Creating NFT – the best tools for issuing tokens of the future

Maciej Zieliński

28 Apr 2021
Creating NFT – the best tools for issuing tokens of the future

The entire world is talking about them- NFT tokens break popularity records not only in blockchain-related environments. More and more people and companies are becoming interested in issuing them. So how does creating NFT look like in practice? 

NFT tokens take advantage of the Blockchain technology for connecting with a unique digital resource that cannot be replicated. Non-fungibility, in the case of NFT tokens means that each token in a given system is unique. NFT tokens do not have a standard value and often do not allow equivalent exchanges of one for another. Each token represents different, unique information concerning ownership or identity.  

NFT can be used to represent virtually any digital or physical object, including:

  • Music
  • Collectible items
  • Graphics
  • Videos 
  • Items in video games (e.g. weapons or avatars)
  • Virtual Land 
  • Real-world assets e.g. real estate, sports shoes

Creating NFT 

Creating simple NFTs, such as graphics, collectible cards, or GIFs, is a relatively simple process, even for people who do not have extensive knowledge in the field of blockchain technology.  


Which blockchain to create NFT?

The basic question you should answer before starting work is which Blockchain protocol to choose to issue your NFT tokens. Various blockchain protocols have their own different NFT standards as well as compatible markets and portfolios. For example, tokens created with Binance Smart Chain can be sold only on platforms that support Binance Smart Chain assets. This means that we cannot sell them on, for example, OpenSea which is based on Ethereum.

Currently, Ethereum blockchain is most often used to create NFT. However, there are alternatives that are becoming increasingly popular.

  • Binance Smart Chain 
  • Flow by Dapper Labs 
  • Tron
  • EOS
  • Polkadot
  • Tezos
  • Cosmos
  • WAX
Creating NFT

What platform for selling NFT

There are many online platforms on the market where we can sell our NFT. It is worth taking a closer look at the characteristics of some of them. 

OpenSea

The platform refers to itself as the largest NFT exchange in the world. OpenSea allows selling NFT created in ERC721 and ERC1155 standards (both on Ethereum). Currently, 700 different projects offer tokens on the platform, including Axie, CryptoKitties, and Decentraland. 

The platform's great advantage consists in the ease of creating your own NFT with the included tools. 

Rarible

Rarible is a community-owned stock exchange that focuses on artistic NFT. Creators can take advantage of the platform for creating new tokens representing their works such as books, graphics, music albums, and videos.

The platform assigns ERC-20 RARI tokens to its users, thus making them the "co-owners". 75,000 RARIs are distributed per week.

SuperRare

SuperRare focuses on trading unique digital arts. The creators describe their platform as a meeting point of Instagram and Christie's, offering a completely new approach to interacting with culture and art on the internet.

Currently, SuperRare works only with a relatively small group of exclusively selected artists. However, it is already possible to fill out a form and apply to join once the platform is fully launched. 

Foundation

The Foundation brings together creators, crypto investors, and collectors on a single platform focused on digital art. 

Every time there is an NFT trade carried out on it, the artist earns 10% on the transaction. This means that the creator receives 10% of the sale value when a collector resells that person's work at a higher price. 

Myth Market

Myth Market is a group of trading platforms that support different brands of digital trading cards. 

Other platforms for creating and trading NFT include for example:

  • Mintable
  • BakarySwap
  • AtomicMarket
  • KnownOrigin
  • Enjin Marketplace
  • Async Art
  • Portion

Creating NFT on Ethereum 

Since Ethereum constitutes the largest NFT ecosystem, this article describes the creation of NFT precisely with it. In terms of other protocols, this process looks relatively similar, but if you have any questions you can always contact our team of specialists. 

To issue your own NFT on Ethereum, you will need:

  1. ETH worth about 50-100 dollars.
  2. A cryptocurrency wallet supporting ERC-721 tokens (NFT is created in it), for example MetaMask 

There are many platforms that allow creating NFT on Ethereum. Below we focus on the largest of them - OpenSea. 

Creating NFT

Creating NFT step-by-step

  1. After clicking on the "Create" button, the platform will ask you to connect your crypto wallet. After entering the password, it will be automatically connected with OpenSea. 
  2. Move the cursor over the "Create" button in the upper right corner and select the "My collections" button, and then click on the blue "Create" button.
  3. In the window that should then appear, submit your graphic, enter a name and description. That is how you will create a folder for your NFT. 
  4. By click on the pencil icon in the upper right corner, enter a background image for your collection page.
  5. Now all it takes is to click on "Add New Item" and upload a graphic, audio, video, or 3D model.
  6. Now, just press the "Create" button and... thus, create the first NFT.

How much does it cost to create NFT?

Creating NFT on OpenSea is free. However, some platforms charge a fee, called "Gas" (Ethereum-based platforms). "Gas" is simply the amount of Ether needed to perform a specific operation in a blockchain. This cost varies and depends on the overload of the entire network. The more people perform transactions at a given time, the higher the fee. 

How to create NFT? - Conclusion

Thanks to the numerous available tools, creating NFT has become extremely simple, not requiring specialist knowledge. It is safe to say that basically anyone can issue simple NFT tokens. 

It is worth noting that NFT is still a very young and dynamically developing area, so it can be expected that the most interesting solutions are yet to be developed. For this reason, among other things, we have ranked NFT tokens as one of the hottest tokenization trends for 2021. 

Would you like to take advantage of NFT in your project? Contact our specialists who can offer 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!