Front-running focuses on leveraging classified information to gain a competitive financial advantage by compromising the interests of other market participants. In the case of blockchain-based systems, front-running is an unethical practice in which a specific user or a group can exploit their prior knowledge of pending transactions to achieve an unfair advantage over other users in the network. Let us learn more about front-running and how it works to find effective remedial measures.

What is Front Running Attack?

The foremost highlight in a guide to front-running attacks would focus on their definition. In the domain of traditional finance, front-running has been a formidable challenge for decades. It creates difficulties in maintaining integrity of stock markets, and Wall Street has termed front-running as an illegal activity. The alarming fact about front-running is that the concept has found its way into the world of blockchain and smart contracts. However, the underlying logic of front-running attacks is similar to the one used on traditional stock markets.

The front-running attacks on blockchain and smart contracts aim at exploiting genuine transactions to the advantage of hackers. On top of it, the impact of front-running in the domain of cryptocurrencies is significantly massive. As a matter of fact, MEV bots have used front-running attacks to amass almost $1 billion by exploiting retail investors. Apart from the concerns for safety of funds, front-running also creates concerns regarding fairness and transparency of the blockchain landscape. Interestingly, front-running attacks can also serve a good purpose for recovering assets stolen in hacking attacks.

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How Does Front-Running Work in Crypto?

The outline of emerging blockchain attacks is incomplete without referring to front-running attacks. In the traditional stock markets, brokers can implement front-running attacks by using their knowledge of a client placing a major order. The broker would use the information for making a trade on their own to draw benefits from the expected price movement due to the major order of the client.

In the case of blockchain and crypto markets, front-running attacks could assume a more sophisticated form. Validators can use their knowledge of the mempool or the transaction queue for reordering, including or omitting transactions that can help them with financial benefits. 

Let us assume an example of front-running detection to understand how it works in the case of blockchain systems. A miner could notice a large buy order for a specific cryptocurrency token. Based on this observation, the miner could place their own buy order before the big buy order and then validate the larger buy order. As a result, the miner could generate profits on the basis of arbitrage.

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What is the Role of MEV Bots in Front-Running Attacks?

The review of front-running attacks would also bring you across the implications of MEV or Maximum Extractable Value bots. Validators have the authority to approve transactions on blockchain, which offers them an unfair advantage for deploying front-running attacks. Apart from validators, the massive network of MEV traders also creates risks of blockchain front-running attacks by using bots. The front-running bots run on the power of smart contracts tailored for scanning and capitalizing on pending transactions. The bots can change the order of pending transactions in the mempool and create the ideal foundation for successful front-running attacks.

It is important to reflect on the threat of MEV bots, as almost 50 teams actively pursue MEV trading. The best teams have used front-running vulnerability to generate millions in profits during favorable market conditions. In the case of public and completely decentralized blockchains, you can find universal accessibility of transaction data. 

On top of it, decentralized exchanges do not have any SEC cybersecurity rules, thereby offering the ideal ground for growth of front-running activity. Therefore, the blockchain landscape is filled with traders who want to capitalize on the vulnerabilities in the blockchain landscape to find victims.

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Working Mechanism of Front-Running Attacks

The responses to “What are front-running attacks in blockchain?” also require a clear explanation of their working mechanisms. You can reduce front-running attacks to some basic templates for understanding how they work. The three distinct scenarios in which front-running attacks can work on blockchain systems include displacement, insertion, and suppression. Here is an overview of how each type of front-running attack works in practice.

• Displacement

In the case of displacement attacks, the malicious actor would use a higher gas price to ensure that they can place their transaction ahead of other impending transactions. With the help of outbidding, hackers could achieve the advantage of priority processing for their transactions, thereby leading to front-running. 

• Suppression

Another common approach for front-running attacks points to suppression. Attackers can leverage the volume of transactions to their benefit in such cases. In the case of suppression attacks, the hacker would create a barrage of transactions accompanied by considerably higher gas prices. The collection of such transactions is termed a ‘suppression cluster.’ Due to the exceptionally high number of high-priority transactions, a victim’s transaction would struggle to find a place in the same block.

• Insertion 

The next important type of attack for front-running in blockchain systems is the insertion attack. It is one of the most complicated front-running attacks on blockchain systems with a sandwich approach. Insertion attacks involve the implementation of front-running for a transaction, followed by back-running the same transaction.

Attackers can deploy insertion by placing the victim transaction in a sandwich of transactions. The first transaction would have a higher gas price, while the second transaction would have a lower gas price. Such types of attacks are commonly visible in the case of decentralized exchanges. As a result, hackers could capitalize on whale transactions or large-scale trades to extract massive profits through front-running. 

You can develop a better understanding of such blockchain attacks by using an example. Insertion or sandwich attacks in front-running focus on big transactions. Let us assume that a buyer wants to purchase 1000 ETH at the existing market rate, say $1630. An MEV bot would spot this large buy order in the mempool or the public transaction pool.

The bot would capitalize on this information and buy 1000 ETH at $1630 per unit. Due to the massive buy transaction, the market effects can lead to execution of the victim’s purchase order at a higher price of $1635 per unit of ETH. The bot would then immediately sell off the 1000 ETH it had purchased. At a profit of $5 for each unit of ETH, the attacker could pocket a profit of $5000.

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Which Factors are Responsible for Triggering Front-Running Attacks?

Blockchain experts and users have been searching for effective approaches to resolving the concerns of front-running attacks. You can find an effective solution to address the risks of front-running attacks by learning front-running detection best practices. The ideal approach for detecting possibilities of front-running attacks focuses on identifying important triggers for front-running. Here is an outline of the notable triggers which can lead to front-running attacks.

• Large Transactions

The large trades or whale transactions could lead to significant movement in the market. Front-running hackers look out for such massive movements in the market for deriving sizeable profits.

• Pending Transactions

The most common site of front-running attacks on blockchain is the mempool. It is a type of waiting queue for pending transactions and serves as a repository of information for front-running attacks. Hackers could use information about larger and potentially profitable transactions for front-running them prior to their confirmation.

• Liquidity Pool Fluctuations

Upon adding or removing liquidity in a DeFi pool, you might encounter considerable price fluctuations. As a result, it could become an alluring target for front-running attacks.

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• Arbitrage Opportunities 

Attackers could also front-run arbitrage trades when they find price discrepancies between different tokens or exchanges.

• New Token Listings

New token listings on decentralized exchanges demand a considerable level of attention, thereby expanding the size of their buy orders. The rush to buy a new token creates the perfect opportunity for front-running. 

• Flash Loans 

The possibilities of blockchain front-running attacks are also visible in the case of flash loans. Flash loans don’t involve any collateral, and the loan is repaid within a single transaction. However, flash loans could also open the doors for potential price manipulation, which leads to front-running attacks.

• Updates for Oracles and APIs

Oracles and APIs serve as trusted sources of external data for smart contracts. The changes or updates in oracles and APIs could help in influencing contract outcomes, thereby making them easier front-running targets.

• Governance Proposals

The guides on “What are front-running attacks in blockchain?” also point to the role of governance proposals in encouraging front-running attacks. New governance decisions could lead to changes in token values, particularly associated with partnerships, protocol changes, or modifications in fee structures. All these factors create the ideal grounds for front-running attacks.

• Order Book Review

Certain decentralized exchanges follow the order book format, which makes them an easy target for front-running attacks. Hackers could simply review the order book on the DEXs to identify pending transactions that are suitable for front-running.

Methods for Preventing Front-Running Attacks

The most important priority in any discussion on front-running attacks would focus on methods for preventing such attacks. It is important to look at the preventive measures by platforms hosting the transactions as well as the users conducting the transactions. Let us take a look at the methods used for preventing front-running attacks from the viewpoint of platforms and users.

How Can Platforms Fight Against Front-Running Attacks?

The outline of best practices for front-running detection and resolution of issues would emphasize the role of platforms hosting the transactions. Blockchain networks, liquidity pools in DeFi, and decentralized exchanges must pay to certain aspects to ensure safeguards against front-running attacks. First of all, DeFi platforms could implement restrictions on slippage rates to offer the best swap outcomes. At the same time, it prevents front-runners who can exploit higher slippage rates to extract considerably higher profits.

Platforms hosting transactions could also fight against front-running blockchain attacks through the implementation of commit-reveal schemes. The commit-reveal scheme involves committing to a specific action without disclosing the details, followed by revealing the exact information. On top of it, batch transactions could help in bundling different transactions together and processing them as a single unit. As a result, hackers would have a tough time isolating transactions for front-running.

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How Can You Fight Against Front-Running Attacks?

Users could also find effective ways to avoid front-running attacks on blockchain by utilizing privacy platforms or tools for offering confidential transactions. You should also learn about the latest security best practices, platform updates, and security patches of a platform for better security against front-running attacks. Users should also ensure that they do not disclose information about large transactions as it could help in maintaining their trading intentions as discreet information.

It is also important to rely on platforms that have front-running safeguards that can prioritize user security. You can also focus on avoiding peak transaction times to reduce the chances of becoming front-running victims. The advantage of layer 2 scaling solutions, such as rollups or state channels, could help in reducing the front-running risks by off-chain transaction processing.

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Conclusion 

In the domain of cryptocurrencies, front-running attacks are an unethical practice in which validators and bots could manipulate transactions to achieve financial gain. Some of the common types of front-running attacks include insertion, displacement, and suppression. The tactics in front-running could evolve significantly, and you must learn about the strategies for addressing them by learning more about them. Therefore, platforms and users would have to follow remedial measures to address the concerns due to front-running. Learn more about the threat of front-running to security in blockchain-based systems and how to fight them with proven best practices now.

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

The post Front-Running Attacks in Blockchain: A Complete Guide appeared first on 101 Blockchains.

Assume that an IT service provider employs 30 people in its customer service team, which handles inbound and outbound calls on behalf of the organization. What if the organization uses an AI-powered chatbot for managing communications with customers? Should the organization remove the 30 employees from the job? The principles of AI ethics help in answering such questions. 

The importance of AI ethics principles is clearly visible in the ways in which they guide AI initiatives across different sectors. However, governments, tech vendors, and business communities have been coming up with different versions of AI principles. Let us learn more about the top ethical principles for implementing AI solutions.

What are AI Ethics?

The definition of AI ethics can serve as the first pointer for understanding the principles of ethical AI. Ethics represent a general set of principles for differentiating between right and wrong. If you want to find the answers to ‘What are AI ethics principles,’ then you must know about the significance of ethics in AI. 

Ethical principles for AI systems provide the guidelines for responsible development, training, testing, and deploying the system. You can understand the importance of ethical AI by identifying the lack of foresight regarding the implementation of artificial intelligence systems. In such cases, AI systems could lead to disastrous economic and societal consequences. 

Businesses should look for the risks and advantages associated with AI adoption. However, creating an extended list of AI principles and implementing them in an organization are completely different things. On the contrary, you must understand the value of ethical principles in AI and the best practices for integrating them into your AI adoption strategy.

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Why Do You Need Ethics in AI?

Ethics are the most crucial requirement for defining the responsible development and utilization of artificial intelligence systems. Artificial intelligence has become one of the integral elements of products and services offered by organizations. Therefore, organizations should adopt the best AI ethics principles for creating trust around the use of AI. 

Most important of all, you must know about the significance of an AI code of ethics or an AI value platform. It showcases a policy statement that offers a formal definition of the role of artificial intelligence in developing the human race. The code of ethics for AI aims to offer guidance to stakeholders for taking ethical decisions about using artificial intelligence.       

The discussions about the ethics of AI emerge from the notions of dangers involved with the applications of AI. Isaac Asimov, the creator of robotics, pointed out that autonomous AI agents could present multiple challenges and risks. Interestingly, the most popular AI ethics principles could address such problems. Asimov outlined the Three Laws of Robotics, which serve as the code of ethics for AI. Apparently, Asimov had come up with his code of ethics way before the development of autonomous AI agents. 

