In today's increasingly digital world, the demand for secure and dependable cryptographic systems is at an all-time high. Blockchain technology has emerged as a revolutionary force in many industries, thanks to its decentralized and unchangeable characteristics. However, existing cryptographic algorithms face significant security threats from the advancing quantum computer technology. This article will discuss the significance of post-quantum cryptography in protecting blockchain networks against the impending quantum challenges.

Understanding the Quantum Threat

Quantum computers, employing quantum mechanics principles, promise unprecedented computational capabilities that may render existing cryptographic algorithms ineffective. Conventional encryption techniques, such as RSA and ECC (Elliptic Curve Cryptography), depend on the complexity of specific mathematical problems for security. Quantum computers, however, hold the potential to solve these problems exponentially faster, consequently dismantling the cryptographic foundation that supports blockchain networks.

Various risks are associated with quantum computers' impact on blockchain networks. The most prominent risk includes compromising the security of digital assets managed within blockchain systems. Transactions, smart contracts, and private keys that depend on cryptographic algorithms might become susceptible to quantum computer attacks. As quantum technology progresses, adversaries may decrypt encrypted information, tamper with transactions or counterfeit digital signatures – leading to severe financial and reputational damage for those relying on blockchain networks.

Additionally, blockchain's decentralized and transparent nature makes it particularly prone to quantum attacks. Given that blockchain transactions are publicly accessible, a quantum computer-equipped attacker could retroactively decrypt past transactions. This undermines the core principles of immutability and trust that underpin blockchain technology.

To address this urgent and critical challenge posed by the quantum threat, it's vital to take a proactive approach. Incorporating post-quantum cryptography into blockchain systems is crucial for maintaining long-term security and sustainability of these networks. By utilizing cryptographic algorithms that can withstand quantum computer attacks, blockchain networks can preserve data confidentiality, integrity, and the authenticity of transactions and digital assets. Even in light of quantum advancements.

The subsequent sections of this article will investigate the practicality of implementing post-quantum cryptography in blockchain systems. We will explore specific solutions, evaluate their performance implications, and emphasize the initiatives being taken towards standardization and compatibility. Through this examination, we seek to contribute to the comprehensive understanding and adoption of post-quantum cryptography as a vital defense against the quantum threat within the blockchain environment.

Exploring the Viability of Post-Quantum Cryptography in Blockchain

Implementing post-quantum cryptography within blockchain systems is a multifaceted effort demanding a thorough examination of numerous aspects. With the impending emergence of quantum computers, shifting to post-quantum cryptographic algorithms entails its own set of challenges. This section delves into the practicality of incorporating post-quantum cryptography into blockchain and scrutinizes the advancements in this domain.

Investigations and Progress in Algorithms

Intensive investigations are being undertaken by cryptographic researchers and organizations to explore post-quantum cryptographic algorithms that can withstand attacks from quantum computers. Lattice-based, code-based, and multivariate-based schemes are some examples that aim to preserve security even against quantum adversaries. Meticulous research and evaluations are performed to assess the mathematical underpinnings, security attributes, and practicality of these algorithms for actual implementation.

Concerns about Performance

A significant hurdle while adopting post-quantum cryptography in blockchain lies in the performance costs arising from these novel algorithms. Frequently, post-quantum cryptographic algorithms demand higher computational power and memory compared to conventional cryptographic algorithms. Such heightened computational requirements can influence blockchain networks' efficiency and scalability, possibly altering transaction throughput and consensus mechanisms. Nevertheless, ongoing investigations and optimization endeavors seek to address these performance issues, making post-quantum cryptography more practical for blockchain systems.

Integration with Current Blockchain Protocols

Modifications and revisions to existing protocols may be essential for integrating post-quantum cryptography into blockchain networks. Blockchain platforms like Ethereum proactively investigate incorporating post-quantum cryptographic algorithms through initiatives such as EIP-2938. The objectives include ensuring congruity and consensus among network users while establishing a trajectory towards quantum-resistant security.

The Role of Standardization and Interoperability

Standardization holds paramount importance when adopting and executing post-quantum cryptography within blockchain systems. Institutions like the National Institute of Standards and Technology have introduced competitions and evaluations to pinpoint and standardize post-quantum cryptographic algorithms. This standardization process confirms interoperability, cultivates trust, and facilitates widespread utilization of these algorithms across varied blockchain networks.

