ERC-3643: Revolutionizing or Adding Complexity?

Miłosz Mach

16 Jan 2024
ERC-3643: Revolutionizing or Adding Complexity?

The community's involvement in the research and development of blockchain technology is once again challenging traditional paradigms. As we have previously observed, the tokenization of Real World Assets (RWAs) has faced a multitude of challenges. ERC-3643 approaches the legal sphere with flexibility and the utmost care, facilitating the proficient integration and use of blockchain infrastructure for assets of various kinds. In this article, we will explore the essence and advantages of this standard, along with potential complexities that may be lurking.

ERC-3643 and Overall Considerations

Trust

Transfers of ERC-20 tokens typically follow a specific process on Ethereum Virtual Machine (EVM) blockchains, adhering to the standard ERC-20 implementation. This process includes the initiation, approval, and eventual transfer of the tokens. Normally, KYC (Know Your Customer) and AML (Anti-Money Laundering) checks require integration with external APIs.

ERC-3643 uses a model that necessitates predefined criteria before transfer execution. The standard integrates ONCHAINID, which acts as a smart contract for a digital identity management system. It enables third-party-issued identification certificates and KYC or AML service providers to interact and enforce legislative requirements on the tokens. Linking an investor's wallet to an ONCHAINID represents a significant shift in the security tokens market. Issuers can recover the token balance using the 'recovery' function when necessary. This process involves comparing and validating information with associated off-chain databases.

Usability

ERC-3643's design acknowledges the global scope of blockchain applications and caters to cross-jurisdictional landscapes through modular design. Its architecture features transaction limits, investor qualifications, and geographical restrictions checks. It communicates with existing EVM-compatible systems or ERC-20-based tokens. The strong emphasis on regulations has some implications for investor confidence. Institutional bodies prioritize legal safety and risk management and most likely ERC-3643 fits both rigorous financial policies and a string of assets tokenization demands. It allows for the implementation of multi-level permissions and roles within its token ecosystem. This hierarchical structure adds an extra layer of security, as it controls who can execute specific actions within the token’s life cycle.

ERC-3643 and Artificial Intelligence

The convergence of blockchain technology and Artificial Intelligence (AI) tools heralds a groundbreaking synergy, poised to unlock new capabilities. This triad of technologies intertwines to create a more intelligent, efficient, and secure ecosystem. By integrating AI algorithms with ERC-3643's smart contracts, there is a significant leap in automated decision-making capabilities. AI can analyze vast amounts of data to make informed decisions about token transactions, ensuring compliance, and optimizing operational efficiency. AI's ability to process and analyze complex datasets simplifies intricate processes involved in token management. It enables more nuanced and sophisticated handling of ERC-3643 tokens, catering to a variety of use cases without adding operational complexity. The integration of AI tools can optimize transactional processes within the ERC-3643 framework, enhancing speed and reducing costs.

Closer Outlook

Table 1: Advantages of ERC-3643

Table 2: Potential Challenges in Front of ERC-3643

Wondering how this standard performs against other protocols? We have previously compared the ERC-1400 and ERC-3643 capabilities.

Conclusion

By facilitating a secure and compliant environment for tokenization, ERC-3643 encourages innovation in the digital asset space. It seeks to evolve alongside changing global regulatory jurisdictions. Further enhancements may include more sophisticated mechanisms. Undoubtedly, the TREX protocol remains at the forefront of blockchain technology adaptability, not only limited to intangible assets like intellectual property and patents. From agriculture to entertainment, becoming more deeply integrated into DeFi ecosystems, offering novel investment opportunities. The standard establishes a foundation for growth, although it appears innovative, flexible, and compliance-oriented, it might not be without drawbacks.

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 is ERC-3643 and how does it improve tokenization?

  • ERC-3643 is a standard for tokenizing Real World Assets (RWAs) on blockchain. It integrates with legal frameworks, ensuring secure asset transfers and compliance with regulations like KYC/AML.

How does ERC-3643 address regulatory compliance?

  • ERC-3643 utilizes ONCHAINID for identity verification and regulatory enforcement. It includes features like transaction limits and investor qualifications to meet global regulatory requirements.

What are the main advantages of ERC-3643?

  • ERC-3643 offers enhanced security through multi-level permissions, global applicability, and integration with existing systems. It also enables the integration of blockchain with AI tools for optimized operations.

What challenges might ERC-3643 face?

  • Challenges include regulatory complexity, interoperability with existing systems, and scalability for handling various asset types and transaction volumes.

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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