• According to the first law of Asimov, robots or autonomous AI agents must not inflict any type of harm on humans or allow any harm due to their actions. 
• The second law for AI points out that robots should obey humans, except in circumstances that do not align with the first law. 
• Finally, the third law provides instructions to robots to protect themselves while following the first two laws. 

Artificial intelligence has been created for replication, improvement, or replacement of human intelligence. The necessity of AI ethics principles is visible in the requirements for utilizing large repositories of different types of data for developing insights. For example, AI projects that use biased or inadequate data could showcase potentially harmful and unintended consequences. In addition, the innovation in AI systems has created challenges in understanding the ways in which AI draws conclusions. Therefore, it is important to have an AI ethics framework guided by proven principles.

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Ethical Challenges in AI

The ideal approach for outlining ethical AI principles would focus on identifying their purpose. What are the problems you want to solve with AI principles? The principles of ethical AI aim at resolving problems, such as explainability, misuse, fairness, and responsibility. 

Fairness refers to the challenges emerging from the availability of personally identifiable information or PII. It implies that AI systems should avoid all types of biases in ethnicity, race, or gender. 

Explainability is also another important challenge for ethical principles in AI, as AI systems work through a complex assortment of data processes and algorithmic systems. Organizations that use AI systems can experience difficulties in explaining the source data and the output data alongside the working mechanisms of the algorithms and the rationale for their actions. AI should have higher traceability to ensure easier identification of the underlying causes of any discrepancies. 

Misuse of AI algorithms is also another prominent ethical challenge associated with AI. The search for answers to “What are AI ethics principles?” would also guide towards the importance of misuse as an ethical issue for AI. Artificial intelligence algorithms could serve diverse purposes, and malicious users could implement AI algorithms for unapproved use cases. Therefore, it is important to evaluate these scenarios at the design stage to minimize risks and introduce safety measures for reducing the adverse effects. 

Another noticeable risk associated with ethical AI is responsibility. It is important to define clear responsibilities for the consequences of AI-based decisions. You can find a relation between the problem of responsibility and the solution, i.e., accountability. It is important to find the right balance in some cases where AI systems can serve more helpful results than human activity, albeit with some problems. For example, you can identify the merits of autonomous driving systems, which lead to multiple fatalities, albeit ensuring better safety than human driving.

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How is an AI Code of Ethics Important for Ethical Principles in AI?

The code of ethics is like a framework to ensure ethical AI implementations while resolving problems in three different areas. AI code of ethics provides a clear glimpse of the AI ethics principles that would support responsible use of AI. The three notable areas prioritized in an AI code of ethics include policy, technology, and education. Let us find out more about the three elements of an AI code of ethics to understand how they fit with AI principles. 

• Policy 

Policy development helps in creating the ideal framework to ensure standardization and effective ways for defining regulations for responsible AI. For example, the Asilomar AI principles serve as a major force for beginning a conversation. In addition, other definitions of ethical AI policies have been emerging as prominent benchmarks for defining new ethical principles for artificial intelligence. 

The objective of top AI ethics principles also focuses on addressing legal issues. Organizations should integrate AI policies into their code of ethics. However, the effectiveness of AI principles depends on the compliance of users and employers to the specified requirements. 

• Awareness 

The next important element in defining AI ethics is awareness or education. Data scientists, consumers, executives, and front-line employees must understand policies, potential negative impact of unethical AI, and important considerations. The biggest concern related to the tradeoff between flexibility for data sharing and AI automation alongside the negative repercussions of adverse automation or oversharing. 

• Technology 

How can organizations follow the most popular AI ethics principles to perfection? Executives have to create AI systems that can detect unethical behavior and fake data. For example, organizations should use AI systems for detecting deepfakes or sophisticated cyber-attacks. It is important to ensure that organizations have open, trusted, and transparent AI infrastructure. 

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What are the Most Popular AI Ethics Principles?

The advantages of principles for ethical AI could help businesses in many ways. You should know that the core values of ethical principles focus on betterment of societies, environment, humanity, and individuals. The best AI ethics principles follow these core values for offering the benefits of responsible and credible AI. Interestingly, UNESCO has reported recommendations on the ethics of AI for defining the fundamental principles of ethical AI. Here is an outline of the ethical principles for responsible use of AI.

• No Harm and Proportionality 

AI systems should be used only for the objectives they have been created to address. It means that AI systems should not do anything out of the blue other than the essential practices for achieving a legitimate objective. Risk assessment is an important requirement for preventing any issues that might arise with such applications. 

• Security 

The next important addition to answers to “What are AI ethics principles?” is security. All AI stakeholders should avoid and resolve concerns due to unwanted damage, such as safety risks. In addition, it is also important to revolve the vulnerabilities to attacks or security risks. 

• Right for Data Protection and Privacy

Privacy is also one of the key elements in ethical principles for artificial intelligence. All AI actors should safeguard and promote privacy throughout the AI lifecycle. In addition, it is also important to establish comprehensive data protection frameworks.

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• Responsibility and Accountability 

AI systems should provide flexibility for auditing and traceability. Some of the important mechanisms required to safeguard ethical AI principles of responsibility and accountability include due diligence mechanisms, impact assessment, and audits. All of these factors help in avoiding conflicts with human rights regulations and risks to environmental safety. 

• Fairness

Fairness of non-discrimination is one of the general principles recommended by UNESCO for safeguarding AI ethics. All the agents involved in AI systems should promote fairness, non-discrimination, and social justice. The principle of fairness aims at following an inclusive approach to ensure that the benefits of AI are accessible to every individual.

• Adaptive Governance and Multi-Stakeholder Collaboration

Data is the lifeblood of AI systems. Therefore, it is important to ensure that AI systems comply with international law for the use of data. At the same time, it is also important to respect national sovereignty. The list of ethical principles in AI also emphasizes the need for participation of diverse stakeholders. The involvement of multiple stakeholders is also a crucial principle for ensuring inclusive approaches in governance. 

• Human Oversight 

One of the most crucial factors in safeguarding ethical principles for AI points to the concerns due to replacement of human workers with AI. UNESCO implies that all member states should ensure that AI systems never replace human accountability and responsibility.

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• AI Awareness

The collection of ethical AI principles also includes awareness and literacy about artificial intelligence. Public awareness regarding data and AI should be the first priority for encouraging adoption of AI systems. The methods for encouraging AI awareness include digital skills and AI ethics training, civic engagement, media and information literacy, and open and accessible education. 

• Sustainability 

AI systems should go through an assessment for evaluation of their impact on sustainability. Artificial intelligence systems should be developed in accordance with the Sustainable Development Goals of the United Nations. 

• Transparency 

The principles for ethical AI also include transparency, as it is a core element for ethical deployment of artificial intelligence. At the same time, it is also important to ensure that the level of transparency aligns with the context. In addition, it is also important to ensure that transparency does not meddle with other principles like security, safety, and privacy.

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Conclusion 

The principles for ethical AI provide a definitive framework to ensure responsible design, development, and utilization of artificial intelligence systems. You can notice how the most popular AI ethics principles prepare the safe and responsible use of AI with transparency. Artificial intelligence has the capability to emulate human intelligence, thereby establishing possibilities for ethical concerns. 

On the other hand, you could not find a single framework for principles for ethical use of AI. For example, UNESCO has charted the essential principles for ethical use of AI. At the same time, you can also refer to Asilomar AI principles for guidance on establishing responsible AI systems. Learn more about AI fundamentals and how ethics plays a major role in AI.

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Therefore, discussions about top web3 trends have gained the attention of everyone in the tech landscape. Web3 aims to democratize the Internet, where power would reside in the hands of users. It would utilize a combination of different technologies such as blockchain, augmented reality, virtual reality, smart contracts, and AI for creating a semantic, ubiquitous, and decentralized internet.

Blockchain and AI technology serve as the key drivers of web3 transformation. For example, blockchain provides the foundations for creating a decentralized web. On the other hand, AI could offer support for creating autonomous systems that could work on their own without intervention.

However, questions like “How AI is transforming Web3?” have paved new avenues for discussions about the impact of web3 trends on AI. Web3 and AI have the potential to transform the ways in which people interact with the Internet and each other. Let us find out the top trends in web3 that would have a major impact on the future of AI.

Understanding the Power of Web3

The discussions about the role of web3 in the future of AI should start with an in-depth understanding of the power of web3. How could it change the future of artificial intelligence, a technology which serves as a core component of web3? Web 3.0 or web3 is the next generation of the Internet, which focuses on decentralization of power and control from big tech corporations and governments. It has been developed on the foundations of web2, which dictates the existing state of the Internet featuring social media, centralized platforms, and online services.

The best web3 trends visualize the development of a decentralized web that features blockchain technology, peer-to-peer networks, and cryptography. Web3 also addresses some of the prominent setbacks associated with web2, such as censorship, uninformed access, and use of personal information and data privacy.

With the help of decentralized technologies like blockchain, web3 could offer a user-centric, secure, and more transparent internet. You can clearly notice that web3 is a game-changer in the field of digital transformation. What are the strengths of web3? You can find an effective answer in the different components of web3.

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Important Components of Web3

The next important aspect required for understanding the interplay between web3 and AI is the outline of components in web3. First of all, you must notice that Web3 Trends 2023 revolves primarily around blockchain technology, cryptocurrencies, and smart contracts. Blockchain ensures decentralization in web3 through removal of centralized intermediaries or authorities for validating or controlling data and transactions. On top of it, blockchain also ensures better security, immutability, and transparency in web3 applications.

Cryptocurrencies could facilitate peer-to-peer transactions without the need for banks. The role of cryptocurrencies in web3 draws the limelight towards possibilities for development of new economic models like DeFi. In addition, cryptocurrencies can strengthen the web3 landscape by providing independent instruments for financial exchange. 

The review of the most popular web3 trends also focuses on smart contracts, which can facilitate the automatic execution of agreements and transactions upon compliance with specific conditions. Smart contracts are essential for web3 as they help in developing dApps, which would populate the web3 ecosystem. Decentralized apps could include decentralized social media platforms, prediction markets, governance systems, marketplaces, and decentralized versions of traditional applications.

Some of the other important components of web3 include decentralized identity, decentralized governance, and decentralized storage systems. Decentralized storage systems are one of the formidable future trends in web3 with the continuously growing demand for solutions like IPFS. Decentralized identity systems could offer complete control and ownership to users over their personal data.

Furthermore, decentralized governance systems could help community members participate in decision-making, thereby ensuring an active role for users in the growth of web3 platforms. All of these components of web3 have a significant influence on defining the development of AI in the future.

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The Intersection between Web3 and AI

Before you ask questions like ‘What is the future trend of AI?’ you must understand how web3 and AI complement each other. Web3 and AI have the capability to enhance each other’s capabilities. Web3 could help in creating a user-centric and decentralized internet, and AI could help in creating intelligent computer systems that could learn from data and simulate human intelligence. Most important of all, web3 and AI could unlock many new possibilities alongside giving new dimensions for development to technology, society, and business. Here is an outline of the different ways in which web3 and AI could work together.

• Decentralized AI

The foremost entry among the potential use cases of the combination of web3 and AI would point to decentralized AI. The decentralized infrastructure of web3 could offer the ideal environment for running AI algorithms. How will the top web3 trends define improvements in functioning of AI algorithms? AI algorithms would not have to rely on centralized cloud platforms or servers with web3.

On the contrary, web3 could ensure distribution of AI models and data across different nodes in a network. As a result, it could ensure better scalability, privacy, and security. Decentralized AI could open new prospects for better collaboration and safeguards for privacy in machine learning. The combination of web3 and AI could enable multiple participants to contribute their data for machine learning and develop new models without disclosing sensitive information.

