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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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

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!