions. Token emissions may follow a vesting schedule, impacting circulating supply over time. Understanding the release schedule is key for evaluating potential supply-side pressures.

Utility and On-Chain Mechanisms

The AGI token plays a role in network operations, facilitating transactions, accessing AI-related services, and incentivizing development. Depending on the ecosystem design, AGI may serve as a payment mechanism, a staking asset, or a governance token. If staking is involved, yield dynamics and lock-up periods influence liquidity and token velocity.

Network fees and transaction costs are additional considerations. Fee structures can determine adoption rates and address potential inflationary risks if the token supply increases with rewards. If demand doesn’t scale proportionally with supply, downward pressure on price can emerge.

Token Distribution and Centralization Risks

Allocation models determine the initial and ongoing distribution of AGI. If a significant percentage of tokens are concentrated among early investors or foundation-controlled wallets, centralization risks arise. High token concentration can lead to governance control by a small group, undermining decentralization.

Locked and vested allocations should be assessed to understand future dilution risks. If large unlock events occur, they can impact circulating supply and market dynamics. Transparent allocations to development, treasury, or ecosystem funds are necessary to prevent sell pressure from foundation-controlled reserves.

Inflation vs. Deflationary Mechanics

AGI’s token design dictates whether it follows inflationary or deflationary dynamics. If the network issues new tokens consistently, evaluating burn mechanisms or long-term demand drivers is essential. A model where token issuance outpaces adoption can lead to gradual devaluation.

Conversely, if deflationary mechanisms exist—such as token burns linked to usage—supply reduction can support price stability. However, aggressive burning strategies might also impact network incentivization, affecting validator, developer, or ecosystem growth sustainability.

Liquidity and Market Constraints

Liquidity depth determines how efficiently AGI trades across exchanges. If liquidity is fragmented or heavily reliant on a single venue, slippage concerns can emerge. Limited trading pairs or low market-making participation may result in higher volatility.

Bridging between ecosystems, if applicable, introduces smart contract risks and cross-chain liquidity fragmentation. The degree of liquidity mining incentives or market-making support also impacts overall token velocity and accessibility.

AGI Governance

AGI Governance: Decentralization, Decision-Making, and Challenges

Governance is a crucial aspect of AGI’s crypto ecosystem, influencing how upgrades, economic parameters, and protocol-level decisions are made. The governance structure determines the degree of decentralization, the efficiency of proposal execution, and the overall resilience of the network against malicious actors or centralization risks.

Governance Mechanisms and Token Holder Influence

AGI governance typically leverages a token-based voting system where holders can propose and vote on changes. This voting power is often proportional to token holdings, meaning entities with a significant stake have a stronger influence. While this aligns incentives for long-term token holders, it raises concerns about plutocratic control, where governance decisions could be swayed by a few large stakeholders rather than a broad-based community consensus.

Governance proposals can cover protocol upgrades, economic adjustments, or integrations with external platforms. In some cases, off-chain discussions precede formalization, using community forums or governance platforms before a proposal reaches the on-chain voting phase. However, participation in governance is often low, typically due to voter apathy, technical barriers, or the relatively complex nature of proposals. Low engagement can leave governance mechanisms susceptible to control by a minority of token holders.

On-Chain vs. Off-Chain Governance Dynamics

AGI governance may involve a mix of on-chain and off-chain mechanisms. On-chain governance ensures transparency and immutability but can be constrained by smart contract limitations, requiring careful design to prevent vulnerabilities. Off-chain discussions and signaling mechanisms, such as community-run governance forums or social consensus systems, often play a significant role in shaping governance before formal on-chain execution.

An additional challenge is the balance between efficiency and decentralization. A governance system that requires extensive participation for every change can slow decision-making, while a more centralized governance body may compromise decentralization. Finding an optimal balance remains an ongoing challenge for AGI and similar crypto assets.

Smart Contract Risks and Upgrade Governance

Smart contract-based governance is susceptible to vulnerabilities, such as governance attacks or unforeseen code exploits. If governance contracts are not designed with robust safeguards, attackers could manipulate voting mechanisms or execute unexpected code changes. Additionally, upgrade paths must be carefully managed to ensure security while preserving the flexibility to adopt necessary protocol improvements.

Governance frameworks also need to address upgradeability trade-offs. If the governance structure allows frequent and sweeping changes, it can introduce instability. Conversely, a rigid governance model could make adapting to evolving market conditions difficult. Managing these conflicting dynamics is key to sustaining AGI’s governance integrity over time.