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• Personalized AI Services

The outline of Web3 Trends 2023 also emphasizes the necessity of personalization in web3. Web3 focuses on data ownership and user control, which could create the foundations for personalized AI services. At the same time, personalized AI services must be tailored to safeguard user privacy and preferences.

Web3 could help in developing AI models that utilize encrypted data or run directly on user devices. The decentralized approach to data collection ensures that users don’t have to compromise with control over personal information to obtain personalized services from AI solutions.

• Data Marketplaces

Data ownership and user control are the major themes in web3, which also serve as ideal examples for explaining the feasibility of combining web3 with AI. The decentralized nature of web3 could help in creating data marketplaces, which would define the future of AI with an innovative advancement. Data marketplaces could help in monetizing data through incentives for sharing data. AI algorithms could leverage the data marketplaces to access high-quality and diverse datasets for improving their performance.

• Decentralized Governance with AI

Another promising example of blending web3 with artificial intelligence is evident in decentralized governance systems. Decentralized governance mechanisms like DAO could utilize AI technologies to improve the decision-making process. AI algorithms could support data analysis and provide access to insights alongside ensuring consensus among the community members. 

The discussions about “What is the future trend of AI?” might draw references to possibilities of using AI in DAOs. Web3 expands the scope of applications of AI. For example, AI-powered DAOs could capitalize on benefits of automating specific tasks and improve efficiency. On top of it, AI could also facilitate the foundations for enabling informed decision-making without compromising on inclusion.

What are the Web3 Trends that would Shape the Future of AI?

The potential of AI for powering up digital transformation through web3 is a core precedent for web3. However, AI is not just another technology that would help you build web3. On the contrary, some of the most popular web3 trends would define radical changes in AI development. Here are the top trends in web3 that would shape the future of artificial intelligence.

1. Decentralized AI

The intersection of web3 and AI leads to the development of decentralized AI. It is a new perspective on AI utility by leveraging blockchain and other decentralized technologies. Decentralized AI can provide answers to “How AI is transforming web3?” alongside showcasing the impact of web3 trends on AI. It would help in creating AI systems that are under the ownership and control of the community. Decentralized AI could resolve many concerns with centralized AI, including bias, security, and privacy.

2. AI-Powered NFTs

Non-fungible tokens, or NFTs, are a specific variant of digital assets that have gained massive popularity in the web3 ecosystem. NFTs are unique digital assets stored on blockchain networks, which could represent every asset, including real estate and digital artworks. Artificial intelligence could serve as a powerful tool for creating new opportunities to use NFTs. For example, AI-powered NFTs could support use cases for verification of product authenticity and creation of personalized experiences.

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3. Federated Learning 

The next addition among the future trends in web3 that would influence AI refers to federated learning. It is a new approach to machine learning in which AI models would be trained using data from user devices. Federated learning could help in addressing the concerns regarding privacy with centralized AI.

4. Social Media with AI

Artificial intelligence has a major role in transforming social media networks. AI could help in creating more personalized experiences by capitalizing on the top web3 trends such as data ownership and control. AI could emerge as a critical tool for decentralized social media networks with functionalities for moderating conversations and removing harmful content.

5. DeFi Advancements with AI

The applications of artificial intelligence would also discover new directions with the rise of web3 trends 2023 in the domain of DeFi. DeFi, or decentralized finance, offers a new model for financial systems developed on the foundations of decentralized technologies. Artificial intelligence could help in creating new DeFi applications for trading assets or lending and borrowing.

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6. AI as a Tool for Cybersecurity

Web3 can capitalize on the capabilities of AI for detecting patterns and anomalies to facilitate improvements in cybersecurity. Therefore, artificial intelligence could emerge as a new contender in the domain of cybersecurity by detecting and preventing attacks proactively. The future of AI with web3 could introduce highly responsive mechanisms for monitoring threats and responding to incidents.

7. AI in Supply Chain 

The combination of web3 and AI emphasizes the element of transparency. Web3 could determine the future of artificial intelligence by providing opportunities for implementing AI in supply chain management. For example, AI could serve a crucial role in optimization of inventory management, preventing fraud, and tracking shipments. 

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8. AI in Healthcare

The outline of best web3 trends would also draw attention to the applications of web3 in healthcare. Artificial intelligence could have a revolutionary impact on healthcare sector in future. As of now, the use cases of AI in clinical research and drug development provide a clear impression of the future of artificial intelligence in healthcare. AI could help in efficient and faster diagnosis, providing personalized healthcare services, and developing new approaches for treatment.

9. AI in Transportation

The applications of AI in the transportation sector would leverage decentralization in web3 to introduce massive value-based improvements. For example, most popular web3 trends like decentralization could help in creating systems that use AI for developing self-driving cars. In addition, web3 trends could empower AI systems for optimization of traffic flow and improving public transportation systems.

10. AI and Web3 Gaming 

The prominent growth of web3 trends, such as metaverse and web3 gaming, also provide opportunities for incorporating AI. Web3 could define the future course of AI in the gaming industry with credible value advantages. AI could help in development of more realistic and immersive games alongside ensuring proactive detection of cheating. Most important of all, AI systems could also ensure personalization of gaming experiences.

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Final Words

The review of the top web3 trends and their impact on AI shows that the combination of web3 and AI could create new benchmarks in digital transformation. Web3 emphasizes notable trends such as decentralization, use of cryptocurrencies, NFTs, and smart contracts. On the other hand, AI helps in simulation of human intelligence in computers. With a combination of web3 and AI, you can find prospects for transformation of AI itself.

Web3 would open the scope for different use cases of AI. For example, AI could help in defining the designs of future metaverse and web3 games. In addition, AI could also serve as a critical component of decentralized systems for monitoring supply chains, transportation fleets, or healthcare systems. Explore the endless opportunities for capitalizing on the power of AI now.

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

The post Top 10 Web3 Trends Shaping the Future of AI appeared first on 101 Blockchains.

Smart contracts provide the foundation for creating dApps on the blockchain of your choice. The best thing about smart contracts is that they are pieces of code stored on a blockchain network. Smart contracts are similar to Ethereum accounts, albeit with significant differences against external accounts. For example, external accounts could connect to multiple Ethereum networks, such as Goerli Testnet. On the other hand, smart contracts are specific to the particular network on which they are deployed. 

When you deploy smart contract, it will help you create a contract account or instance on the network. Developers could generate multiple instances of a smart contract on one or multiple blockchain networks. You can deploy smart contracts by sending transactions to the network in the form of bytecode. Developers can choose different approaches for deploying smart contracts on Ethereum. Let us learn more about the recommended methods for deploying smart contracts in 5 minutes.

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Overview of Smart Contracts

Many of you might have doubts regarding the technical requirements for deploying smart contracts. However, you can overcome your apprehensions to deploy smart contract Ethereum with an overview of the fundamental concepts of smart contracts. Smart contracts are self-executing programs that can execute agreements and transactions without the involvement of intermediaries. Smart contracts have the following distinctive features,

• Turing completeness.
• Execution of contractual agreement between parties.
• Autonomous execution of transactions or agreements.
• Flexibility for execution in virtual machines such as EVM.
• Deployment on blockchain network. 

You can create smart contracts by leveraging programming languages such as Solidity. Subsequently, you can use different tools, such as Hardhat and Truffle, for deploying smart contracts on the desired blockchain network.

Deploying Smart Contracts Using Hardhat

Hardhat is one of the popular tools used for deploying smart contracts. You don’t have to follow any important technical prerequisites for using Hardhat to facilitate smart contract deployment on the blockchain. Here are the important steps for deploying smart contracts by using Hardhat. 

• Setting up the Environment

Before you create an instance on smart contract, you would have to set up the environment for deploying smart contracts. You can run the following code in the terminal of your system.

mkdir my-project-folder

cd my-project-folder

npm init -y

npm install --save-dev hardhat

The project starts by developing a project folder, followed by using ‘npm init –y’ to generate an NPM project. The ‘-y’ would imply the need for affirmation for every prompt. Subsequently, you can follow ‘deploy smart contract example’ by installing the ‘hardhat’ library.

Hardhat serves as a comprehensive Ethereum development environment, which helps in testing, compiling, deploying, and debugging smart contracts. It is also important to ensure installation of all project-centric dependencies. For example, you should add ‘npm install @openzeppelin/contracts’ for NFT smart contracts.

Excited to learn about the critical vulnerabilities and security risks in smart contract development, Enroll now in the Smart Contracts Security Course!

• Initiating Hardhat Project

The next step in deploying smart contracts using Hardhat involves initiating a Hardhat project. Within the project folder, you have to use the ‘npx hardhat’ command to create a basic Hardhat project. You have to select the default option for supporting all prompt questions. 

The guides on how to deploy smart contracts using Hardhat also require identification of folders with a sample smart contract within the contract folder. You have to delete the files such as ‘contracts/Greeter.sol’, ‘test/sample-test.js,’ and ‘script/sample-script.js.’ Subsequently, you would have to save the smart contract within the ‘\contracts’ folder.

• Configuration of Network and Private Key 

You can use a Testnet such as the Mumbai Testnet by Polygon. In the case of this example, you have to open the ‘hardhat.config.js’ and use the following code in place of the existing code. 

require('@nomiclabs/hardhat-waffle');


module.exports = {

  solidity: '0.8.3',

  networks: {

    mumbai: {

      url: '<mumbai-rpc>',

      accounts: ['<your-private-key>'],

    },

  },

};

As you deploy smart contract using Hardhat, it is important to follow certain precautions in this step. You would have to select the correct Solidity version and add the Mumbai RPC alongside your private key. You can also find a list of RPCs within the official document. It is also important to ensure that the file containing your private key should be restricted to the local machine. In addition, you can also configure the other networks within the networks section of the code. You could choose the network you want to use in the final deployment phase.

• Creating the Code for Deploying Smart Contracts 

Once you have set up the configuration file and development environment, you have to work on the code for smart contract deployment. You have to create the ‘deploy.js’ file within the ‘\scripts’ folder and use the following code. 

const main = async () => {

    const ContractFactory = await hre.ethers.getContractFactory('your-contract-name') // the file name under 'contracts' folder, without '.sol'

    const Contract = await ContractFactory.deploy(param1, param2, ...) // the constructor params

    await Contract.deployed()

    console.log("Contract deployed to:", Contract.address)


    // // You can test the function.

    // let txn = await nftContract.functionName()

    // // Wait for it to be mined.

    // await txn.wait()

    // console.log("function invoked!")

}


const runMain = async () => {

    try {

        await main()

        process.exit(0) // emit the exit event that ends all tasks immediately, even if there are still asynchronous operations that have not been done. The shell that executed node should see the exit code as 0.

    } catch (error) {

        console.log(error)

        process.exit(1)

    }

}

runMain()

The code provides a clear explanation for the different elements with clarity. You can utilize ‘hre.ethers’ for obtaining the contract and deploying it. However, it is important to follow certain precautions, such as,

Refraining from using ‘.sol’ in the ‘your-contract-name’ section in line 2. You can only use ‘myContract’ in this case. 

In the next line, you have to provide all the parameters needed for the ‘constructor’ in your smart contract. 

The lines from 7 to 11 can help you interact with smart contracts by invoking the desired functions. 

• Deploying the Contract

The final stage to deploy smart contract Ethereum is practically the easiest one in the guide for deploying smart contracts. You can use the following command for deploying the smart contract.

‘npx hardhat run scripts/deploy.js –network mumbai’  

You can find the output in the terminal with a message indicating successful Solidity compilation. It is important to remember the address of your smart contract, which is essential for creating the front end.

Want to understand the importance of smart contracts audits? Check out Smart Contract Audit Presentation now!

Deploying Smart Contracts by Using Truffle    

Developers could find multiple setup options for deployment, migration, and accessibility of smart contracts. In addition, they could choose different options according to the level of visibility they want in the Ethereum Virtual Machine. The two options involve using Geth for running a full Ethereum mining node and using an online IDE such as Remix. On the other hand, you can create instance on smart contract and deploy it with better effectiveness by using Truffle. It is a popular smart contract development tool for compilation and deployment of smart contracts while offering enhanced control and visibility.

