ERC-1400 vs ERC-3643 – Comparing Token Standards

Miłosz Mach

08 Jan 2024
ERC-1400 vs ERC-3643 – Comparing Token Standards

Imagine a world where the complexities of finance and the ingenuity of blockchain technology converge harmoniously. ERC-1400, a standard that has established rules around securities offerings, and on the other side, ERC-3643 - versatile in broadening technology utilization and tokenization horizons. They are keystones of modern funds management, each with its unique flair and profound implications. As we navigate their nuances, we’ll shed light on their roles, differences, and analogies.

Understanding ERC-1400


Origins and Purpose of ERC-1400

ERC-1400 introduces a standard for security tokens on the Ethereum blockchain. Security tokens, which illustrate digital forms of traditional investment contracts like stocks, bonds, and company shares require a token standard capable of navigating this intricate regulatory environment. The intent was to bring clarity and purified structure to the tokenization of securities, ensuring the process is compliant with existing laws and regulations. Such instruments, in particular, demand a future-thinking approach sticking to the thorough financial legal framework and its progressive traits.

Key Features of ERC-1400

ERC-1400 is characterized by several features that serve the specific needs of security tokens:

Compliance with Financial Regulations

Control and Transparency

Granular Oversight of Transactions empowers issuers with monitored access to token operations, essential for financial compliance and investor trust. The standard enables rules enforcement and qualifications for each transfer. That means all movements of the token adhere to platform operational criteria. The level of legitimacy provided by ERC-1400 supports the credibility of security token offerings, both in the eyes of regulators and institutional investors.

Comparative Analysis: ERC-1400 vs ERC-3643

ERC-1400 and ERC-3643 cater to distinct needs and scenarios. This analysis aims to contrast features, applications, and the different problems they address.

Wondering what is ERC-3643 all about and how it works? Click to learn more in our latest article.

Table 1: Core Characteristics and Use Cases

Table 2: Technical Features and Institutional Adoption

Unifying the Standards

Before exploring the differing attributes, it's important to recognize the familiar ground shared by ERC-1400 and ERC-3643:

Regulatory Compliance Focus

  • Common Goal for Ordinances Implementation: Both standards supervise legal regulatory alignment;
  • Bridging Traditional Finance and Blockchain: They facilitate wider use of blockchain in traditional economic sectors.

Modular Architecture

  • Flexibility and Customization: The solutions inherent in ERC-1400 and ERC-3643 allow developers to influence certain details of the token or adapt features to meet specific needs, from top-down legislation to highly advanced technological refinements;
  • Adaptability for Future Enhancements: This is not only about meeting current essentials but also about paving the way for future enhancement. As per their modular structure, changes can be made without the need for system overhauling, thereby future-proofing the token standards.

Distinctive Features and Differences

While ERC-1400 and ERC-3643 allocate these foundational similarities, they diverge in their purpose, scope, and technical implementations.

ERC-1400: Specialized for Security Tokens

Targeted Use Case

  • ERC-1400 serves the domain of security tokens, which are digital versions of aforementioned stocks or bonds. This standard addresses applicable and potential regulatory challenges associated with their tokenization.

Investor Protection and Financial Compliance

  • It commits to investor protection guaranteeing detailed party verification, and the proper maintenance of holders' rights.

ERC-3643: Broader Scope for Asset Tokenization

Versatile Tokenization

  • Unlike ERC-1400, ERC-3643 accommodates a wide range of assets beyond securities.

Reinforced Token Control 

  • Advanced token behavior patterns provide issuers with a higher degree of customization and control;
  • Optimized gas cost and streamlined contract processes also make it well-suited for high transaction volume and large-scale applications.

Conclusion

The comparative journey through ERC-1400 vs ERC-3643 reveals a harmonious standards coexistence. Together, despite a different purpose, they reflect the dynamic nature of blockchain technology. ERC-1400 and ERC-3643 shape the future of technology, and accordingly, with their introduction, the community has been equipped with a solid fundament to actively participate in any asset digitization.

If you are interested in utilizing ERC-3643 or other blockchain-based solutions for your project, please reach out to contact@nextrope.com

FAQ

What are the key features of ERC-1400?

  • ERC-1400 embeds legal governance into the token lifecycle, ensuring compliance with securities regulations, and provides granular oversight of transactions, enhancing control and transparency.

How do ERC-1400 and ERC-3643 unify standards?

  • Both standards focus on regulatory compliance and bridging traditional finance with blockchain technology. They feature modular architecture, offering flexibility for customization and adaptability for future enhancements.

What is the purpose of ERC-1400 and ERC-3643?

  • ERC-1400 specializes in security tokens, addressing regulatory challenges and ensuring investor protection. ERC-3643 has a broader scope for asset tokenization, providing advanced token control and optimization for high transaction volume applications.

Tagi

Most viewed


Never miss a story

Stay updated about Nextrope news as it happens.

You are subscribed

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!

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
Nextrope Logo

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