Technical future of AGI

AGI Crypto Asset: Technical Developments and Roadmap

Ongoing Enhancements to the AGI Protocol

The AGI ecosystem continues to evolve with a focus on scalability, interoperability, and decentralized AI deployment. Recent upgrades to smart contract architecture aim to optimize gas efficiency, reducing transaction costs for AI model interactions. Layer-2 integrations are actively explored to mitigate Ethereum’s congestion issues, with sidechains and rollup-based solutions under evaluation.

AI marketplace infrastructure improvements are also a priority. Developers are working on refining AI model staking mechanisms, allowing for more seamless onboarding of machine learning models onto the blockchain. However, consensus mechanisms for AI evaluation remain a challenge, as current models may be vulnerable to Sybil attacks or centralized influence.

Transition Toward Multi-Chain Compatibility

Cross-chain functionality is integral to AGI’s roadmap. Efforts are underway to support interoperability with networks beyond Ethereum. Bridges to alternative blockchains, particularly those with lower fees and higher throughput, are being developed. The integration of Cosmos’ IBC and Polkadot’s XCM protocols has been discussed, but security risks associated with bridges remain a concern for implementation.

Token standards are also being revised to accommodate multi-network transactions while maintaining AGI’s core utility. The eventual goal is to enable decentralized AI inference across multiple blockchains without relying on centralized intermediaries.

Challenges in AI Compute Decentralization

A significant technical hurdle for AGI adoption is the decentralization of AI computation. Current implementations require substantial off-chain processing due to blockchain limitations in handling complex neural networks. Solutions such as zk-SNARKs and other cryptographic proofs are being evaluated for efficient AI verification on-chain. However, these approaches introduce high computational costs, slowing down adoption.

Decentralized AI training frameworks still lack sufficient contributors, making true distributed model learning difficult to achieve in a trustless environment. Without more efficient incentivization mechanisms, AI compute centralization risks remain persistent.

Future Roadmap and Research Directions

Upcoming protocol iterations focus on refining decentralized AI service discovery and autonomous agent coordination. AI agents on-chain require improved governance structures to prevent monopolization by well-funded entities. DAO-led initiatives are in discussion but remain early-stage with governance attack vectors yet to be addressed.

Additionally, edge AI integration remains a longer-term vision. The ability for decentralized AI models to run efficiently on IoT devices or low-power nodes presents multiple challenges related to latency and model compression. Advances in federated learning frameworks could play a role in mitigating these bottlenecks.

Despite ongoing developments, unresolved issues in scalability, AI consensus integrity, and computational decentralization remain critical factors in determining AGI's long-term viability.

Comparing AGI to it’s rivals

AGI vs. AGIX: Key Differences and Competitive Analysis

Underlying Technology and Architecture

AGIX operates as the native token of the SingularityNET ecosystem, which is designed to create a decentralized marketplace for AI services. It utilizes a multi-agent system where AI developers can publish, sell, and integrate various AI models. In contrast, AGI follows a different architectural path, focusing on an alternative mechanism for AI model deployment and interaction. One notable difference is how AGIX emphasizes interconnectivity between AI services, whereas AGI might prioritize a distinct methodology for AI execution and coordination.

Tokenomics and Utility

AGIX leverages a token model with staking mechanisms, allowing users to participate in governance and earn rewards. Staking also plays a role in securing the ecosystem and incentivizing long-term holding. While AGI has its own token utility, including potential governance functions, its staking and economic incentives may be structured differently. The effectiveness of these tokenomic models can influence developer adoption and user engagement.

Smart Contract and Blockchain Considerations

AGIX currently operates on multiple blockchain networks, offering Ethereum-based ERC-20 compatibility along with cross-chain functionality. The team has also explored migration and interoperability features across other chains to optimize gas fees and scalability. Comparatively, AGI has to address similar concerns regarding transaction costs, blockchain congestion, and cross-chain interaction. How each project balances security, ease of use, and blockchain choice can significantly impact adoption rates.

AI Model Accessibility and Integration

One of the significant differences between AGIX and AGI is how each project handles AI model accessibility. AGIX provides an open AI marketplace, allowing developers to monetize AI services efficiently. The modular and composable nature of services within SingularityNET enables easy integration of different AI models. AGI, depending on its framework, could present a different approach to AI accessibility—whether in terms of centralization risks, ease of deployment, or how AI models can be built and executed.

Development and Community Support

AGIX benefits from an established community and continued development driven by its ecosystem expansion. There are collaborations and partnerships aimed at growing AI adoption and ensuring long-term sustainability. AGI’s community engagement and development roadmap are crucial factors that determine its ability to compete. If AGI lacks strong developer involvement or active governance participation, it may struggle to gain traction against AGIX’s broader network effects.

Challenges and Drawbacks

Scalability, gas fees, network security, and AI interoperability remain ongoing challenges for both AGIX and AGI. Governance centralization risks, token utility consistency, and AI model efficiency also play significant roles in shaping long-term viability. Whether AGIX or AGI can better handle these technical and economic issues will determine their comparative success in the decentralized AI landscape.