Before you start using Truffle for deploying smart contracts, it is important to follow some essential precautions. First of all, you must save your Metamask wallet mnemonic.  In addition, you would have to obtain some test Ether. Another important requirement for deploying smart contracts involves obtaining a Ropsten API key through Infura. Here are the important steps required for deploying a smart contract by using Truffle. 

• Setting up Truffle 

The first step in a guide on how to deploy smart contracts using Truffle involves setting up Truffle. You can use the following command for setting up Truffle,

npm install –g truffle

Now, you have to create an empty repository and ‘cd’ in it, followed by using the following command, 

truffle init  

Then, you have to install the HDWalletProvider.

npm install --save truffle-hdwallet-provider 

• Creation of Contract

You have to create the new contract ‘HelloWorld.sol’ in the ‘./contracts’ section by using the following code.

pragma solidity ^0.4.23;

contract HelloWorld {

    function sayHello() public pure returns(string){

        return(“hello world”);

    }

}

• Deploying the Contract 

The actual step for deploying the contract involves creation of a deployment script. You have to create the ‘2_deploy_contracts.js’ deployment script in the ‘./migrations’ folder by using the following code:

var HelloWorld = artifacts.require(“HelloWorld”);

module.exports = function(deployer) {

    deployer.deploy(HelloWorld, “hello”);

    // Additional contracts can be deployed here

};

• Configuration of Ropsten Network and Provider

Developers can configure the Ropsten network and provider by adding the following snippet in the ‘module.exports’ section in ‘truffle.js.’ 

var HDWalletProvider = require("truffle-hdwallet-provider");

const MNEMONIC = 'YOUR WALLET KEY';


module.exports = {

  networks: {

    development: {

      host: "127.0.0.1",

      port: 7545,

      network_id: "*"

    },

    ropsten: {

      provider: function() {

        return new HDWalletProvider(MNEMONIC, "https://ropsten.infura.io/YOUR_API_KEY")

      },

      network_id: 3,

      gas: 4000000      //make sure this gas allocation isn't over 4M, which is the max

    }

  }

};

It is important to use your own ‘API_KEY’ and ‘mnemonic’ in the code. At the same time, you should also add ‘.gitignore’ to the file that contains your wallet mnemonic. Now, you can deploy the smart contract to Ropsten by using the following command. 

truffle deploy --network ropsten

Truffle would deploy to the local developer network only by default. The deployment would lead to a console log like the following,

Running migration: 1_initial_migration.js
Deploying Migrations…
… 0xd01dd7...
Migrations: 0xf741...
Saving successful migration to network…
… 0x78ed...
Saving artifacts…
Running migration: 2_deploy_contracts.js
Deploying HelloWorld…
… 0x0aa9...
HelloWorld: [SAVE THIS ADDRESS!!]
Saving successful migration to network…
… 0xee95...
Saving artifacts…

In this stage, you should pay attention to saving your contract address. You can examine the wallet address transactions by using an explorer-like Etherscan. 

• Access the Deployed Network

Another crucial addition to the steps in deploy smart contract example using Truffle framework involves setting up the Truffle console to the Ropsten network.

truffle console --network ropsten

You can access the deployed instance of your smart contract by using the following command,

HelloWorld.deployed().then(function(instance){return instance });

You could also retrieve the instance by using the public address through the following command,

web3.eth.contract(HelloWorld.abi, contractAddress)

In this case, the ‘HelloWorld.abi’ is the locally compiled ABI. The ‘contractAddress’ is the contract instance deployed publicly.

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How Can You Deploy Smart Contracts to a Local Network?

Another important highlight in a guide to deploy smart contract Ethereum would point at deploying smart contracts to a local network. You can use an emulator for deploying smart contracts on a local network, such as Ganache-cli. The emulator manages all the aspects of deploying the contract, and you don’t have to worry about the amount of gas and security required for transactions. However, you have to pass the Ganache provider in the form of an argument to the web3 instance.

How Can You Deploy Smart Contracts to Ethereum Network?

Prior to deployment of smart contracts to Ethereum blockchain, it is important to ensure that the account has the required Ether balance. The process of deploying a contract is similar to sending a transaction that would require gas fees for processing. However, the process to deploy smart contract directly to Ethereum would require some time to complete.

Web3.js can help you interact with the network in the same way as local deployment, albeit with customizing the provider that would be passed to the web3 instance. Rather than creating your own nodes for connecting to the Ethereum network, you can utilize a developer platform with RPC endpoints such as Alchemy or Infura.

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Conclusion

The guide on how to deploy smart contracts by using tools like Hardhat and Truffle provides a clear roadmap for deploying smart contracts within 5 minutes. You should also notice the best practices for deploying smart contracts to local network and directly to Ethereum network. However, it is also important to learn about the important dependencies and requirements for deploying smart contracts. Learn more about smart contract development and explore the essential best practices for creating and deploying smart contracts right now.

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

The post How to Deploy a Smart Contract in 5 Minutes? appeared first on 101 Blockchains.

However, security has emerged as a major concern for web3, primarily due to the use of smart contracts. Even a comprehensive web3 security audit could miss notable vulnerabilities such as integer overflow attacks, denial-of-service attacks, and reentrancy attacks. Furthermore, the decentralization in web3 apps also presents a formidable security concern as the apps would not have a centralized server or authority for taking care of security. In addition, web3 is largely open-source in nature, thereby enabling hackers to access the code and unravel vulnerabilities. 

You might be wondering about the solution to the web3 security issues as they could impose a massive burden of financial losses. Interestingly, you can find a reliable answer for avoiding web3 security issues in penetration testing. Penetration testing for web3 apps can help in evaluating dApps smart contracts alongside other web3 components for identifying vulnerabilities and potential sites of attack. 

You must understand the importance of web3 penetration testing, its different variants, and the methodology for penetration testing in web3 applications. Let us learn more about penetration testing in web3 and how it works. 

What is Web3 Penetration Testing?

Penetration testing or pentest in web3 is similar to the approaches followed for security testing in web2 applications. Anyone who wants to learn Web3 should know that web3 development has gained significant improvement in momentum. Many companies and developers want to capitalize on the web3 technologies and principles for embracing the decentralized web. Web 3.0 is a revolutionary paradigm that changes the functioning of different industries, such as finance, gaming, and supply chain management. 

The number of web3 startups has been growing steadily alongside the continuously expanding volumes of investment in web3. However, the growing popularity of web3 also paves the path for web3 vulnerabilities that can lead to irreversible consequences. If you go through the recent reports about web3 security, you can find that web3 security issues cause massive losses. 

For example, the total financial losses due to web3 security breaches in 2022 were over $3.5 billion. In addition, reports have pointed out that the losses due to web3 security breaches in the first six months of 2023 have crossed $650 million. Therefore, it is important to look for proactive methods that can help safeguard user data, funds, and integrity of blockchain architecture. 

Penetration testing can outperform the most powerful web3 security tools for safeguarding web3 apps and users. Penetration testing in web3 is a comprehensive process for evaluating the security of smart contracts, blockchain networks, and dApps. The recommended approach for penetration testing in web3 focuses on simulation of real-world attacks for identifying weaknesses and vulnerabilities in the web3 landscape.

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Difference between Traditional Penetration Testing and Web3 Penetration Tests

Web3 penetration tests differ from traditional penetration testing in different ways. The first difference is evident in the fact that web3 apps run in decentralized environments, which presents specific security risks. For example, smart contract vulnerabilities could open new surfaces of attack for hackers. In addition, web3 apps also follow different protocols and interfaces, such as JSON-RPC, which requires specialist testing knowledge and equipment. 

Another differentiating factor between web3 and web2 penetration tests is the use of blockchain technology. When you learn web3, you can find out that web3 apps feature inherent security traits. However, the inherent security traits could not safeguard web3 apps against vulnerabilities in the code or approaches for interacting with blockchain. 

Most important of all, you must also focus on the necessity of specific regulatory requirements for web3 during penetration testing. For example, DeFi applications must comply with financial regulations in their search for vulnerabilities. 

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Working of Penetration Testing in Web3 

You must know about the ideal steps for implementation of penetration testing in web3 to ensure the best results. Effective penetration testing in web3 requires comprehensive planning and developing the scope of the testing project. Effective planning for a web3 security audit could help in identification and evaluation of all the potential vulnerabilities in web3. 

Some of the critical stages in the planning stage include establishing the objectives and milestones for the project. Subsequently, you would move towards other stages of penetration testing, such as understanding the architecture and development of testing strategy. Here is a detailed overview of different steps in the working of web3 penetration tests. 

• Define the Aim of Testing

The first stage of web3 penetration testing involves clear definition of objectives and scope of testing. What are the objectives for web3 penetration tests? You have to choose the precise targets, such as dApps, smart contracts, or wallets. It is important to understand the target environment to ensure the identification and assessment of all potential vulnerabilities. 

• Understanding the Architecture and Technologies

One of the significant requirements for successful penetration testing in web3 points to your understanding of web3 architecture and technologies. Web3 apps utilize different tools and structures in comparison to traditional web applications. Therefore, you must learn web3 architecture and technology with a clear impression of web3 protocols and interfaces, blockchain technology, and smart contract programming languages. 

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• Select the Testing Procedure

The next stage in the working of penetration tests involves specification of testing procedures required for the tests. You can choose automatic or manual web3 tests. On top of it, you could find dedicated web3 security tools and frameworks for web3 penetration tests. With a clear impression of testing objectives and the target environment, you can pick the ideal tools for successful penetration tests.   

• Prepare Your Testing Plan 

The final stage in the planning phase of the working of penetration tests in web3 involves preparation of testing plan. Once you have defined the objectives, testing techniques, and target environment, you have to create a testing plan. The testing plan would include details about the tests that you would implement and the required tools for the same. 

In addition, you could also determine the timing of different tests. It is important to review the testing plan and strategy with the involvement of all parties to obtain authorization from all the stakeholders.

Types of Penetration Tests in Web3 

The next topic of discussion in a guide to penetration tests in web3 focuses on variants of penetration tests. You should note that penetration tests involve simulation of attacks on web3 systems and networks for identifying vulnerabilities. At the same time, you might come across three distinct types of web penetration testing for mitigating web3 security risks. Here is an outline of the different types of penetration tests involved in web3. 

• External Network Penetration Tests

External network penetration tests focus on identification of vulnerabilities in the perimeter safeguards for web3 apps. In such types of penetration tests, you can find simulations of attacks from external threat actors. The tests help in determining the effectiveness of security controls, such as web application firewalls, firewalls, and intrusion detection systems. The external network penetration test can help in identifying crucial vulnerabilities such as weak password policies, open ports, and unpatched software. 

• Internal Network Penetration Tests

The next variant of penetration test for identifying web3 vulnerabilities is the internal network penetration test. Internal network penetration tests work through simulation of scenarios where a malicious actor gains access to internal network of web3 apps. Such types of penetration tests focus on identifying internal vulnerabilities such as misconfigured access controls, inappropriate network segmentation, and unsecured databases. 

• Application Penetration Test

Web3 security professionals must also focus on the application penetration tests to determine vulnerabilities in the application itself. Application penetration tests are a mandatory addition to web3 security audit as they help in recognizing security issues such as authentication bypass, SQL injection, or cross-site scripting. Application penetration testing is a powerful tool for safeguarding privacy of user data alongside preventing unauthorized access.

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What are the Other Components of Web3 Penetration Tests?

Penetration tests in web3 do not focus on simulation of attacks on the perimeter of web3 apps, their internal networks, and the application itself alone. You could find other components in penetration tests that help in uncovering a wide range of vulnerabilities in web3. 