Test Implementations and Real-Life Evaluation

Multiple pilot projects and initiatives are launched to gauge the feasibility and practicability of p-q cryptography in actual blockchain settings. These implementations aid in pinpointing potential difficulties, performance consequences, and security considerations associated with merging post-quantum cryptography into existing blockchain infrastructures. The knowledge acquired from these pilot projects contributes to refining and enhancing post-quantum cryptographic algorithms for appropriateness within blockchain networks.

Evaluating Solutions for Post Quantum Cryptography Signature Verification

Hash-Based Signatures

Signature schemes based on hash functions, such as Lamport and Winternitz one-time signature schemes, provide post-quantum security due to the computational difficulty of hash functions. Although these schemes offer robust security assurances against quantum attacks, their large signature sizes make them less practical for bandwidth-restricted blockchain networks. Hash-based signatures are appropriate for situations where signature size is not a major concern, like in offline or low-bandwidth contexts.

Lattice-Based Signatures

BLISS and Dilithium schemes are examples of lattice-based signature schemes that leverage the difficulty of specific mathematical problems on lattices to ensure post-quantum security. These schemes have smaller signature sizes than hash-based signatures, rendering them more appropriate for resource-limited blockchain networks. Lattice-based signatures strike a good balance between security and efficiency; however, lattice operations' complexity can affect their performance.

Code-Based Signatures

Error-correcting codes are utilized in code-based signature schemes like McEliece and Niederreiter to provide quantum attack resistance. These schemes have small signature sizes and rapid signature generation capabilities, making them appealing for high-throughput blockchain systems. Nevertheless, code-based signatures may have larger public key sizes compared to other p-q cryptography signature schemes. This can influence storage requirements.

Multivariate-Based Signatures

Rainbow and HFE are multivariate-based signature schemes that rely on the difficulty of solving multivariate polynomial equation systems for post-quantum security. These schemes provide compact signature sizes and efficient signature verification, making them suitable for resource-limited blockchain networks. However, multivariate-based signatures can be prone to specific attacks, such as the Gröbner basis attack, necessitating cautious parameter selection and security analysis.

Hybrid Approaches

The integration of multiple post-quantum cryptography signature schemes characterizes hybrid approaches to capitalize on their respective benefits and address their shortcomings. A hybrid scheme can, for instance, merge a hash-based signature scheme for initial verification with a lattice-based or code-based signature scheme for additional validation. Hybrid approaches strive to deliver a sturdy and adaptable solution that harmonizes security, efficiency, and compatibility with existing cryptographic infrastructure.

When choosing a post-quantum cryptography signature verification solution for blockchain, it is critical to evaluate factors like security, signature size, computational efficiency, storage requirements, and protocol compatibility. The selection of a particular scheme will be determined by the blockchain network's specific demands and limitations.

It is important to note that it remains a developing field, with ongoing research and progress constantly enhancing signature schemes' efficiency and security. Keeping abreast of the latest developments and seeking advice from cryptographic experts is essential when making informed decisions regarding the adoption and implementation of it signature verification solutions in blockchain systems.

Blockchain developers and organizations can choose suitable post-quantum cryptography signature verification schemes by meticulously evaluating and comparing available options, ensuring robust defense against quantum attacks while maintaining optimal performance and scalability levels.

Moving Towards Standardization and Compatibility in Post-Quantum Cryptography:

The significance of standardization grows, enabling interoperability and compatibility among diverse blockchain networks. The adoption of post-quantum cryptographic algorithms and secure digital communication relies heavily on standardization. In this section, we will explore standardization's importance and the developments made thus far.

Standardization of Post-Quantum Cryptography by NIST

The National Institute of Standards and Technology (NIST) is at the forefront of standardizing post-quantum cryptography.
In 2017, NIST launched a public contest inviting submissions for post-quantum cryptography candidate algorithms across various categories, such as encryption, signature, and key exchange.
This contest seeks to pinpoint and select quantum-resistant algorithms that are efficient, robust, and can be widely implemented across various applications and sectors.
Currently in its final stages, the competition is narrowing down several algorithms for potential post-quantum cryptography standards.

Challenges in Interoperability and Compatibility:

Attaining compatibility and interoperability among different cryptographic algorithms and blockchain networks is a complicated feat.
Current blockchain systems often depend on specific cryptographic protocols and primitives that may not align with post-quantum algorithms.
A seamless shift demands thorough examination of backward compatibility, migration strategies, and consensus from participants.
Collaborative initiatives are essential for creating standards and protocols capable of smoothly integrating post-quantum cryptographic algorithms into existing blockchain networks.

