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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

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.

Hybrid Web3 Architecture

Comparing Web3 Backend APIs vs. Blockchain-Based Logic

FeatureWeb3 Backend (API)Blockchain (Smart Contracts)
Change ManagementCan be updated easilyEvery change requires a new contract deployment
CostTraditional hosting feesHigh gas fees + costly audits
Data StorageCan store large datasetsLimited and expensive storage
SecuritySecure but relies on centralized infrastructureFully decentralized & trustless
PerformanceFast response timesLimited 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!

Tagi

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

Follow us to stay updated!

Tagi

AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?
Table of Contents

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

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