The components in web3 penetration tests include smart contract audits, blockchain testing, wallet software testing, and DevOps penetration testing. Each component plays a crucial role in web3 penetration testing by reviewing different aspects of web3 for security issues. Let us take a look at the important areas of testing in each component of web3 penetration tests. 

• Smart Contract Audits

The role of smart contracts in the web3 ecosystem cannot be undermined. Smart contract audits form a crucial part of web3 security audit procedure as they help in testing access control, transaction order dependency, vulnerability to denial of service, and other asset management capabilities. The common vulnerabilities identified in smart contract audits include time manipulation, insufficient access controls, reentrancy attacks, and short address attacks.

Want to understand the importance of smart contracts audits? Check out Smart Contract Audit Presentation now!

• Blockchain Testing 

The types of tests involved in penetration testing also involve blockchain testing, which checks vital components and potential attack surfaces. Blockchain testing involves evaluation of peer-to-peer protocol vulnerabilities, blockchain block parsing, RPC authentication, and secure RPC method implementation. The common attack surfaces identified in blockchain testing include communication interfaces, OS and services, DevOps, and input management.

• Wallet Software Testing 

The review of web3 security tools and their importance also reflects on the necessity of wallet software testing. Some of the important components involved in wallet software testing include a user interface, RPC interface, software dependencies, and transaction management. In addition, wallet software testing in web3 penetration tests also reviews the connection of web3 wallets to the third-party nodes and services. 

• DevOps Penetration Tests 

Another notable addition among the types of web penetration testing for web3 points at DevOps penetration testing. DevOps has become an open target for malicious actors owing to its large technological footprint and limited security controls. In addition, DevOps also offers privilege for modification of source code and deploying it into production. 

The primary focus of DevOps penetration tests is directed toward assessment of code repository contents and access privileges, secrets management, and access to production deployment. DevOps penetration tests also focus on the CI/CD infrastructure alongside authentication for sensitive development components and developer access to the production credentials.

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What are the Popular Tools for Web3 Penetration Tests?

The specific design of web3 apps requires the use of specialized tools for penetration testing in web3. You can rely on web3 security tools to support web3 developers and security professionals in recognizing and addressing vulnerabilities. Here are some of the most popular.

• Mythril

Mythril is a smart contract security analysis tool for smart contracts deployed on Ethereum. It also offers the flexibility for identifying different web3 vulnerabilities, including logical errors, reentrancy, and integer overflow or underflow. 

• EthFiddle

EthFiddle is one of the emerging tools in the web3 security landscape, as it can help programmers create and test Ethereum smart contracts in a browser-based environment. The security testing tool features different simulation tools alongside an integrated debugger for evaluation of smart contract security posture. 

• ZAP 

Another notable addition among tools for web3 security points at ZAP. It works as a web3 app security scanner and features different plugins for testing web3 apps.

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Final Words

The overview of web3 penetration testing showcases that it is an ideal technique for security of web3 apps. Web3 security has emerged as a formidable concern for developers and the broader web3 community due to humongous financial losses. On top of it, the decentralization and open-source nature of web3 expose web3 apps to different types of security risks. Users can find the ideal countermeasures for avoiding such security risks by using penetration testing. 

It is important to understand that web3 penetration tests could deviate from conventional penetration testing in certain aspects. However, the ultimate objective of penetration tests revolves around a simulation of attacks to check the resiliency of web applications. Penetration tests can serve as a promising boost to the web3 development landscape and encourage the rise of secure web3 apps. 

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

The post A Detailed Guide To Web3 Penetration Testing appeared first on 101 Blockchains.

Crypto and DeFi bear markets could reduce portfolio value. Most important of all, bear markets also have the tendency to last longer than the expectations of users. However, the good thing for investors in the bear market is the opportunity for re-focusing and spending time on research for projects that could gain momentum in bullish markets. 

The continuous development of the DeFi and crypto market leads to the inevitable occurrence of bear markets. The bear market definition finance systems put forward in the 2022-23 market indicate the negative impact of bear markets. With the formidable price drops in major cryptocurrencies, such as Ethereum and Bitcoin, bear markets can lead to losses for investors.

On the other hand, it is also important to know that the downward trends would reach the lowest point before the bull market begins. Experienced crypto investors could monitor the important metrics and indicators that can guide them toward the next Bull Run. Let us learn more about the top DeFi metrics that can help investors in a bear market.

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What is the Necessity of DeFi Metrics in Bear Markets?

The importance of DeFi metrics in bear market could help in understanding their role in driving investors toward better decisions. Metrics could help investors and analysts identify the beginning of a robust and effective cycle. The common highlights that determine the effectiveness of DeFi projects in bear markets include technical factors, on-chain activity, and supply dynamics. On top of it, the profit/loss metrics could also provide a detailed impression of the effectiveness of DeFi projects.

The technical aspects of a DeFi project point towards its performance according to popular technical benchmarking tools. On the other hand, on-chain activity could showcase the positive trends in network utilization and on-chain activity. Furthermore, supply dynamics could also help in identifying the saturation of coin supply in the ownership of long-term owners. The bear market for DeFi has been characterized by radical drops in prices of native tokens of DeFi projects. In addition, it is also important to learn about the metrics for DeFi market that can help in identifying when the next bull market would happen.

Enroll now in the DeFi Development Course to learn about the functionalities of automated market makers and liquidity pools in DeFi solutions.

Top DeFi Metrics for Bear Market

The primary aim for using DeFi metrics in a bear market is the opportunity to invest in the right projects that could perform well in bullish market conditions. What are the best DeFi metrics in bear market conditions? Here is an outline of the top DeFi metrics that could help you through a bear market.

• Use Case of the DeFi Solution 

The DeFi sector has many projects that promise huge returns and feature protocols with multiple gimmicks. However, only a few projects have successfully delivered products that have high demand and deliver effective utility. One of the most important bear market definition finance sector has put forward is the impression of failing projects. On the other hand, you can use the most important metric for DeFi projects to determine their effectiveness in bear markets. Irrespective of the market conditions, DeFi solutions can withstand the downward trends if they have a proven utility.

You should determine whether the DeFi project has some form of utility. It is important to look for DeFi projects that solve a pressing concern. DeFi projects with a proven utility could stand the test of time in any market condition and could prepare for long-term survival. 

• Exchange Inflows 

The next metric required for evaluating DeFi solutions in a bear market is the exchange inflows. The flow of native DeFi tokens transferred on exchanges can provide a clear impression of investor sentiment. You can find answers to “What is bear market DeFi?” by checking how the native tokens perform on different exchanges. The increasing inflows would mean that investors want to sell their tokens due to bearish sentiment. On the other hand, declining exchange inflows could imply that asset holders want long-term storage, thereby suggesting that the bear market is declining.

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• Competitive Advantage of the Project

The utility of a DeFi project is not the only thing that would guarantee its survival in a bear market. However, the best way to understand bear market for DeFi would involve a study of the unique advantages delivered by the project. It is important to find out how the protocol performs in comparison to other projects which offer solutions for the same problem.

Does the DeFi project offer simpler or better solutions than competitors? For example, DeFi oracles have become one of the common entries among new protocols in the DeFi market. However, users are likely to choose the oldest and most prominently integrated Oracle solution or Chainlink, which is one of the strongest competitors. 

• Cash Reserves

The list of indicators for checking the performance of DeFi projects in bear market also includes cash reserves. Every DeFi project must have a treasury that could help it survive the downward trends in the bear market. Cash reserves are an important metric for DeFi projects, which provide yield on locked assets that can encourage liquidity. 

The DeFi metrics in bear market would also point to the role of cash reserves for obtaining adequate liquidity for surviving an extended bear market. You should look for a DeFi project that has a massive treasury featuring different types of assets such as Bitcoin, Ether, and other stablecoins such as Tether and USD Coin.

The massive, diversified, and well-funded treasury could help in pulling the DeFi projects through tough times. DeFi projects should learn when they should take profits and should not use one token for majority of the protocol’s treasury. 

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• Clear Roadmap

One of the most prominent techniques for determining the effectiveness of DeFi projects in a bear market is the roadmap. A DeFi project does not have a chance at success just because it has a clear roadmap. You should know that past performance does not provide an indicator of future results. 

However, a DeFi project must consistently follow its roadmap and meet important deadlines. The performance of a DeFi project in fulfilling its roadmap could help you identify how it can endure tough times. You could use tools for monitoring the frequency of developer activity on DeFi protocols. If the project does not maintain consistency in fulfilling the roadmap deadlines, then it might be a rug-pull project.

• Market Volatility 

You can find extreme levels of volatility in a bear market for DeFi and crypto projects. Prices go on a downward trend, and volatility reaches peak levels during the bear market. Stabilization and decline of volatility readings in a bear market would imply that the bear market is gradually easing. In addition, it also provides a trusted indicator that shows how markets are embracing support mechanisms to facilitate recovery.

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• Market Sentiment Survey

The most crucial metric in the domain of DeFi points to the sentiment about the project in a market. You can use large-scale surveys to learn more about the inclination of investors and experts about DeFi projects. Sentiments that could indicate fear point at the downward trends, and improvement in optimism could support recovery from bear markets.

• On-Chain Transaction Value 

The outline of best DeFi metrics in bear market would point at the on-chain transaction value. With the total USD value of transactions on a DeFi solution, you can monitor the token velocity and overall network usage. Growth of on-chain transaction values following a bear market shows that the project has been performing effectively for organic adoption.

• Social Sentiment

The social sentiment around a DeFi project would also serve as an important metric for bear markets. Investor psychology also plays a major role in defining the way a DeFi market works in bear conditions. You can track social media posts and communities to obtain insights about crowd sentiment for DeFi projects. It is important to check whether the social sentiment about a DeFi project can shift from fear and anticipation towards optimistic trends.

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• Trading Volume 

The trading volume of DeFi projects also serves as a prominent DeFi metrics in bear market with an impression of its effectiveness. Higher trading volumes indicate that the project has been gaining engagement. On the other hand, it also implies that market participants have been showcasing interest in the DeFi project. DeFi projects with stronger trading activity could imply more conviction.

On the other hand, unstable trading volume could imply that the project has been falling prey to the bear market. It is also important to review the bear market for DeFi projects on the basis of derivatives activity. An increase in derivatives trading activity could imply that the DeFi project has prepared for the end of a bear market.

• Miner Capitulation 

You can also refer to miner capitulation as an important metric for DeFi projects during a bear market. For example, Bitcoin miners could stop their operations when they face a decline in profitability. Therefore, all-time lows for hash rate and rebound in mining activity suggest that miners look forward to the situation of recovery. 

• New Influx of Capital

The most important metric for a bear market definition finance projects could show in a time of recovery is the influx of capital. You should notice that on-chain analysis provides an estimate of the evaluation of realized profit and loss. It is a difference in the value of coins at disposal, as compared to the value during the time of acquisition.

The ratio of realized profit to loss can offer oscillator tracking to prove whether the aggregate volume of realized profits can surpass the volume of realized losses. The ratio of realized profit to loss could help in identifying larger macro shifts in the profitability of DeFi markets. In addition, the volatility of markets could lead to false positives. However, they can be accounted for in the large-scale shifts in the DeFi market. 

• Foundation for DeFi Success

The foundation for a DeFi project also serves as a crucial metric in bear markets. A DeFi project could establish a robust foundation for a bear market when a larger volume of supply should be exchanged at lower prices. The trend implies the expulsion of sellers alongside an equal influx of new accumulation demand. One of the earliest indicators, such as macro trend reversal in strong foundations, emerges in the form of sharp changes in the percentage of the total supply in profit. Interestingly, the trend is triggered by a relatively small increase in pricing.   

• Confidence of New Investors

The most important addition among the best DeFi metrics in bear market points to confidence of new investors in the recovery trend. Observation of the spending patterns of new investors could help in showcasing the confidence of new investors in DeFi projects. One of the practical methods for evaluating the metric for confidence of new investors is the comparison between the values of unrealized profits in newly acquired and HODLed coins and the profit realized from spent coins. In simple words, the metric would evaluate the difference between cost basis of short-term holders that are spending and the ones that are holding.