Advantages of Standardization for Blockchain Networks:

The adoption of post-quantum cryptography by blockchain networks brings numerous benefits through standardization.
A common framework for cryptographic operations ensures interoperability, enabling secure communication among various blockchain platforms.
Algorithms undergoing standardization are rigorously assessed by the cryptography community, instilling confidence in their reliability and security.
Additionally, standardized frameworks simplify the integration of new cryptographic technologies and future enhancements.

Expanding Post-Quantum Cryptography to Additional Blockchain Networks:

The implementation of post-quantum cryptography spans beyond any single blockchain network or protocol. To guarantee long-term security and robustness of their systems, multiple blockchain platforms investigate ways to integrate post-quantum cryptographic algorithms as the quantum threat emerges. In this section, we will examine ongoing efforts to introduce post-quantum cryptography to other blockchain networks.

Ethereum and Post-Quantum Cryptography:

As one of the most prevalent blockchain platforms, Ethereum actively investigates the adoption of post-quantum cryptographic algorithms.
The Ethereum Foundation and its community engage in ongoing dialogue and partnerships with experts to evaluate the feasibility and appropriateness of various post-quantum algorithms for Ethereum's infrastructure.
Developing a roadmap for incorporating post-quantum cryptography that considers the potential impact on performance, scalability, and backward compatibility is the ultimate goal.

Other Blockchain Networks:

Outside of Ethereum, additional blockchain networks recognize the value of post-quantum cryptography.
Platforms like Hyperledger, Corda, and Polkadot proactively explore how quantum-resistant algorithms can be integrated into their protocols to counter emerging threats.
Collaborative work focuses on assessing and testing different post-quantum cryptographic solutions within real-world blockchain settings, taking into account factors such as performance, security, and infrastructure compatibility.

By expanding post-quantum cryptography to various blockchain networks, the goal is to construct a more secure and future-proof foundation for decentralized applications and digital asset transactions. Collaboration between standardization organizations, cryptographic experts, and blockchain platforms is vital in achieving

Conclusion

In conclusion, post-quantum cryptography offers a promising solution to address the quantum threat in blockchain. Efforts are underway to develop efficient and secure algorithms for post-quantum signature verification. Standardization and compatibility initiatives are crucial for seamless integration across different blockchain networks. The industry is actively working towards extending pq cryptography to ensure the security of blockchain transactions.

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AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

How does AI speed up UI component creation? A review of techniques and tools
Automated frontend code refactoring – how AI improves code quality
Code review with AI – which tools help analyze code?

Stay tuned to keep up with the latest insights!

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The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Tomasz Dybowski

04 Mar 2025

The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Introduction

Web3 backend development is essential for building scalable, efficient and decentralized applications (dApps) on EVM-compatible blockchains like Ethereum, Polygon, and Base. A robust Web3 backend enables off-chain computations, efficient data management and better security, ensuring seamless interaction between smart contracts, databases and frontend applications.

Unlike traditional Web2 applications that rely entirely on centralized servers, Web3 applications aim to minimize reliance on centralized entities. However, full decentralization isn't always possible or practical, especially when it comes to high-performance requirements, user authentication or storing large datasets. A well-structured backend in Web3 ensures that these limitations are addressed, allowing for a seamless user experience while maintaining decentralization where it matters most.

Furthermore, dApps require efficient backend solutions to handle real-time data processing, reduce latency, and provide smooth user interactions. Without a well-integrated backend, users may experience delays in transactions, inconsistencies in data retrieval, and inefficiencies in accessing decentralized services. Consequently, Web3 backend development is a crucial component in ensuring a balance between decentralization, security, and functionality.

This article explores:

When and why Web3 dApps need a backend
Why not all applications should be fully on-chain
Architecture examples of hybrid dApps
A comparison between APIs and blockchain-based logic

This post kicks off a Web3 backend development series, where we focus on the technical aspects of implementing Web3 backend solutions for decentralized applications.

Why Do Some Web3 Projects Need a Backend?

Web3 applications seek to achieve decentralization, but real-world constraints often necessitate hybrid architectures that include both on-chain and off-chain components. While decentralized smart contracts provide trustless execution, they come with significant limitations, such as high gas fees, slow transaction finality, and the inability to store large amounts of data. A backend helps address these challenges by handling logic and data management more efficiently while still ensuring that core transactions remain secure and verifiable on-chain.

