What is Dynamic NFT (dNFT)?

Maciej Zieliński

27 Sep 2022
What is Dynamic NFT (dNFT)?

Non-transferable tokens (NFTs) are becoming mainstream after widespread adoption in the Web3 community, attracting media attention from around the world and several prominent athletes and public figures launching their collectible items. As a result, NFTs have become one of blockchain technology's most publicly visible applications. The next step in the evolution of non-exchangeable tokens has just begun. Dynamic NFTs (dNFTs) expand the design space because they can adapt and change in response to external events and data. In this article, we will discuss dNFTs, how they can take NFTs to the next level, and current and potential dNFT use cases.

dNFT

NFT vs. dNFT

Static NFT transactions are non-exchangeable tokens and cannot be changed or modified. Once created, they exist on the blockchain as permanent, immutable records. These NFTs are often used to represent digital assets not intended to be changed or exchanged, such as works of art, collectibles, or digital identifiers. They can also be used to permanently store data in a blockchain, such as a decentralized database. Static NFTs have several advantages over dynamic ones. First, they are more secure because they cannot be changed once created. Second, they are more efficient and compatible with existing systems and protocols. As for shortcomings, however, they certainly exist as well. One is that static NFTs can't be updated or changed, so if you need to add new information, you're only left with creating a new NFT. This makes static NFTs less flexible than dynamic ones, as their very names indicate. Another disadvantage is that static NFTs are not compatible with some smart contract platforms. This limits the types of applications that can be built using static NFTs. But even with these limitations, static NFTs are still the most popular type of NFT and are used in many applications. Their security, performance, and compatibility make them a perfect fit for various applications.

dNFT

Benefits of static NFTs

Static NFTs have many advantages over dynamic NFTs.

First, they are more efficient in terms of storage and bandwidth requirements. This is because static NFTs only need to be saved once, no matter how many times they are exchanged or sold. In contrast, dynamic NFTs must be held every time the owner changes.

Second, static NFTs are more accessible to verify than dynamic NFTs. This is because all the information for the former is contained in a single file. In contrast, dynamic NFTs may have multiple related files, complicating verification.

Third, static NFTs are more resistant to fraud and forgery. Creating fake static NFTs is much more complex than forging dynamic NFTs.

Finally, static NFTs offer more flexibility in how they can be used. For example, they can be used as collateral for loans or as a form of digital property that can be shared.

Overall, static NFTs have many advantages over dynamic NFTs. They are much more efficient in terms of storage and bandwidth requirements. They are also easier to verify, more resistant to fraud and forgery, and offer more flexibility in their use.

Dynamic NFT (dNFT) transactions

Dynamic NFTs or dNFTs are tokens that can be updated or changed after they are created. They are, therefore, different from static NFT transactions, which cannot be modified. dNFTs are helpful in situations where the data associated with an NFT may need to be updated over time, such as digital assets representing real-world objects (e.g., artwork, cars, etc.) that may change ownership or location. Using dNFT, new asset holders can update their tokens to reflect the change in the user.

Benefits of dynamic NFT transactions

Dynamic NFTs have many advantages over traditional NFTs, including:

Increased Holder Engagement - Dynamic NFTs can help you share content or products. By offering something that is constantly changing or evolving, you can keep people coming back for more. This can take the form of new and exciting ways to do new challenges, collect rewards or interact with the content provided.

Greater immersion: Dynamic NFTs also have the potential to provide users with greater immersion. When people feel that they are constantly discovering new things, it can make the experience more enjoyable and memorable. This allows people to spend more time on your content and invest more in what you offer.

More loyalty - people are more likely to become loyal fans if they engage with your content and find new ways to enjoy it. 

More monetization opportunities: if you can successfully attract users with dynamic NFTs, additional monetization opportunities may arise. This could come from paying for premium content, rewards, or spending more time on the site or app.

Greater flexibility: Dynamic NFTs also provide more excellent content creation and distribution flexibility. You can gradually make it available with new challenges or rewards if you have an extensive library of content. This will help keep people interested and coming back for more and give you time to create new opportunities.

If your content is engaging and appealing to people, it is more likely to be shared by participants with their friends and family. This can increase the popularity and even organic growth of your business.

Better analytics: using dNFT can also provide better analytics and insights into how people interact with your content. This can help you optimize your offerings and make changes based on user feedback. It can also help you identify areas of potential monetization or new content ideas.

Increase exposure: Dynamic NFTs can also help increase the popularity of your company or brand. People consistently engaging with your content can lead to more whisper promotion and potential customers.

Increased fan engagement: You may see increased attention if you successfully attract followers through dynamic NFTs. This can include people sharing your content, giving feedback, and even becoming paying customers.

More opportunities for innovation - dNFTs also provide more opportunities for innovation and creativity. You can keep people interested and coming back for more by constantly changing and evolving. This can lead to new ideas and ways to improve your product.

There are many potential benefits to using dynamic NFTs, but remember that they may not apply in all situations. If you are considering using them, carefully weigh the pros and cons to ensure they suit your company or brand.

Potential use cases for dNFT

NFT metadata names tokens, assign attributes, and provide links to files. While tokenID provides a persistent identifier for a verifiable property, metadata is the essence of NFT - it contains the elements that make it worthwhile. Generative NFT designs often have a variety of features, some of which are rarer than others. These features are placed in the NFT metadata next to the IPFS link of the image or video corresponding to the NFT feature. In dNFT, these features change depending on external conditions. This feature is helpful for character development in blockchain-based games. When you first start playing with a playable NFT character, the basic statistics of the NFT are reflected in its metadata. The metadata changes as the player's level increase to reflect the character's development.

Use cases for dynamic NFT

Earlier in this article, we mentioned that in-game characters are a potential use case for dynamic NFTs. However, this is just an example. Therefore, we believe two additional use cases should be presented in this section to illustrate the benefits of dynamic NFTs.

Sports card dNFT

Sports card - we will use the card of a football player as an example. Let's assume we have a dynamic NFT representing a real soccer player. NFTs can store information such as speed, agility, power, goals, assists, etc., in the token's metadata. However, as the season progresses, these statistics change; for example, a player may score several goals. This means that dynamic NFTs can choose to retrieve data about a player's progress from outside the chain and update the metadata accordingly. This is not possible with static NFTs because the metadata will be persistent, from the moment someone creates a token.

Real estate and dNFTs 

The second example is real estate - in this case, we will use a house. Let's assume that we have a dynamic NFT representing this type of investment. When tokenizing tangible assets, it is advantageous to be able to change the metrics usually required.

In this example, we want the NFT metadata to reflect specific changes in the property that may be of interest to prospective buyers. For instance, they should show maintenance history, age, market value, past sales, etc. Therefore, when tokenizing physical assets such as real estate, it is advantageous to be able to update and change the token metadata.

Summary

As you can see, dNFTs are the answer to all the limitations of classic NFTs. Their liquidity and change over time make modern technologies quickly adapt to the real world. No wonder the above technology is developing faster and faster! 

Tagi

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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!