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Conclusion 

The bear market in the DeFi sector has created an environment of uncertainty for investors. However, comprehensive research and careful selection of DeFi metrics in bear market could help in finding the ideal ways to navigate the DeFi market. It is important to look for the DeFi projects that have been gaining the attention of new investors, have a clear roadmap, and fulfill important deadlines.

In addition, you must also look at the metrics such as trading volume and on-chain transaction volume. On top of it, you can also utilize metrics such as social sentiment and market sentiment for DeFi projects in bear markets. Learn more about DeFi and find out the best practices for finding your way through a bear market scenario.

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

The post Which DeFi Metrics Are Useful in a Bear Market? appeared first on 101 Blockchains.

You must have come across multiple news articles and blog posts singing praises of generative AI alongside outlining its limitations. Generative AI tools such as ChatGPT and Google Bard have proved how AI helps businesses. Prompt engineering elevates the potential of AI for the transformation of conventional systems and processes in different industries.

The new prompt engineering certification launched by Metin (Senior Software Enginner) is a trusted learning resource that will help you achieve specialization in prompt engineering. It helps you learn more than the fundamentals of prompts in generative AI and the best practices for creating prompts. The CPEE certification training course offers practical training that will help you utilize Large Language Models and techniques for prompt engineering. Let us show you some of the most promising highlights of our new prompt engineering certification training course.

The Certified Prompt Engineering Expert Certification Course 

Generative AI has created promising opportunities for the mainstream adoption of artificial intelligence. For example, more than 60% of companies utilize generative AI, with the highest adoption rate in the marketing and advertising industry. According to a survey by Salesforce, around 75% of users rely on generative AI for the automation of tasks at work and communication. 

The survey also revealed that around 67% of employees believe generative AI could offer more value compared to other technology investments. Therefore, an AI prompt engineering certification will help you in navigating the newly formed career paths for AI professionals. On top of that, more than 80% of IT leaders believe that generative AI would have a transformative impact on their business.

The Certified Prompt Engineering Expert certification has been created to offer a comprehensive training course on prompt engineering. In the prompt engineering course, you will learn about the basic concepts of prompting and large language models. This training course also helps you learn about the important techniques and patterns for prompt engineering. Most importantly, the course includes hands-on training, demos, and interactive exercises that help you reinforce the learning objectives.

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The Target Audience for the CPEE Certification Course 

The new certification training course on prompt engineering serves as an ideal option for professionals wanting to specialize in prompt engineering. It will help you become a Certified Prompt Engineering Expert with an in-depth understanding of the capabilities of prompt engineering. This certification course is an ideal choice for candidates aspiring to become AI professionals. Furthermore, it will help the following groups of learners with distinct value advantages.

• Data scientists, AI researchers, NLP, and machine learning practitioners
• Software developers and engineers
• Product & Program Managers
• Innovation Managers & Entrepreneurs
• Anyone who wants to learn about LLMs and AI advancements
• Individuals interested in obtaining an industry-leading Prompt Engineering certification

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Basic Information about the Certified Prompt Engineering Expert Certification

The Certified Prompt Engineering Expert (CPEE) certification training course is an exclusive instrument that helps you achieve prompt engineering expertise. You can use the course to achieve the following learning objectives.

• Develop a comprehensive understanding of Large Language Models and the techniques for prompt engineering.
• Achieve practical expertise in the use of OpenAI API alongside the best practices for implementing API calls to enable interactions with LLMs. 
• Familiarize yourself with the latest industry trends and technical skills required for using LLMs in different contexts throughout different industries. 

The new AI prompt engineering certification serves as a vital resource for familiarizing with a broad range of topics related to prompt engineering. Interestingly, this certification serves as a comprehensive prompt engineering course that helps you prepare for a career in generative AI with hands-on training, demos, and interactive exercises. Here are some of the important topics covered in the Certified Prompt Engineering Expert certification training course.

• The fundamentals of Large Language Models
• The basic concepts of prompting. 
• Patterns and techniques required for prompt engineering.
• How to use OpenAI API to make API calls.
• GPT-4 extensions and their practical applications. 
• Advanced techniques and patterns in prompt engineering for complex tasks.
• The future of prompt engineering, LLMs, and generative AI.

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The Reasons to Choose the Certified Prompt Engineering Expert Certification

The Certified Prompt Engineering Expert certification course is not just a regular prompt engineer certification for beginners. It offers promising value advantages with its unique learning outcomes that help you specialize in prompt engineering for different use cases. Here are some of the promising reasons to choose the Certified Prompt Engineering Expert certification training course.

• Comprehensive Specialization

The first reason to choose the Certified Prompt Engineering Expert certification course is the advantage of comprehensive specialization. You can use the new prompt engineering certification launched on our platform to prove your skills in basic as well as advanced patterns and techniques for prompt engineering. This certification course also helps you learn about OpenAI APIs and GPT-4 extensions alongside the practices for incorporating web searching capabilities in prompt engineering and GPT-4.

• Professional Guidance and Learning 

The next reason to choose the Certified Prompt Engineering Expert certification is the opportunity to become a Certified Prompt Engineering Expert with the guidance of a professional instructor. You will learn the skills for prompt engineering under the tutelage of an industry expert, which ensures credibility. On top of it, you can also reach out to your course instructors for doubts and queries regarding the topics covered in the certification course.

• Recognition for Prompt Engineering Skills

The most important reason to choose the Certified Prompt Engineering Expert certification course is the assurance of recognition of your skills. You can become a prompt engineering expert with an industry-recognized credential, thereby serving effective opportunities for career development.

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Final Words

The new Certified Prompt Engineering Expert certification course launched on our platform is proof of our efforts toward encouraging professional technology training. With the CPEE certification course, we aim to help learners unravel the paths to new career opportunities in AI. As a trusted partner in the career development journey of thousands of professionals, we believe in continuous improvements. Learn more about the Certified Prompt Engineering Expert certification and find out its value benefits now.

The post The World’s First Accredited Prompt Engineering Certification Launched appeared first on 101 Blockchains.

Decentralized technologies have offered a strong foundation for growth of innovation. However, the advantages of decentralization could not overshadow the unique challenges to blockchain security. For example, decentralization introduces transparency, and hackers could leverage the transparency to identify vulnerabilities in smart contract code.

At this point of time, questions like “What does blockchain use for security?” present significant concerns for blockchain projects and professionals. Blockchain security leverages a combination of different cybersecurity principles and best practices alongside tools for mitigating security risks.

Blockchain security tools or security software can provide better transaction integrity and data protection. In addition, blockchain security tools can also improve the management of access controls alongside providing timely notifications about discrepancies. Let us learn more about some of the most popular security tools used in the domain of blockchain.

What are the Top Tools for Blockchain Security?

Security tools serve as an important component of blockchain security with their unique functionalities. Interestingly, you can find a broad range of additions in a blockchain tools list for ensuring security of your valuable blockchain apps and assets. Blockchain security is an essential priority for small businesses, financial institutions, technology companies, government agencies, manufacturers, healthcare organizations, and developers.

What are the top tools for blockchain security you can use right now? The blockchain ecosystem has hundreds of tools that can help you safeguard blockchain security at all costs. Interestingly, the collection of blockchain security tools features platforms that help in monitoring blockchain networks and dApps for security discrepancies. Here is an outline of different popular tools for blockchain security with a brief overview of their functionalities.

• Beosin Eagle Eye Platform

Beosin Eagle Eye is one of the best blockchain tools for ensuring security of blockchain networks and solutions. It provides round-the-clock security monitoring and real-time notification services for different blockchain networks. Beosin Eagle Eye sends security alerts and notifications to customers when it detects discrepancies and security risks such as flash loans or hacks. One of the formidable advantages of Beosin Eagle Eye is the faster identification of high-risk transactions. It leverages off-chain and on-chain data analysis alongside multi-dimensional security assessments for improving blockchain security.

• Consensys Diligence

Consensys Diligence is a comprehensive smart contract audit service that supports everyone, including established corporations and startups. It can help you in the deployment and maintenance of Ethereum-based dApps and smart contracts. Consensys Diligence offers access to some of the popular blockchain security tools for comprehensive analysis and hands-on review in smart contract audits.

It supports you in effective and early code audits during the development process to prevent potential vulnerabilities. The APIs on Consensys Diligence provide affordable smart contract security tools alongside the assurance of error-free code. On top of it, developers can integrate Consensys Diligence tools in their development environments for continuous security analysis. Furthermore, the platform also offers a vulnerability report with details of the vulnerabilities and suggestions for risk mitigation.

• Chainalysis

Another popular tool for blockchain security is Chainalysis. It is a cryptocurrency analytics software that helps financial institutions and regulators prevent malicious actors from using cryptocurrencies in illegal activities, such as extortion and money laundering. Chainalysis is one of the top blockchain security tools with different features that offer value-added improvements.

For example, the Chainalysis Reactor serves as an intuitive interface for conducting an in-depth investigation into the origins and ownership of crypto transactions. Chainalysis KYT also helps in ensuring monitoring of transactions for compliance with essential regulatory requirements. Interestingly, the interface of Chainalysis has been designed for intuitiveness and to ensure alignment with workflows recommended for anti-money laundering.

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• Elliptic

The list of top platforms that offer blockchain security tools also includes Elliptic, a trusted destination for crypto compliance services. It is one of the prominent additions to a blockchain tools list for addressing the requirements for managing financial crime risks and ensuring regulatory compliance.

On top of it, businesses can also rely on Elliptic for blockchain analytics to access accurate and relevant insights for over 100 different crypto assets. Regulatory compliance and AML monitoring can play a crucial role in building trust between regulators, partners, other stakeholders, and customers.

• SlowMist

SlowMist Technology is another platform that has come up with exclusive tools for blockchain security. It was created in January 2018 and has emerged as a prominent tool for defense against blockchain security threats. The team members behind SlowMist have extensive experience in world-class cyber safety engineering.

As one of the popular blockchain security tools, SlowMist serves some of the top projects worldwide. SlowMist follows a distinct approach to delivering threat detection solutions. In addition, the integrated threat defense solutions by SlowMist can help in dealing with existing local conditions for blockchain security.

• Hacken

The most important reason to focus on blockchain security is the sensitive data stored in blockchain applications. Malicious actors could seek vulnerabilities in blockchain solutions as an easy way to make some profits. Hacken can help you ensure safeguards against such vulnerabilities with comprehensive penetration tests.

It is one of the top additions to a blockchain tools list for security of blockchain applications. Blockchain applications address many other functionalities, including smart contracts, online transaction management, and asset ownership management. With the help of comprehensive audits by Hacken, blockchain solutions could stand up to the challenges of prominent security issues.

• Forta

Forta is an emerging entry among the top tools for ensuring blockchain security. It is a decentralized monitoring network that supports real-time detection of threats in web3 systems. As one of the best blockchain tools for security, it leverages the power of machine learning to generate insights about scams, exploits, and different threats from a community of web3 security experts.

The Scam Detector on Forta could help in scanning web3 wallets for screening pre-signed transactions. In addition, DeFi developers could create and use custom detection bots for monitoring the security of DeFi protocols. On top of it, the Attack Detector bot in Forta can offer real-time alerts for threats to investors.

Start your journey to becoming an expert in Web3 security with this interactive Web3 Security Expert Career Path.

• Harpie

The next addition among important tools for blockchain security includes Harpie. It is the first variant of on-chain firewall for protecting blockchain users against theft, hacks, and scams. The tool monitors your wallet multiple times in a second and stops transactions when it detects any possibility of foul play.

It offers immediate updates on the new vulnerabilities identified in blockchain wallets. In addition, Harpie also leverages the largest and most updated database of malicious addresses to prevent security breaches. Most important of all, Harpie also offers easy access to a broad collection of resources and information on new and historical web3 scams.

