Account Abstraction on Ethereum: A Deep Dive into the ERC-4337 Standard

Karolina

14 Nov 2023
Account Abstraction on Ethereum: A Deep Dive into the ERC-4337 Standard

Ethereum, since its inception, has stood at the forefront of blockchain innovation, introducing concepts that have revolutionized the industry. At its core, Ethereum is not just a cryptocurrency but a platform for decentralized applications (dApps), powered by its native token, Ether. Among the numerous advancements in the Ethereum ecosystem, one concept that is gaining momentum is Account Abstraction. This concept, particularly highlighted in the ERC-4337 standard, presents a paradigm shift in how accounts are managed on the Ethereum blockchain, promising enhanced security and a more seamless user experience.

Account Abstraction, though a technical concept, has far-reaching implications for everyday users, developers, and the broader Ethereum community. It represents a move towards a more flexible and user-friendly blockchain, addressing some of the challenges and limitations of the current account model. As we delve into this topic, we will uncover the intricacies of Account Abstraction and the pivotal role of the ERC-4337 standard in reshaping the Ethereum experience.

Understanding Account Abstraction

Ethereum primarily uses two types of accounts: Externally Owned Accounts (EOAs) and Contract Accounts. EOAs are controlled by private keys and are typically used by individuals to send transactions or interact with smart contracts. In contrast, Contract Accounts are governed by their contract code and are used to deploy and run smart contracts.

The traditional Ethereum account model, centered around EOAs, has its limitations. It often leads to complex management of private keys and lacks flexibility in transaction execution. This is where Account Abstraction comes into play. It proposes a unified account model, blurring the lines between EOAs and Contract Accounts. Under Account Abstraction, user accounts would essentially function like smart contracts, enabling more complex and secure transaction rules beyond the simple private key model.

ERC-4337 Standard: An Overview

The ERC-4337 standard represents a significant milestone in Ethereum's ongoing evolution, offering a novel approach to implementing Account Abstraction without necessitating extensive changes to the core Ethereum protocol. This standard introduces a framework that enables users to experience the benefits of Account Abstraction, bringing enhanced flexibility and security to account management on the Ethereum blockchain.

The Core Concept of ERC-4337

At its heart, the ERC-4337 standard is about enabling accounts on Ethereum to behave more like smart contracts. This shift allows for more sophisticated rules around transaction execution, which traditionally could only be applied to Contract Accounts. The key innovation of ERC-4337 is the introduction of a new entity known as the 'User Operation.' These are bundles of transactions that users sign, which are then executed by a new type of account called a 'Bundler.' Bundlers are responsible for submitting these operations to the blockchain, ensuring that they conform to the user's predefined rules.

Technical Mechanisms

ERC-4337 operates through a smart contract, known as the 'EntryPoint,' which acts as a hub for User Operations. Users send their signed operations to this contract, which then delegates the execution to the appropriate smart contract wallets. This process is facilitated by relayers who, in exchange for a fee, submit these operations to the EntryPoint. The beauty of this setup is that it does not require any changes to miners' or validators' operations in the Ethereum network, making it a less intrusive yet effective solution for Account Abstraction.

Benefits of ERC-4337

The introduction of the ERC-4337 standard brings several key advantages:

Enhanced Security: By allowing accounts to set more complex rules for transaction execution, ERC-4337 provides an additional layer of security. This includes capabilities like multi-signature verification and automated checks before transaction execution.

Improved User Experience: With ERC-4337, users can enjoy a more streamlined and flexible transaction process. For instance, they can execute batch transactions, set up recurring payments, or integrate more sophisticated wallet recovery options.

Greater Flexibility: Developers can create more innovative dApps with complex transaction requirements, thanks to the flexibility offered by ERC-4337. This could lead to new use cases and applications on the Ethereum blockchain.

Implementing Account Abstraction with ERC-4337

The implementation of Account Abstraction using the ERC-4337 standard marks a pivotal moment in Ethereum's development. This process involves several critical steps and considerations for both developers and users.

Implementation

  1. Smart Contract Wallet Deployment: The first step involves deploying a smart contract wallet compatible with the ERC-4337 standard. This wallet will manage the user's assets and execute transactions based on predefined rules.
  2. Setting Up User Operations: Users need to define their transaction rules and parameters within these smart contract wallets, known as User Operations.
  3. Utilizing Relayers and Bundlers: To execute transactions, users interact with relayers who submit their operations to the EntryPoint contract. Bundlers then include these operations in the blockchain.

Considerations for Developers and Users

  • Security: While ERC-4337 enhances security, developers must ensure that the smart contract wallets and User Operations are robust against potential vulnerabilities.
  • User Experience: Developers should focus on creating intuitive interfaces for setting up and managing User Operations, making the process user-friendly.
  • Cost Implications: Implementing ERC-4337 may involve additional costs, such as fees for relayers. Users and developers need to consider these financial implications.

Impact on the Ethereum Ecosystem

Increased Security and Trust: With more robust account security features, Ethereum can attract a broader audience, including those previously wary of blockchain's security aspects.

Enhanced User Accessibility: Simplified transaction processes and user-friendly interfaces will lower the barrier to entry, potentially leading to increased adoption of Ethereum-based applications.

Innovation in dApps Development: Developers will have more freedom to experiment with complex transaction mechanisms, leading to innovative dApps that could redefine the blockchain landscape.

Long-Term Implications

Standardization and Interoperability: Account Abstraction could become a standard feature in future blockchain platforms, enhancing interoperability across different networks.

Influence on Other Blockchains: Ethereum's move towards Account Abstraction may inspire similar developments in other blockchain ecosystems, fostering a new wave of blockchain innovation.

Conclusion

The introduction of Account Abstraction, particularly through the ERC-4337 standard, is a landmark development in Ethereum's history. It represents a significant stride towards a more flexible, secure, and user-friendly blockchain platform. As we venture into this new era, the potential of Ethereum to revolutionize not just finance but various sectors of the economy becomes increasingly evident. The ERC-4337 standard is not just an enhancement of Ethereum's technical capabilities but a step towards realizing the broader vision of blockchain technology - a more open, secure, and accessible digital future for all.

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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?

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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?

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