What is Decentralized Exchange?

Maciej Zieliński

25 Sep 2021
What is Decentralized Exchange?

DEX is far more than just another DeFi trend in the game. Decentralized exchanges allow crypto traders to swap tokens in a peer-to-peer model. Direct transactions between parties, no need to sign in - these are just some of their advantages over centralized exchanges.

In this article, you will find:

  • How does decentralized exchange work?
  • Decentralized exchange vs centralized exchange 
  • Pros and cons of decentralized exchanges
  • What do DEXs mean for the world of decentralized finance?

Definition: 

Decentralized exchange, also known as DEX, is a platform where crypto investors can buy and sell cryptocurrencies without intermediaries.

Substantially any exchange working on a peer-to-peer basis could be called decentralized. Yet, in this article, we will focus on those with backend existing on a blockchain. 

Thanks to the usage of that technology, no one takes custody of your assets and the safety of transactions is guaranteed by protocol. Therefore, you don't have to give the exchange this amount of trust as in the case of centralized exchanges.

How does CEX work?
How does CEX work?

How does centralize exchange work?

Centralized exchanges, for example, Coinbase, are digital markets where people can buy, sell and trade digital assets such as Bitcoin, Ether, or other cryptocurrencies.

To access the site, you have to sign up by providing banking details and identifying personal data. This is the part of KYC and AML practices, which have to be followed by every centralized exchange. Because such data is fragile, it's quite clear that a security dilemma is present even before one starts trading.

Buying cryptocurrencies on a centralized exchange

The price of each coin on the site is based on an "order book" - consisting of orders to buy and sell.

Trading on centralized exchanges generally seems simple. You just need to choose coins and confirm a transaction.  The exchange will show the funds you acquired in your account. Then you can trade them for other digital assets.

Yet, users don't really hold their funds. Exchange work here as a custodian of customer funds. Furthermore, trades don't occur on the blockchain. Instead, they take place only within the exchange's database.

Disadvantages of central authority

From a technical point, when you deposit funds on a centralized exchange, you lose control over it. Exchange puts your funds into wallets controlled by it.

Additionally, it owns your private keys. Therefore, if you want to withdraw your money, the exchange has to sign the transaction on your behalf.

Security

With CEXs come some vital security questions.

Firstly, exchanges can limit user access to their assets or even restrict the ability to trade them.  Secondly, the risk of a hacker attack is always present. Exchanges work very hard to avoid it, yet as the example of Mt. Gox shows, they are still vulnerable.

Advantages of centralized exchanges

Generally, this type of cryptocurrency exchange is easier and more convenient to use than decentralized exchanges. Especially for the newcomers.

Moreover, trading there is often faster because the whole process doesn't take place on a blockchain. Additionally, trading fees can be lower as well.

Decentralized exchanges vs centralized exchanges
Decentralized exchanges vs centralized exchanges

How do decentralized exchanges work?

In many ways, decentralized exchanges are similar to centralized ones. However, differences are more than substantial. Essentially in decentralized exchanges, trade rely on a blockchain (most often Ethereum or Binance Smart Chain). Trading between users is conducted using smart contracts - orders are executed on-chain. Thanks to that, during the whole process exchange, doesn't take control of users' assets.

Cross-chain exchanges are a very promising novelty on the DEX market. Yet, most of the popular decentralized exchanges operate only on one blockchain - most often Ethereum or Binance Smart Chain.

There are three ways in which decentralized exchanges operate trading:

  • On-chain order book
  • Off-chain order book
  • Automated Market Maker
How does decentralized exchange handle trading?
How does decentralized exchange handle trading?

On-chain order book

There are decentralized exchanges where every transaction is written to a blockchain. It means that every order, as well as cancellation or alteration, is handled on-chain.

Without a doubt, this is the purest approach to decentralization. There is absolutely no third party involved at any stage of trading. Everything is extremely transparent. Unfortunately, there are vital downsides as well.

The on-chain order book is far less practical than the other two options. Firstly, because every node on the blockchain record the order, placing it requires paying a fee. Furthermore, users have to wait until the miner adds necessary data to the chain. It translates to high costs and poor liquidity.

Front running

Front running refers to a situation when some insider posses information about a pending transaction and uses this knowledge to place an order before the transaction is completed. Because he benefits from the fact that is inaccessible to the public, it’s illegal. Some believe that’s a serious threat in the on-chain model.

It can't occur in the traditional way, since everything is recorded on the global ledger. Yet, a miner can observe the order before it's added to the blockchain,  and add their order first.

Off-chain order book

Off-chain order books are a bit more centralized than their counterparts. But they are also far more practical. In this model, orders are hosted elsewhere and only the final transaction is settled on the blockchain. Moreover, you can still benefit from non-custodial storage.

Because orders aren't stored on-chain, this approach is faster and less costly. Furthermore, it helps to achieve better liquidity of trades. However, it can encounter some of the security issues typical for CEXs.

Automated Market Maker (AMM)

Automated Market Maker, sometimes called Proactive Market Maker, has some serious advantages over the previous two solutions.

In order books model, if you have Bitcoin and want to trade it for Ether, you need someone who wants to buy Bitcoin and have Ether. Moreover, they have to be willing to trade at an agreed-upon price.

AMM simply removes counter-parties and applies algorithms that deal with asset pricing. With Automative Market Maker, you can trade Ether regardless of whether there’s someone who wants it for Bitcoin. 

To achieve it, AMMs typically use liquidity pools. We'll explain this term in another article.

Decentralized margin trading

Margin trading refers to the practice of borrowing funds from a broker to trade a financial asset, which forms the collateral in lending from the broker. Usually, a broker in DeFi is one of the AMMs.

Pros and cons of Decentralized Exchange
Pros and cons of Decentralized Exchange

Pros of decentralized exchange

Lower risk

Decentralized cryptocurrency exchange doesn't hold users' assets. Because they are held in a private wallet, and you have the keys, they are immune to hacks.

No KYC needed 

Most of DEXs doesn’t have to follow KYC and AML requirements, because they don’t intermediate in transactions between parties. That’s why it’s often more convenient to build your own DEX than CEX. 

More options

On the DEX platform, trades of tokens that aren’t listed on CEXs are possible.

Cons of decentralized exchange

Trading volume

The volume traded on CEXs is still much higher than that on DEXs. Liquidity is lower as well.

Higher fees

It's not an absolute norm, but when it comes to fees CEXs often offer the best price.

Convenience

Decentralized exchange is less user-friendly than a traditional one.

Conclusion

Decentralized Exchanges can be considered as one of the key factors in the current Defi boom. That’s why we mentioned them among the top DeFi trends for 2021. To this point, everything indicates we were right. Just look at the success of projects like Uniswap or PancakeSwap. 2021 definitely belongs to DEXs. Apparently, today crypto traders value high security, privacy, and the wide range of options that they bring. 

Yet, DEXs are still a relatively new branch of the crypto world. Therefore, there is still much space for innovation. That’s why more and more investors become interested in building their own Decentralized Exchange. With the high speed of blockchain technology development and the growing popularity of alternative crypto assets, circumstances are more than promising.

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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!