• Trail of Bits

Trail of Bits is also a renowned choice in blockchain security tools list for safeguarding the most popular web3 solutions. It is a leading blockchain auditing platform and also provides the advantage of designing open-source web3 security tools. Trail of Bits could help blockchain companies strengthen smart contract security alongside understanding and managing contract storage.

It stands out from the crowd with powerful functionality for identification of different vulnerabilities possible in the world of blockchain and web3. Trail of Bits also creates industry-leading tools for web3 developers. As a matter of fact, it is the creator of Slither, a popular web3 security tool.

• Pocket Universe

Pocket Universe is an accessible blockchain security tool for maintaining safety of your assets during transaction signing. The responses to “What does blockchain use for security?” could point to the functionalities of Pocket Universe for identifying and reporting malicious transactions.

On top of it, you can avail of Pocket Universe as a free browser extension. The tool also helps you understand which assets are transferred with your signature. Most important of all, it provides insurance of up to $2000 if users lose their assets under the platform’s protection. Pocket Universe has over 60,000 users and analyzes more than 100,000 signatures every day.

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• Arbitrary Execution

Arbitrary Execution is also one of the most popular tools for blockchain security, which safeguards web3 protocols, users, and their assets by leveraging continuous security solutions. It is a notable addition to blockchain tools list for maintaining a focus on engineering for security of decentralized technology.

The platform relies on the capabilities of its team of security researchers to strengthen blockchain security. The security researchers could capitalize on their security expertise, attacker mindset, tactics, and techniques for safeguarding the web3 ecosystem. Interestingly, Arbitrary Execution has successfully completed more than 45 audits on web3 projects and protocols.

On top of it, the platform has developed tools for continuous monitoring of blockchains to identify anomalous activity. Most important of all, Arbitrary Execution has also introduced the Web3 SDLC, which serves as a valuable framework for integration of security in the web3 development lifecycle.

• SharkTeam

The list of notable players in the domain of blockchain security also features emerging names such as SharkTeam. It provides smart contract audits, incident response, and chain analytics services. SharkTeam makes it to the list of best blockchain tools for security with proven effectiveness in ensuring web3 security.

The team behind SharkTeam has envisioned comprehensive protection for security in web3. As a matter of fact, the effectiveness of SharkTeam in facilitating blockchain security depends on expertise of senior researchers and security experts. The security experts can leverage their knowledge of blockchain and smart contracts to offer a stronger security infrastructure to protect blockchain solutions.

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• ChainAegis

The collection of the best security tools for blockchain and web3 also includes players like ChainAegis. It provides a broad range of services for ensuring blockchain security, such as risk warning, intelligent analysis, and protocol ranking. The on-chain security analysis tool serves real-time monitoring advantages alongside responsive notifications for blockchain transactions.

It depends on extensive address labeling with markers for specific risk types, such as ransomware and fraud, to ensure comprehensive risk identification. Users could leverage ChainAegis for round-the-clock address monitoring and timely notifications for suspicious transaction behavior. You can also use the customization feature on ChainAegis for personalized configuration of monitoring rules suited to distinct requirements.

• ERC-20 Verifier

The comprehensive list of top blockchain security tools would also include ERC-20 Verifier. It is a popular analysis tool that helps in confirming whether smart contracts adhere to the ERC-20 format. OpenZeppelin offers the ERC-20 Verifier tool for confirming the underlying structure of smart contracts verified on Etherscan. Users can rely on ERC-20 Verifier for scanning known smart contracts, and developers could have a detailed impression of the underlying logic in smart contracts.

• MythX

MythX is also another popular addition to a blockchain security tools list with its functionalities for EVM smart contract security analysis. The most noticeable highlight of MythX is that it focuses on EVM smart contracts only. Developers can capitalize on the functionalities of MythX with popular IDEs like Remix.

In addition, MythX also offers flexibility for use with Truffle for enhancing the security pipeline before and after deploying smart contracts. The service helps in adjusting the severity of analysis. Most important of all, MythX leverages fuzzing for identifying Solidity code vulnerabilities before the production stages.

Get familiar with the terms related to blockchain with Blockchain Basics Flashcards.

Conclusion

The outline of popular blockchain security tools shows that you have multiple options for protecting blockchain solutions. As the blockchain ecosystem matures and evolves with the introduction of web3 technologies, it is important to focus on blockchain security. You need reliable best practices alongside the right tools for addressing different precedents of blockchain security.

For example, audit tools can help in monitoring smart contract code for errors before deployment. On top of it, you can also find platforms that offer real-time monitoring or blockchain security analytics. Developers could use some blockchain security tools with the benefit of integration in the development lifecycle for strengthening blockchain security. Learn more about the fundamentals of blockchain security and best practices for safeguarding your new blockchain-based projects.

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

The post A Comprehensive List Of Blockchain Security Tools appeared first on 101 Blockchains.

As a matter of fact, it is one of the top NLP applications that use speech recognition to understand user queries and requests. Why do you need NLP now? Analysts have pointed out that almost 80% to 90% of the data available today is in unstructured formats. Most of the information is present in the form of conversations on social media platforms and interactions with customer service representatives.

Companies want to rely on natural language processing applications to draw insights from the massive repositories of unstructured information. NLP focuses on improving the ability of machines to understand and respond in natural language. Therefore, it can also provide new ways for transforming or personalizing user experiences. Let us learn more about the different types of NLP applications that could guarantee promising improvements for the productivity of businesses.

Want to develop an in-depth understanding of large language models and prompt engineering techniques? Enroll now in the Certified Prompt Engineering Expert (CPEE) Certification

What is NLP and How Does It Work?

The first thing required for understanding different NLP applications is the definition of NLP and a clear impression of its working mechanisms. You can understand how the applications of NLP work by identifying the different steps in the working of NLP algorithms. Natural language processing systems use machine learning to train computers to understand, process, translate, and respond to natural language. You can identify the importance of NLP in its ability to improve communication between machines and humans. Rather than breaking down sentences into different words, NLP can enable computers to understand the semantics and context of user statements.

You can explore a detailed description of the workings of NLP with a review of natural language processing tools and frameworks. Some of the popular NLP tools include Python Natural Language Toolkit and statistical machine learning models. You can use these tools to create new NLP applications tailored for specific tasks. On the other hand, you might have some doubts regarding how NLP systems work under the hood.

The examples of NLP applications in AI systems could create confusion for users in understanding the working mechanisms of NLP. How could NLP teach natural language to machines that understand nothing but ones and zeroes? On the contrary, machine learning has the power to train computers to understand natural language. A natural language processing algorithm works by following simple steps such as,

• User sends their service request to the machine through text message or voice.
• The machine would record the sound in the case of voice data and convert it into text. 
• The NLP system divides the text into different components and interprets the context of the conversation alongside the intent of the user. 
• According to the results of the NLP, the computer would determine the ideal command that should be used. 

You can notice that the best NLP applications rely on similar algorithms capable of transforming words in the text into labels according to their position and function in a sentence. One of the prominent NLP techniques points to word embedding, which can help in resolving multiple NLP issues.

Word embedding could transform human language into numerical form. As a result, computers could easily identify the subtle nuances included in natural language, such as sarcasm. Word embedding focuses on transforming words into a set of numbers or an N-dimensional vector for storing information about the meaning of the concerned word.

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Top Applications of Natural Language Processing

Natural language processing is an important subdomain of artificial intelligence that can help machines understand and process human language. The working mechanisms of natural language processing applications leverage computational linguistics alongside machine learning, statistical modeling, and deep learning. Most important of all, you must note that NLP could introduce promising business benefits.

Therefore, innovative developments in the domain of natural language processing could create opportunities for achieving more productivity. New NLP applications with diverse functionalities could help speed up business processes, improve customer relationships, and reduce monotonous tasks. Let us take a look at the top examples of NLP applications and how they could provide crucial advantages to businesses in different sectors.

• Speech Recognition 

The examples of Siri and Alexa provide a clear impression of the growing popularity of speech recognition. As a matter of fact, it is one of the top NLP applications that could transform user experiences. Voice recognition technology is not new, as scientists have been working on such solutions for decades. However, NLP has enabled better prospects for significant developments in speech recognition. Natural language processing has enabled the development of multiple speech recognition software systems for decoding the human voice.

The applications of speech recognition have proved successful in hands-free computing, video games, home automation, and virtual assistance. Interestingly, the method also helps to replace other forms of input, such as clicking, typing, or selecting text. Speech recognition is one of the answers to “What are the common applications of NLP?” as it has become an important addition to various products. Voice assistants like Google Assistant, Siri, or Alexa use NLP to provide seamless conversations between humans and machines.

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• Information Extraction 

Natural language processing could also serve as the ideal component for information extraction systems. Information extraction refers to the automatic extraction of structured data from semi-structured or unstructured machine language. The examples of natural language processing for information extraction could point to full-text extraction of internet content through entity extraction, clustering, fact extraction, relationship extraction, or text categorization.   

Such types of NLP applications in AI could help businesses utilize every piece of information at their disposal. Most businesses take product decisions according to information from social media, the news, and on the internet. However, it is important to note the difficulties in obtaining structured and valuable information from such sources. You can find such content in the form of images, infographics, and text. Natural language processing can help in using this content as input for analysis and extraction of relevant information in desired formats for the decision-making process.

• Text Classification 

Text classification or text categorization is one of the sub-tasks involved in information extraction. It is one of the best NLP applications that helps you understand the potential of NLP. Text classification involves labeling of sentences, text reports, paragraphs, and other variants of unstructured text. The text classification applications of NLP could support use cases of natural language inference and sentiment analysis. Businesses can tap into the potential of text classification NLP solutions for improving different processes.

You can use text classification applications of natural language processing for sorting incoming messages. Appropriate classification of incoming messages could help in providing timely alerts about customer requests and promotional messages. In addition, text classification could also emerge as a powerful tool for improving lead scoring. On top of it, NLP could also help in classifying urgent user service requests according to priority.

Excited to learn about ChatGPT and other AI use cases? Enroll now in ChatGPT Fundamentals Course

• Semantic Search

One of the most noticeable applications of natural language processing is semantic search. You must have noticed how the content consumption behavior of internet users has changed in the last decade. People could easily search for information on their smartphones and find easy answers to complex questions within seconds. 

How can natural language processing tools improve search capabilities? You must notice that the accuracy of search results has been questioned in recent times. Therefore, the best search engines use natural language processing technology for delivering ‘smart’ search results.

The ‘smart’ search results imply that search engines look for semantics in user queries. Natural language processing ensures that search engines provide the information sought by users rather than forcing them to browse through all the results. Semantic search engines could be accessible through browsers, e-commerce platforms, and smartphones. In addition, mobile applications, as well as corporate systems such as CRMs, could also utilize NLP for facilitating semantic search.

• Market Intelligence 

Marketing professionals can also leverage NLP to search for people who are most likely to purchase from the brand. You might wonder about questions like “What are the common applications of NLP?” and their relevance for market intelligence. The behavior of users on the internet, alongside activity on social media pages or search engine queries, could provide massive volumes of unstructured customer data. Marketing professionals could use NLP to learn more insights about their customers with each interaction.

You can find examples of NLP applications in AI for marketing intelligence with Seamless AI. It can help in finding the contact information of leads. On the other hand, you can also choose a service known as ML Analyzer, which offers text summarization benefits.

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• Email Filters 

The most popular example of email filters would obviously draw attention to the spam filter. Spam is responsible for around 85% of the global email traffic, thereby implying the necessity of email filters. How can the applications of NLP serve email filters? You must have noticed how email filters have evolved over the years to help you maintain an organized inbox. For example, Gmail offers different categories for organizing your emails, such as primary, promotions, updates, and social.

