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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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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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