NFT tokens – they are the future of tokenization

Maciej Zieliński

23 Mar 2021
NFT tokens – they are the future of tokenization

Among the many ways to distinguish tokens, the most basic is the division into convertible and non-exchangeable tokens - fungible, non-fungible (NFT tokens). Until now, first and foremost, tokens of the first category have enjoyed the greatest popularity and recognition in the Blockchain environment. However, this may change dramatically soon.

Although cryptographic tokens are made of just a few lines of code, their potential is enormous. We already use them today, among others creating digital equivalents of real assets such as stocks and real estate, or creating innovative systems for tracking products in the supply chain. And with the increasing digitization, the list of their applications is constantly growing.

NFT tokens use Blockchain technology to connect to a unique digital asset that cannot be replicated. Recently, they have found more and more applications in such key areas as IoT or supply chains. In 2020 alone, their total value has tripled to over $ 315 million.

NFT tokens - how they differ from others

Non-fungibility in the case of NFT tokens means that each token in a given system is unique. Such tokens are not of a standard value and often do not allow for the equivalent exchange of one for another. Each token represents distinct, unique ownership or identity information

NFT tokens basic advantages:

  • They are impossible to counterfeit
  • They can be moved
  • They keep property rights

NFT tokens

NFT Tokens - Key Applications

Certification

We can use NFT tokens to prove the origin of a document, piece of data or basically any physical object in the real world. And because such tokens cannot be duplicated, and the information contained in them cannot be manipulated, we are sure that such a token - a certificate of authenticity, will never be forged.

Securing the authenticity of works of art, luxury fashion or exotic cars - the possibilities of such tokens go much further. If the land records were transferred to the blockchain, ownership would only be a matter of having the token corresponding to the property. The same applies to the rights to extract raw materials or the rights to water. Non-exchangeable tokens have countless potential applications wherever ownership certification is important. Already today, NFT tokens are used to sell digital works of art. An example is the American artist Mike Winkelmann (known as Beeple), who auctions NFT tokens equivalent to the ownership rights of his works. The most expensive of them - The Complete MF Collection was sold this way for nearly eight hundred thousand dollars. It was thanks to the American that Christies became the first large auction house to auction the NFT token - Everydays: The First 5000 Days.

The identity of things

Like people, products, machines and raw materials can also have their own digital identity. IDoT is a key element of blockchain-based supply chains and IoT applications. For example, by granting unique tokens to products, it becomes possible to trace their entire path in the supply chain - from raw material extraction, through production to sale to retail customers. This not only allows you to secure their origin, but also to control the conditions of transport, especially important in industries such as food. If a broken chicken comes to the supermarket, thanks to the tokens, it is easy to determine at which stage the deficiencies occurred and which entity is responsible for them.

ERC-721 tokens

Currently, the most popular standard in which NFTs are created is ERC-721 running on Ethereum. Introduced in 2018, it gained popularity thanks to the online collector game Crypto Kitties. Apart from the ease of creating immutable tokens, its greatest advantage is its compatibility with other Blockchain networks. In addition to Ethereum, such tokens will work, among others on Blockchains such as EOS or NEO.

The most popular is does not mean the only one. NFT tokens have already been a pain of interest for among others Binance cryptocurrency exchange, which plans to release its own standard of NFT tokens in 2021 - BEP-721.

NFT tokens
cryptozink.io

Largest NFT projects

OpenSea - NFT's leading art and other collector's items market.

Async.Art - to kolejny rynek stokenizowanej sztuki, pozwalający użytkownikom nie tylko na sprzedaż i zakup, ale również stworzenie swojego własnego tokenowego dzieła. 

Axie Infinity - a platform for purchasing virtual land. It was through it that one of the users purchased land worth over $ 1.5 million, which is the largest NFT purchase ever.

Decentraland - a leading NFT project focusing on a distributed virtual world. The users buy virtual land here too.

NFT tokens - summary

It is worth noting that NFT tokens are still a very new area. Therefore, it can be expected that the most interesting solutions are yet to be developed. Therefore, at Nextrope, we have placed NFT tokens among the hottest tokenization trends for 2021.

Would you like to use tokenization in your project? Contact our specialists who can give you a free consultation.

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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