How to use liquidity pools in your decentralized exchange

Maciej Zieliński

27 Oct 2021
How to use liquidity pools in your decentralized exchange

Recently we summed up all you need to know about Automatic Market Makers. Get to know their key element- liquidity pools. How do they work and what do you need to know before you decide to implement them into your decentralized exchange? 

What will you find in the article?

  • Role of liquidity pools in AMM
  • Why liquidity pools are essential for DEXs
  • How does liquidity pool work?
  • LP tokens
  • How to use liquidity pools?

Definition

Liquidity pools are digital assets managed by smart contracts that enable trades between different tokens or cryptocurrencies on Decentralized Exchanges. Assets are deposited there by liquidity providers - investors and users of the platform. 

Liquidity pools are a backbone of Automatic Market Maker, which replaces one side of a trade with an individual liquidity pool. 

Decentralized Exchanges: Liquidity Pools

Liquidity pools are among the most robust solutions for contemporary DeFi ecosystems. Currently, most DEXs work on the Automatic Money Maker model, and liquidity pools are a crucial part of it.

To fully understand the importance of DeFi liquidity pools, we should first look at variable ways in which DEXs can handle trading. 

How do decentralized exchanges operate trading? 

  • On-chain order book
  • Off-chain order book
  • Automated Market Maker

Currently, the last of them seems to be the most effective. Therefore the vast majority of modern DEXs are based on it. Since liquidity pools are its backbone, their importance in the DeFi sector is undeniable. 

Problems with ordering books 

Before launching the first automated market makers, liquidity was a significant issue for decentralized exchanges, especially for new DEXs with a small number of buyers and sellers. Sometimes it was simply too difficult to find enough people willing to become a side in trading pair.

In those cases, the peer-to-peer model didn’t support liquidity on a sufficient level. The question was how to improve the situation without implementing a middle man, which would lead to losing the core value for the DeFi ecosystem - decentralization. The answer came with AMM.

Trading pairs 

Let’s use the example of Ether and Bitcoin to describe how trading pairs work in the order book model on DEX

If users want to trade their ETH for BTC, they need to find another trader willing to sell BTC for ETH. Furthermore, they need to agree on the same price. 

While in the case of popular cryptocurrencies and tokens, finding a trading pair shouldn’t be a problem, things get a bit more complicated when we want to trade more alternative assets. 

The vital difference between order books and automatic market makers is that the second one doesn’t require the existence of trading pairs to facilitate trade. All thanks to liquidity pools.

Role of liquidity pool in AMM

Automated Market Maker (AMM) is a decentralized exchange protocol that relies on smart contracts to set the price of tokens and provide liquidity. In an automated market makers' model, assets are priced according to a pricing algorithm and mathematical formula instead of the order book used by traditional exchanges.

We can say that liquidity pools are a crucial part of this system. In AMM trading pair that we know from traditional stock exchanges and order book models is replaced by a single liquidity pool. Hence users trade digital assets with a liquidity pool rather than other users.

P2P VS P2C

Peer-to-peer is probably one of the best-known formulas from the DeFi ecosystem. For a long time, it was a core idea behind decentralized trading.

Yet blockchain technology improvement and the creativity of developers brought new possibilities. P2C - peer-to-contract model puts smart contracts as a side of the transaction. Because smart contract can’t be influenced by any central authority after it was started, P2C doesn’t compromise decentralization.

Essentially Automated Market Makers is peer-to-contract solutions because trades take place between users and a smart contract. 

Liquidity providers

Liquidity pools work as piles of funds deposited into a smart contract.  Yet, where do they come from?

The answer might sound quite surprising: pool tokens are added to liquidity pools by the exchange users. Or, more precisely, liquidity providers.

To provide the liquidity, you need to deposit both assets represented in the pool. Adding funds to the liquidity pool is not difficult and rewards are worth considering. The profits of liquidity providers differ depending on the platform. For instance, on Uniswap 0.3% of every transaction goes to liquidity providers.

Gaining profits in exchange for providing liquidity is often called liquidity mining.

How do liquidity pools work?

Essentially, the liquidity pool creates a market for a particular pair of assets, for example, Ethereum and Bitcoin. When a new pool is created, the first liquidity provider sets the initial price and equal supply of two assets. This concept of supply will remain the same for all the other liquidity providers that will eventually decide to stake their found in the pool. 

DeFi liquidity pools hold fair values for assets by implementing AMM algorithms, which maintain the price ratio between tokens in the particular pool.

Different AMMs use different algorithms. Uniswap, for example, uses the following formula:

a * b = k

Where 'a' and 'b' are the number of tokens traded in the DeFi liquidity pool. Since 'k' is constant, the total liquidity of the pool must always remain the same. Different AMMS use various formulas. However, all of them set the price algorithmically. 

Earning from trading fees

A good liquidity pool has to be designed to encourage users to stake their assets in it. Without it supplying liquidity on a sufficient level won't be possible.

Therefore most exchanges decide on sharing profits generated by trading fees with liquidity providers. In some cases (e. g., Uniswap), all the fees go to liquidity providers. If a user's deposit represents 5% of the assets locked in a pool, they will receive an equivalent of 5% of that pool’s accrued trading fees. The profit will be paid out in liquidity provider tokens. 

Liquidity provider token (LP token)

In exchange for depositing their tokens, liquidity providers get unique tokens, often called liquidity provider tokens. LP tokens reflect the value of assets deposited by investors. As mentioned above, those tokens are often also used to account for profits in exchange for liquidity. 

Normally when a token is staked or deposited somehow, it cannot be used or traded, which decreases liquidity in the whole system. That’s problematic, because as I mentioned, liquidity has a pivotal value in the DeFi space

LP tokens enable us to liquid assets that are staked and normally would be frozen until providers will decide to withdraw them. Thanks to LP tokens, each token can be used multiple times, despite being invested in one of the DeFi liquidity pools.

Furthermore, it opens new possibilities related to indirect forms of staking. 

Yield Farming

Yield farming refers to gaining profits from staking tokens in multiple DeFi liquidity pools. Essentially liquidity providers can stake their LP tokens in other protocols and get for it other liquidity tokens. 

How does it work?

Actually, from the user perspective, it's quite simple:

  • Deposit assets into a liquidity pool 
  • Collect LP tokens
  • Deposit or stake LP tokens into a 
  • Separate lending protocol
  • Earn profit from both protocols 

Note: You must exchange your LP tokens to withdraw your shares from the initial liquidity pool.

How to use Liquidity pools in your DEX?

Decentralized finance develops at tremendous speed, constantly bringing new possibilities. The number of people interested in DeFi investments increases every day; hence the popularity of options such as liquidity mining recently has grown significantly. While deciding to launch our DEX, you have to be aware of that.

As I mentioned, liquidity has pivotal importance for decentralized finance, particularly for exchanges. Liquidity pools can't exist without investors that will add liquidity to them. Their shortage will lead to low liquidity. In consequence, that will be a cause of the low competitiveness of the exchange. On the other hand, for new DEXs it's still easier than attracting enough buyers and sellers to support order book trading.

Implementing liquidity pools to your DEX requires not only experience of blockchain developers’ fluently using DeFi protocols but also a solid and well-planned business strategy. That's why choosing a technology partner with previous experience with both blockchain development and business consulting in the decentralized finance field might be the optimal solution.

Do you want to gain more first-hand knowledge regarding liquidity pools development and implementation? Don't hesitate to ask our professionals that will gladly answer 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!