Natural language processing works as the core element of these email filters. When you receive emails in the inbox, NLP techniques such as keyword extraction and text classification help in scanning the emails automatically.     

• Language Translation 

The list of NLP applications would also point towards language translation. It is important to note that language translation is one of the top NLP applications as it can bridge the gaps in communication. Online translators utilize NLP for translating language with better accuracy and ensuring grammatical correctness.

It is important to note that different languages do not support direct translation and could have different arrangements of sentence structure. Natural language processing could remove the barriers to semantic understanding of different languages to facilitate effective translation.

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• Chatbots

You could not compile a list of best NLP applications without mentioning chatbots. As a matter of fact, NLP has emerged as a formidable tool for creating semantic chatbots. The primary objective of using NLP for chatbots is to ensure accurate and relevant responses to customer queries.

Chatbots are accessible through the web or apps and messenger platforms. Businesses can leverage the power of NLP in chatbots for personalizing their communications with customers. Subsequently, the improvement in customer service could lead to better opportunities for ensuring customer satisfaction. 

• Sentiment Analysis 

Another noticeable application of natural language processing is sentiment analysis. It is the ideal answer to “What are the common applications of NLP?” and focuses on evaluation of people’s opinions of a specific subject or product. Businesses can use sentiment analysis to find out how their customers feel about their products and services. In addition, it is an effective tool for leveraging NLP to extract insights from data submitted by users according to their will.

• Auto-correction 

The applications of NLP could also point to auto-correction, which helps in correcting errors in text. It works by flagging incorrect words which are not discovered in the dictionary. Subsequently, NLP makes the necessary edits in the typed word and compares the words to a dictionary. The NLP algorithm would help them calculate probabilities and identify the world that would most likely find its place in the final word.

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Final Words

The outline of the top NLP applications showcases the revolutionary potential of natural language processing. It is an important subdomain of AI focused on enabling semantic communication between humans and machines. NLP implies that computers should not only understand the words in text or speech data but also the context and emotions behind them.

Natural language processing powers some of the most crucial functions, such as speech recognition, text classification, sentiment analysis, and language translation. Learn more about the fundamentals of natural language processing and how it works with comprehensive training courses now.

The post Top 10 Applications of Natural Language Processing (NLP) appeared first on 101 Blockchains.

You might be seeking layer 0 blockchains as alternatives to layer 1 blockchain networks. The adoption of layer 1 networks has been increasing, thereby leading to a rise in value of native cryptocurrencies associated with the project. Let us learn more about the importance of layer 0 blockchain networks amidst the increasing hype around layer 1 blockchains.

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What is the Problem with Layer 1 Blockchains?

The first step to learn layer 0 blockchain fundamentals involves a review of problems with layer 1 blockchains. It is important to note that the demand for dApps has been increasing and would lead to increased capital flow into the blockchain space for supporting development. The growing demand for layer 1 blockchains as infrastructures for dApps and web3 development has uncovered the pain points associated with layer 1 networks. For example, they would struggle to meet the requirements of developers alongside end users with contradictory views regarding the balance between scalability, decentralization, and security.

The necessity of blockchain layer 0 solutions emerges from the setbacks of layer 1 networks. One of the first limitations of layer 1 networks points to monolithic architecture. It implies that a single blockchain network serves as the stage for functioning of data availability, execution, and consensus layers.

The stacked design creates pressure on the system, thereby leading blockchains to compromise one of the elements to achieve the other two. For example, layer 1 blockchain networks would have to compromise on scalability to achieve decentralization or compromise with decentralization to ensure security. 

Another notable reason to focus on layer 0 network alternatives is the lack of control over underlying architecture. One of the prominent setbacks of underlying infrastructure for dApps developers points to the rising gas fees on Ethereum network. As a result, it could lead to a rise in costs for using dApps on Ethereum.

Decentralized app developers also have to make compromises in the design of their dApps to ensure compatibility with layer 1 networks. It would also present limitations on the ability to explore different consensus mechanisms alongside experimenting with different token incentive models. 

You can notice that the blockchain and web3 ecosystem relies prominently on layer 1 blockchains. On top of it, you could also notice the problems with tradeoffs due to the scalability trilemma in layer 1 blockchains. Therefore, it is important to learn about the alternatives to layer 1 blockchains.

What are Layer 0 Blockchains?

The growing interest in layer 0 blockchain projects has fuelled the demand for learning about layer 0 blockchain fundamentals. Layer 0 blockchain networks represent the base infrastructure and serve as the most crucial component to support blockchain networks and dApps in achieving unlimited scalability. At the same time, layer 0 blockchains could help in ensuring higher levels of censorship resistance and decentralization.

You might be eager to learn about layer 0 blockchain examples and their implications for scalability in blockchain networks. Layer 0 points at the type of protocol that can help developers in launching multiple layer 1 blockchain networks. Each layer 1 network can be tailored for achieving a specific task and could address the different dimensions in scalability trilemma.

In addition, the layer 1 networks associated with layer 0 network could also exhibit interoperability. It ensures that users can capitalize on the functionalities of multiple blockchain networks with the experience of using one network. Layer 0 blockchain networks have been powered with software development tool kits or SDKs.

The SDKs can help developers launch their own blockchain networks or layer 1 chains or sidechains, which stay connected with the layer 0 chain while working independently. You can think of layer 0 chains as the infrastructure to launch new blockchains, such as Bitcoin or the Ethereum network. 

Some of the popular layer 0 blockchain networks include Polkadot and Cosmos. Every layer 0 blockchain features a unique implementation and serves distinct approaches for helping developers launch their own blockchains.

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Importance of Layer 0 Blockchains

The next important aspect you must learn in a layer 0 blockchain guide would point at the importance of layer 0 blockchains. Layer 0 blockchain networks have the capabilities for resolving three crucial problems of web3 development such as scalability, interoperability, and flexibility. Here are some of the notable factors that prove the importance of layer 0 blockchains.

• Scalability Trilemma

One of the foremost reasons to learn layer 0 blockchain fundamentals is the scalability trilemma. It refers to the trade-offs between security, decentralization, and scalability during the design of a blockchain and defining the on-chain rules. Centralization leads to increased speed and a reduction in censorship resistance and security. Decentralization ensures a reduction in speed alongside increasing security and censorship resistance. Scalability trilemma implies that you cannot achieve perfect decentralization without compromises in scalability.

The scalability trilemma is a prominent concern for monolithic blockchain architecture, where a single network manages all critical functions. As a result, it could increase the chances of congestion alongside creating difficulties for scalability. The functionalities of a blockchain layer 0 design ensure better scalability by increasing the transaction throughput. The transaction throughput points to the total number of transactions that a network can manage simultaneously.

• Interoperability 

Interoperability is the capability of computer systems with different programming for interpretation and exchange of information without friction. One of the prominent highlights of a layer 0 network is the facility of interoperability between different layer 1 blockchain.

You can assume that layer 1 blockchains as large cities that should be connected to each other through bridges for economic activity. Layer 0 blockchain networks could use different iterations of cross-chain transfer protocol to enable the transfer of tokens. 

Layer 0 blockchains could facilitate transfer of data from layer 1 to layer 0 and layer 0 main chain to layer 1 networks. In addition, layer 0 networks also facilitate interoperability through the flexible data and token transfer between layer 1 blockchains. Cosmos is one of the popular layer 0 blockchain examples for understanding how layer 0 empowers interoperability.

Cosmos utilizes the Inter-Blockchain Communication or IBC protocol for classifying layer 1 networks as ‘zones.’ The ‘zones’ could communicate with each other and transfer value by utilizing the ‘HUB’ main chain as a central intermediary and checkpoint. Similarly, Horizen also utilizes the Cross-Chain Transfer Protocol or CCTP for helping sidechains in communicating and transferring tokens to the main chain.

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Working Mechanism of Layer 0 Blockchains 

The most important highlight in a guide on layer 0 blockchains points at their working mechanism. You can understand the working mechanism of layer 0 blockchain projects with a review of the important components in layer 0 architecture. The three important components of layer 0 protocols include the main chain, side chains, and a cross-chain transfer protocol. The three-component model for explaining the layer 0 blockchain architecture provides a simple understanding of their working mechanism. Each component in the architecture of a layer 0 protocol serves distinct functionalities.

• Main Chain

The responses to “What is a layer 0 blockchain?” invite attention to the functionality of the main chain. It serves as the primary blockchain or the layer 0 blockchain, which stores all the transaction data from different layer 1 chains.

• Sidechains

Sidechains are independent layer 1 networks that have their own collection of validator nodes and can run their independent consensus mechanisms. The sidechains do not depend on the main chain for security. However, they would share the security of the primary chain as it is the most decentralized and biggest chain. 

Sidechains in a layer 0 network could share security in different ways. For example, users could stake the native token of the layer 0 chain for becoming a validator on a layer 1 network. It implies that users could lose their layer 0 token stake and their layer 1 stake for submitting fraudulent transactions.

On the other hand, layer 1 blockchains could also periodically share their network state, transaction history, and updated record of account balances with layer 0. It helps in keeping a backup with a network with more security for scenarios where the layer 1 networks are compromised.     

• Cross-Chain Transfer Protocol

The next crucial component in the architecture of blockchain layer 0 networks is the cross-chain transfer protocol. It is important to note that the cross-chain transfer protocol could appear in different forms in different layer 0 networks. The primary objective of the cross-chain transfer protocol is the flexibility for enabling transfer of tokens and different forms of data between blockchains in a completely secure and trustless manner. In the case of Cosmos, the cross-chain transfer protocol is Cosmos IBC. Avalanche uses the Avalanche Warp Messaging protocol, while Polkadot uses the Polkadot XCMP protocol.

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What are the Advantages of Layer 0 Blockchains?

Layer 0 blockchains have emerged as a more scalable and efficient alternative in the blockchain ecosystem. They can help in managing large volumes of transactions alongside serving the advantages of better privacy and security. As a result, everyone wants to learn layer 0 blockchain basics and how they support the existing blockchain ecosystem. Here are some of the most noticeable advantages of layer 0 blockchains over traditional blockchain networks.

• Better Performance and Speed

The first entry among the important benefits of layer 0 blockchains points to the improvements in performance and speed. Layer 0 blockchains utilize different technologies such as parallel processing and sharding for optimization of data storage and consensus processes for increasing transaction throughput. 

• Improved Privacy and Security 

Layer 0 blockchains could offer better privacy and security by using cryptographic technologies such as multi-party computation and zero-knowledge proofs. The use of cryptographic technologies helps in avoiding the concerns of fraud and security breaches. As a result, layer 0 blockchain projects are suitable for enterprise applications, which demand better data privacy and security.

• Cost Reduction 

The value advantages of layer 0 blockchain networks in terms of cost point to improvements in performance and scalability. As a result, users could find lower transaction fees alongside reductions in other costs related to the use of blockchain networks. It helps ensure the accessibility of blockchain-based solutions for businesses and individuals. 

• Seamless Integration with Different Blockchains

The best highlight in a layer 0 blockchain guide would point to the facility of seamless integration with different blockchains. Layer 0 blockchains provide exceptional ease for integration of decentralized applications and services with legacy systems. The flexibility of seamless integration with other blockchains enables better growth and adoption of blockchain technology.

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Conclusion 

Layer 0 blockchains are the beginning of a new chapter in the world of blockchain and web3. The primary objective of a layer 0 network revolves around addressing the issues pertaining to compatibility and size of transactions. Some of the prominent advantages of layer 0 blockchains point to conclusive improvements in scalability of blockchain networks.

Popular examples of layer 0 blockchains, such as Cosmos and Polkadot, show that the blockchain ecosystem has embraced layer 0 networks. However, it is also important to learn about the working mechanisms of layer 0 networks along with the important components in the layer 0 architecture. Learn more about the advantages of layer 0 networks to identify their importance for web3 development. Dive deeper into the details of some popular examples to discover the potential of layer 0 blockchains right now.

*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!

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