AGI vs FET: A Detailed Comparison

Architectural Differences and Smart Agent Capabilities

AGI and FET both aim to advance artificial intelligence within blockchain ecosystems, but their architectural choices differ significantly. AGI focuses on decentralized AI services, allowing developers to create and monetize AI models using smart contracts. In contrast, FET employs an autonomous economic framework where "Autonomous Economic Agents" (AEAs) operate independently to perform complex tasks, reducing human oversight. While AGI relies on traditional smart contracts, FET's infrastructure enables direct peer-to-peer agent interactions, leading to potentially lower operational costs but also introducing complexity in deployment.

Scalability and Network Efficiency

Scalability is a key concern for both projects, but they address it with different strategies. AGI primarily builds on Ethereum-based smart contracts, which can lead to congestion and high gas fees during peak activity. FET, however, utilizes its own scalable ledger, designed to handle high-throughput transactions specifically optimized for AI workloads. This provides an efficiency advantage, particularly in an environment where real-time AI execution is crucial. However, a dedicated ledger means fewer external integrations compared to AGI's Ethereum-native approach, which benefits from widespread ERC-20 compatibility.

Decentralization vs. Operational Control

AGI places a strong emphasis on full decentralization, ensuring that AI services remain open, permissionless, and resistant to control by centralized entities. FET, however, allows for a certain degree of operational control through its agent economy, where entities can predefine AI behaviors. This introduces questions of trust, as users must rely on the integrity of these autonomous agents. While AGI adheres to strict decentralization principles, FET’s model could be more vulnerable to manipulation unless all participating agents maintain equilibrium.

Use Cases and Market Adoption

While both projects serve AI-driven automation, their application focuses diverge. AGI is primarily geared toward providing AI models for decentralized applications (dApps), allowing developers to integrate machine learning into various Web3 services. FET leans more towards automating business operations, industrial IoT, and supply chain logistics through its agent-based system. This distinction leads to differences in adoption—AGI finds more traction in AI-enhanced DeFi, while FET aligns closely with enterprise automation. The enterprise focus of FET may offer institutional adoption potential but also demands a centralized coordination layer, which contradicts the ideals of fully open AI systems.

Liquidity and Exchange Accessibility

Both tokens are widely listed, but AGI benefits from its Ethereum-native presence, ensuring broader DeFi composability. FET, while liquid, requires bridges or additional network transitions to interoperate with major DeFi platforms, creating friction for users seeking efficient asset utilization. The need for cross-chain interactions in FET’s ecosystem can introduce security risks unless well-audited bridge protocols are in place.

AGI vs OCEAN: A Comparative Analysis

Core Functionality and Use Case Differences

AGI and OCEAN are both focused on AI and data economy, but their approaches differ significantly. AGI is primarily centered on artificial general intelligence and fostering decentralized AI networks. In contrast, OCEAN is focused on the tokenization and decentralized exchange of data assets, enabling AI projects and data-driven applications to access and monetize datasets. The distinction lies in AGI prioritizing AI development while OCEAN optimizes AI’s access to structured data resources.

Data Accessibility and AI Integration

One of OCEAN’s primary strengths is its extensive focus on data marketplaces, which allows AI models to train on diverse datasets in a decentralized manner. This creates a critical synergy between data providers and AI developers. AGI, while emphasizing decentralized AI, does not operate a similar large-scale incentivized data-sharing network. Instead, AGI’s model leans toward the coordination of AI agents, whereas OCEAN’s framework allows structured data consumption for existing AI applications.

However, challenges exist with OCEAN’s reliance on data monetization. The effectiveness of its token incentives depends heavily on external data providers contributing quality datasets. Issues like data privacy, regulatory compliance, and potential Sybil attacks on data quality also impact the ecosystem’s robustness. In contrast, AGI sidesteps this by focusing more directly on AI algorithm advancements rather than the marketplace aspect.

Decentralization Approach

AGI and OCEAN both advocate decentralization, but with key differences. AGI’s model is focused on distributing AI processing workloads across independent nodes, promoting autonomous intelligence at scale. OCEAN, on the other hand, applies decentralization to data exchange, where buyers and sellers interact through smart contracts without needing centralized control. While both aim to remove intermediaries, OCEAN inherently requires varying levels of trust in data providers, whereas AGI places trust in its distributed AI agent network.

Challenges in Token Utility and Adoption

AGI and OCEAN both use native tokens to facilitate their ecosystems, but the utility models vary. OCEAN’s token is integral to data transactions, staking mechanisms, and incentivizing data providers, whereas AGI’s token is designed more for governance, AI service interactions, and incentivizing participation in AI development.

