The Technology Behind ERC-3643: How Does it Work?

Miłosz Mach

05 Jan 2024
The Technology Behind ERC-3643: How Does it Work?

The journey of token standards has been one of constant evolution and breakthroughs. ERC-3643 marks a departure from its predecessors, introducing a suite of advanced features structured to meet the expanded demands of modern applications. Its framework re-envisions the way tokens operate within the Ethereum network. This article unravels the technological intricacies of ERC-3643 and describes its foundations.

Technical Anatomy of ERC-3643

ERC-3643 is a blend of innovative and practical designs tailored for complex blockchain applications. It evolves the scope for tokenization, balancing flexibility with stringent regulations. The standard may strengthen token smart contract performance, utilizing:

Modular Structure: Accommodates the needs of different platforms, enabling the integration of various functionalities as independent or interlinked components;

Multi-Layered Token Control Mechanism: Introduces token management features including role-based permissions and multi-signature requirements. This provides fine-grained authority over token behavior, enhancing security against misuse and uncertified parties access prevention:

  • Advanced Permissioning: Employs access controls that ensure token operations comply with predefined regulations. It allows token issuers to control who can hold, transfer, and perform operations;
  • Restriction Features: Set up of protocols that can secure compliance with various law requirements, an essential factor for tokens representing regulated assets. Issuers may enforce legal and policy integrity directly within the token’s smart contract. This consists of automated acquiescence checks, mandatory permission for token transfer, and on-chain governance mechanisms.

Conditional Operations: The standard extends the idea of conditional transactions where specific principles must be met for the action to be executed;

Dynamic Token Behavior and Customizability: Certain rules can be modified post-deployment according to specific needs;

Token Recoverability: Enables token retrieval in cases of accidental loss or theft;

Gas Cost Optimization Techniques: This concerns the use of optimized bytecode in the smart contract and efficient data storage methods, reducing the amount of computational work required for transaction conduction;

Inter-Contract Communication System: Thanks to well-defined interfaces and an event-driven infrastructure, ERC-3643 tokens interact with different EVM-compliant platforms.

ERC-3643: Prioritizing Security

It offers a suitable environment for assets operating in highly scrutinized markets.

  • Programmable Criteria: ERC-3643 permits customizable compliance logic implementation and enables the regulations encoding into the token. This includes automatic enforcement of trading restrictions;
  • Cross-Jurisdictional Adherence: Acknowledging the global nature of blockchain platforms, ERC-3643 strives to be used in various circumstances. It encompasses Know Your Customer (KYC) and Anti-Money Laundering (AML) procedures. It does not guarantee embedded out-of-the-box components. This, in turn, opts for propitious infrastructure incubating the external services integration;
  • Reporting: Supports comprehensive audit trails essential for regulatory oversight. Consists of immutable transaction logs, token holder reports, and automated compliance reports.
  • Security Protocols: Robust security protocols to safeguard against common vulnerabilities found in smart contracts.

ERC-3643: Bridging Technology Interest and Cross-Jurisdictional Regulation Compliance


Conformity, viewed from a holistic perspective, constitutes an essential factor in fostering technology adoption. ERC-3643 thoroughly examines legislation and innovation tendencies, thereby effectively empowering the overall process.

  • Interoperability: ERC-3643 is compatible with other Ethereum-based applications. Tokens can easily interact within current ecosystems;
  • Adaptability: It recognizes the fluid variability of the regulated markets, and responds to newly determined conditions.

Conclusion

The inception of ERC-3643 stems from the constraints of past token standards. Representing a decent shift, it marries the need for adherence compliance with a trending spirit of digital tokenization. As we have observed, this standard underscores the paramount importance of regulatory and security awareness. Its forward-thinking structure suggests that it will adapt alongside regulatory changes and functional advancements. If that's the case, ERC-3643 may serve as a beacon for the tokenization of EVERY asset.

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 its difference from previous standards?

  • ERC-3643 is an advanced token standard on Ethereum, prioritizing security and offering modular structure, multi-layered control, and gas optimization, unlike previous standards.

Key features of ERC-3643?

  • Modular structure, multi-layered control, advanced permissioning, compliance features, conditional operations, token recoverability, gas optimization, and inter-contract communication.

How does ERC-3643 ensure security?

  • Through programmable compliance criteria, cross-jurisdictional adherence, comprehensive reporting, and robust security protocols.

Significance of ERC-3643?

  • It represents a shift by combining compliance with tokenization trends, with potential for use in tokenizing any asset.

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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