Moreover, Web3 applications must consider user experience. Fully decentralized applications often struggle with slow transaction speeds, which can negatively impact usability. A hybrid backend allows for pre-processing operations off-chain while committing final results to the blockchain. This ensures that users experience fast and responsive interactions without compromising security and transparency.

While decentralization is a core principle of blockchain technology, many dApps still rely on a Web2-style backend for practical reasons:

1. Performance & Scalability in Web3 Backend Development

Smart contracts are expensive to execute and require gas fees for every interaction.
Offloading non-essential computations to a backend reduces costs and improves performance.
Caching and load balancing mechanisms in traditional backends ensure smooth dApp performance and improve response times for dApp users.
Event-driven architectures using tools like Redis or Kafka can help manage asynchronous data processing efficiently.

2. Web3 APIs for Data Storage and Off-Chain Access

Storing large amounts of data on-chain is impractical due to high costs.
APIs allow dApps to store & fetch off-chain data (e.g. user profiles, transaction history).
Decentralized storage solutions like IPFS, Arweave and Filecoin can be used for storing immutable data (e.g. NFT metadata), but a Web2 backend helps with indexing and querying structured data efficiently.

3. Advanced Logic & Data Aggregation in Web3 Backend

Some dApps need complex business logic that is inefficient or impossible to implement in a smart contract.
Backend APIs allow for data aggregation from multiple sources, including oracles (e.g. Chainlink) and off-chain databases.
Middleware solutions like The Graph help in indexing blockchain data efficiently, reducing the need for on-chain computation.

4. User Authentication & Role Management in Web3 dApps

Many applications require user logins, permissions or KYC compliance.
Blockchain does not natively support session-based authentication, requiring a backend for handling this logic.
Tools like Firebase Auth, Auth0 or Web3Auth can be used to integrate seamless authentication for Web3 applications.

5. Cost Optimization with Web3 APIs

Every change in a smart contract requires a new audit, costing tens of thousands of dollars.
By handling logic off-chain where possible, projects can minimize expensive redeployments.
Using layer 2 solutions like Optimism, Arbitrum and zkSync can significantly reduce gas costs.

Web3 Backend Development: Tools and Technologies

A modern Web3 backend integrates multiple tools to handle smart contract interactions, data storage, and security. Understanding these tools is crucial to developing a scalable and efficient backend for dApps. Without the right stack, developers may face inefficiencies, security risks, and scaling challenges that limit the adoption of their Web3 applications.

Unlike traditional backend development, Web3 requires additional considerations, such as decentralized authentication, smart contract integration, and secure data management across both on-chain and off-chain environments.

Here’s an overview of the essential Web3 backend tech stack:

1. API Development for Web3 Backend Services

Node.js is the go-to backend runtime good for Web3 applications due to its asynchronous event-driven architecture.
NestJS is a framework built on top of Node.js, providing modular architecture and TypeScript support for structured backend development.

2. Smart Contract Interaction Libraries for Web3 Backend

Ethers.js and Web3.js are TypeScript/JavaScript libraries used for interacting with Ethereum-compatible blockchains.

3. Database Solutions for Web3 Backend

PostgreSQL: Structured database used for storing off-chain transactional data.
MongoDB: NoSQL database for flexible schema data storage.
Firebase: A set of tools used, among other things, for user authentication.
The Graph: Decentralized indexing protocol used to query blockchain data efficiently.

4. Cloud Services and Hosting for Web3 APIs

AWS Lambda, Google Cloud Functions: Serverless functions that handle smart contract interactions.
Infura, Alchemy, QuickNode: Blockchain RPC node providers offering API access to Ethereum, Polygon and other networks.

When It Doesn't Make Sense to Go Fully On-Chain

Decentralization is valuable, but it comes at a cost. Fully on-chain applications suffer from performance limitations, high costs and slow execution speeds. For many use cases, a hybrid Web3 architecture that utilizes a mix of blockchain-based and off-chain components provides a more scalable and cost-effective solution.

In some cases, forcing full decentralization is unnecessary and inefficient. A hybrid Web3 architecture balances decentralization and practicality by allowing non-essential logic and data storage to be handled off-chain while maintaining trustless and verifiable interactions on-chain.

