Solana NFT – how to build your own token marketplace?

Maciej Zieliński

14 Oct 2021
Solana NFT – how to build your own token marketplace?

The NFT boom isn’t over yet. In fact, it has barely started. The new Solana NFTs projects prove that. What do you need to know about the solutions that may bring NFTs to the mainstream for good?

You are probably aware of the many NFT projects built on the Ethereum or Binance Smart chain. In fact, the Ethereum blockchain still remains the most popular solution for minting and selling NFTs. Yet, because of skyrocketing gas fees and general system network overload, the need for alternatives is constantly increasing. Recently, one strong competitor emerged: Solana. 

Solana blockchain 

Launched in 2020 by the Solana foundation, Solana Blockchain aims to solve scaling problems that struggle with most of the contemporary blockchain protocols. Its main objective is to support DeFi ecosystem growth by fitting in the so-called blockchain trilemma: decentralization, security, and scalability.

Combining those three factors seems to be the holy grail of the blockchain world. Many projects succeed in supporting one or even two of the factors, but fail when it comes to others. Solana engineers believe that they have implemented all three. 

Solana vs Ethereum
Solana vs Ethereum

The hybrid consensus algorithm 

Solana is a third-generation blockchain that, unlike other blockchains, uses a hybrid consensus algorithm. To be more precise, it combines proof-of-history (PoH) with proof-of-stake (PoS). Due to that, it’s able to process over 50,000 transactions per second. For comparison, Ehereum can’t handle more than 30 at a time. Now you know why expectations toward Solana are so high.

SOL

Did Solana creators succeed? Its native currency, SOL, quickly became the 8-largest cryptocurrency with a capitalization of over 43 000 000 000 USD. Therefore, maybe we can assume that they did… At least many believe that.

Solana NFTs 

Ok, so we have a fast, very promising blockchain with quickly increasing popularity. Why shouldn’t we use it for NFT minting? Many of the recently emerged NFT projects prove that it might be a tremendous idea. 

What new possibilities can the Solana ecosystem bring to the world of NFTs? Think about 3D NFT or whole NFT-based games with mechanics mostly performed on-chain. Because of high gas prices, it would never be possible on Ethereum. The low fees and high speed that Solana offers may open entirely new doors for NFT development. 

Solana NFT
Solana NFT

Solana NFT projects

During the last few months, we have observed a true boom among Solana NFTs marketplaces. I’d like to give you a few examples of the most interesting projects:

Solanart

According to its creators, Solanart is the first fully developed NFT marketplace build on the Solana blockchain. With many great projects listed on it and many more to come, it is more than promising. 

Solanart
Solanart

Lollapalooza x Solana NFT

Do you still have doubts that NFTs will change entertainment for good? Check how Lolapalloza, one of the biggest music festivals in the world, enables fans and artists to connect directly by launching their own NFT marketplace.

Lolla NFT is one of the first projects of this kind and it’s built entirely on the Solana blockchain. 

Lolla NFT
Lolla NFT

Building the Solana NFT marketplace 

If you’re thinking about launching your own NFT platform, the Solana blockchain is an important option to consider. Low transaction fees and high speed bring totally new possibilities that attract both creators and investors. After all, it’s a great way to capitalize on one of the most recent trends in the blockchain industry. 

Building the Solana NFT Marketplace step by step
Building the Solana NFT Marketplace step by step

Building the Solana NFT Marketplace Step by Step 

Establish the goals and requirements for your new project. 

In this phase, don’t forget about legal aspects. Different jurisdictions have different regulations. You must be aware of that. 

Prepare a White paper.

It should summarize all the new features you want to include in your marketplace. Not sure how to write a White paper? Choose a blockchain development company that can handle more than just software. In Nextrope, our clients often decide on complex services which start with the business and legal consulting. 

Develop the backend of your exchange.

Here you are going to need the best professionals available. Make sure they are familiar with the Solana blockchain as well as they have previous experience with creating a scalable NFT marketplace. Check out our article Blockchain developer – how to hire the right professional to learn how to find the right specialists.

Design frontend. 

Quality UX and UI are your keys to success. 

Integrate compatible wallets.

Enable your users to store their NFTs in a secure way. One of the most popular choices for Solana NFTs is Phantom Wallet.

On every stage directly involving blockchain technology, you will have to decide between hiring developers in-house and outsourcing them. Our article on outsourcing vs insourcing blockchain may help you. 

The testing phase. 

Make sure everything is on point. Fix all the bugs and glitches. 

Prepare beta version. 

Check if platform operation complies with your requirements.

Maintenance. 

Work on the marketplace doesn’t stop with its launch. If you decide to hire a custom software development company, make sure they will also handle maintenance and further development. 

Conclusion 

Solana is one of the fastest developing phenomena in the DeFi.  Same as NFT. A combination of the two may be the next great trend in the industry. With the abundance of new possibilities, we can expect a rapid growth of new NFT projects in the Solana ecosystem.

Low transaction fees and high speed combined with top-notch security are good reasons to consider Solana while building your own NFT marketplace. If you are looking for a technology partner that will help you choose the right blockchain and develop a scalable solution, contact our professionals who will be happy to answer all your questions.

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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!