A challenge for OCEAN has been ensuring consistent demand for its token beyond speculative trading. Since data availability is influenced by incentives, a decline in high-quality datasets could weaken the ecosystem’s foundation. With AGI, token utility is more directly tied to AI agent collaboration, but it also faces adoption hurdles in ensuring demand for decentralized AI services.

Primary criticisms of AGI

Primary Criticism of AGI

Centralization Concerns in Development and Governance

One of the most persistent criticisms of AGI is the level of centralization in its development and governance. While the project markets itself as decentralized, decision-making structures have often remained within a small core team. This raises concerns about whether AGI operates as a genuinely open system, or if control remains concentrated among a few entities. Critics argue that any blockchain project focused on artificial intelligence should prioritize minimizing custodial control to avoid long-term risks tied to centralization.

Network Efficiency and Scalability Challenges

AGI's network efficiency is another point of contention. Given the computational demands of AI processing, scalability and transaction throughput are critical. Some have questioned whether the blockchain infrastructure supporting AGI can handle the necessary workload without creating bottlenecks. The challenge remains in balancing decentralization with computational efficiency, as on-chain AI operations require significantly more resources than standard transactions.

Uncertain Token Utility and Economic Model

The AGI token's utility within its ecosystem has also faced scrutiny. While the token serves as a means of exchange for AI services, some critics argue that its real-world application is still underdeveloped. Without widespread adoption or clear incentives for token holders beyond speculation, there is concern that the tokenomics may not provide sustainable long-term value. Additionally, inflation mechanics and reward structures have been questioned, with some arguing that they may not sufficiently incentivize providers while also maintaining a stable network economy.

Competition from Other AI and Blockchain Projects

AGI operates in a rapidly evolving sector where multiple blockchain projects are working on decentralized AI solutions. Some argue that AGI lacks a clear competitive edge against emerging alternatives with more efficient models, better funding, or strategic partnerships. Since AI development is highly resource-intensive, competing projects with stronger funding and institutional support may outpace AGI in technological advancement.

Security and Data Privacy Risks

Given the fusion of AI and blockchain, AGI must confront critical security and data privacy concerns. Blockchain-based AI networks introduce unique attack vectors, and any compromise in smart contracts, models, or data integrity could undermine trust in the system. Additionally, the extent to which AGI can guarantee compliance with evolving data privacy regulations remains uncertain, raising potential legal and ethical risks for users engaging with AI-driven applications.

Founders

AGI Founding Team: Key Figures Behind the Crypto Asset

AGI was founded by a team with deep expertise in artificial intelligence, blockchain, and decentralized systems. The leadership and development team played a crucial role in shaping the project’s vision, focusing on decentralizing AI and integrating it with blockchain technology.

Core Founders and Their Backgrounds

The AGI project was spearheaded by individuals with strong academic and professional backgrounds in AI and distributed computing. One of the key figures had extensive experience in machine learning, neural networks, and cognitive computing. This expertise influenced AGI’s technical direction, aiming to create a decentralized network where AI agents could interact, learn, and evolve.

Other founding members came from blockchain development backgrounds, bringing experience in smart contracts, distributed ledger architectures, and tokenomics. Some had previously been involved in high-profile blockchain projects, contributing to AGI’s early credibility. Their experience with Ethereum, smart contract platforms, and decentralized applications (dApps) shaped the initial infrastructure of the AGI ecosystem.

Leadership Dynamics and Challenges

While the founding team had strong technical and theoretical foundations, challenges emerged in execution. The decentralized AI concept, while ambitious, faced hurdles in implementation. Some industry critics pointed to the complexity of scaling AI in a decentralized manner, questioning whether the leadership had the necessary expertise in both AI development and blockchain governance to balance both technical demands effectively.

Internal restructuring and strategic shifts also affected the project. Team members moved on, and new leadership was introduced at various stages, impacting continuity. While some departures were communicated transparently, others were more abrupt, leading to speculation about internal disagreements or shifts in project direction.

Industry Credibility and Community Perception

The founding team’s credibility initially helped AGI gain traction within both the AI and blockchain communities. However, as the project progressed, opinions became more polarized. Some saw the leadership as visionary pioneers working towards complex decentralization challenges, while skeptics criticized the practicality of their approach and the pace of development.

Community engagement from the leadership varied over time, sometimes being highly active in discussions and at other times appearing distant. This fluctuating involvement influenced sentiment around AGI’s long-term viability and execution.

The interplay between a technically strong founding team, internal shifts, and execution challenges continues to shape how the AGI project evolves within the crypto and AI sectors.

Authors comments

This document was made by www.BestDapps.com

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