The key challenge when designing a hybrid Web3 backend is ensuring that off-chain computations remain auditable and transparent. This can be achieved through cryptographic proofs, hash commitments and off-chain data attestations that anchor trust into the blockchain while improving efficiency.

For example, Optimistic Rollups and ZK-Rollups allow computations to happen off-chain while only submitting finalized data to Ethereum, reducing fees and increasing throughput. Similarly, state channels enable fast, low-cost transactions that only require occasional settlement on-chain.

A well-balanced Web3 backend architecture ensures that critical dApp functionalities remain decentralized while offloading resource-intensive tasks to off-chain systems. This makes applications cheaper, faster and more user-friendly while still adhering to blockchain's principles of transparency and security.

Example: NFT-based Game with Off-Chain Logic

Imagine a Web3 game where users buy, trade and battle NFT-based characters. While asset ownership should be on-chain, other elements like:

Game logic (e.g., matchmaking, leaderboard calculations)
User profiles & stats
Off-chain notifications

can be handled off-chain to improve speed and cost-effectiveness.

Architecture Diagram

Below is an example diagram showing how a hybrid Web3 application splits responsibilities between backend and blockchain components.

Comparing Web3 Backend APIs vs. Blockchain-Based Logic

Feature	Web3 Backend (API)	Blockchain (Smart Contracts)
Change Management	Can be updated easily	Every change requires a new contract deployment
Cost	Traditional hosting fees	High gas fees + costly audits
Data Storage	Can store large datasets	Limited and expensive storage
Security	Secure but relies on centralized infrastructure	Fully decentralized & trustless
Performance	Fast response times	Limited by blockchain throughput

Reducing Web3 Costs with AI Smart Contract Audit

One of the biggest pain points in Web3 development is the cost of smart contract audits. Each change to the contract code requires a new audit, often costing tens of thousands of dollars.

To address this issue, Nextrope is developing an AI-powered smart contract auditing tool, which:

Reduces audit costs by automating code analysis.
Speeds up development cycles by catching vulnerabilities early.
Improves security by providing quick feedback.

This AI-powered solution will be a game-changer for the industry, making smart contract development more cost-effective and accessible.

Conclusion

Web3 backend development plays a crucial role in scalable and efficient dApps. While full decentralization is ideal in some cases, many projects benefit from a hybrid architecture, where off-chain components optimize performance, reduce costs and improve user experience.

In future posts in this Web3 backend series, we’ll explore specific implementation details, including:

How to design a Web3 API for dApps
Best practices for integrating backend services
Security challenges and solutions

Stay tuned for the next article in this series!

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Blockchain Development

Blockchain Solutions

Products

Understanding the Quantum Threat

Exploring the Viability of Post-Quantum Cryptography in Blockchain

Investigations and Progress in Algorithms

Concerns about Performance

Integration with Current Blockchain Protocols

The Role of Standardization and Interoperability

Test Implementations and Real-Life Evaluation

Evaluating Solutions for Post Quantum Cryptography Signature Verification

Hash-Based Signatures

Lattice-Based Signatures

Code-Based Signatures

Multivariate-Based Signatures

Hybrid Approaches

Moving Towards Standardization and Compatibility in Post-Quantum Cryptography:

Expanding Post-Quantum Cryptography to Additional Blockchain Networks:

Conclusion

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AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

AI in Frontend Automation – How It Helps with Code Refactoring

How AI in Frontend Automation Speeds Up UI Creation

AI in Code Analysis and Understanding

AI in Frontend Automation – Translations and Error Detection

Summary

The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Introduction

Why Do Some Web3 Projects Need a Backend?

1. Performance & Scalability in Web3 Backend Development

2. Web3 APIs for Data Storage and Off-Chain Access

3. Advanced Logic & Data Aggregation in Web3 Backend

4. User Authentication & Role Management in Web3 dApps

5. Cost Optimization with Web3 APIs

Web3 Backend Development: Tools and Technologies

1. API Development for Web3 Backend Services

2. Smart Contract Interaction Libraries for Web3 Backend

3. Database Solutions for Web3 Backend

4. Cloud Services and Hosting for Web3 APIs

When It Doesn't Make Sense to Go Fully On-Chain

Example: NFT-based Game with Off-Chain Logic

Architecture Diagram

Comparing Web3 Backend APIs vs. Blockchain-Based Logic

Reducing Web3 Costs with AI Smart Contract Audit

Conclusion