What is Automated Market Maker (AMM)?

Maciej Zieliński

07 Oct 2021
What is Automated Market Maker (AMM)?

Forget order books, the future of Decentralized Exchanges lies in Automated Market Makers. Automated Market Maker AMM enables traders to earn shares of transactions in exchange for becoming liquidity providers. What does it mean for DEXs? 

In this article you will learn:

  • What are Automated Market Makers?
  • How does Automated Market Maker work?
  • AMM vs On-chain / Off-chain order book 
  • How to implement liquidity pools into your DEXs
  • Why are Automated Makers so important for the whole DeFi ecosystem?

Automated Market Makers were first introduced to the public with the release of Uniswap in 2018. 

Essentially, they are autonomous trading machines that replace traditional order books with liquidity pools run by algorithms. 

What are Automated Market Makers?

As we mentioned in one of our previous articles, a decentralized exchange can handle trading in three ways:

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

The last one is undoubtedly the most efficient. That's why the vast majority of modern decentralized exchanges are based on it.

Definition:

Automated Market Maker AMM is a decentralized exchange protocol that relies on smart contracts to set the price of digital assets and provide liquidity.

Cryptocurrency assets are priced according to a pricing algorithm and mathematical formula, instead of the order book that is used by traditional exchanges.

The mathematical formula varies from protocol to protocol. Uniswap, for example, uses the following formula:

a * b = k

Where 'a' and 'b' are the number of tokens traded in the 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. 

What's important, Automated Market Makers allow almost anyone create a market using blockchain technology.

How Automated Market Makers work?

For trading pairs, for example, BTC/ETH, Automated Market Makers work similarly to order books, which are based on buy and sell orders. However, a vital difference is that a trading pair isn't needed to make a trade. Alternatively, users can interact with a smart contract that will constitute the other side of the trading pair for them. This is what the term “automated market-making” refers to. 

P2P and P2C

You are probably familiar with the term “peer-to-peer transactions,” which is crucial to understanding decentralized exchanges. Every transaction that runs between two users without any intermediary can be called P2P. 

We can think about Automated Market Makers as peer-to-contract solutions because trades take place between users and a smart contract. 

Liquidity pools

Trading pairs, which you know from Centralized Exchange and Decentralized Exchange using order books, are an individual liquidity pool in Automated Market Maker. Therefore, users are essentially trading funds with liquidity pools, rather than with other users. 

If you want to trade two tokens, for example, sell BNB for Ether, you need to find the BNB/ETH liquidity pool. 

We can imagine a liquidity pool as a large pile of assets. But where do they come from?

Liquidity providers 

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

In exchange for providing liquidity, liquidity providers earn fees on transactions in their pool. Unlike traditional market making with professional market makers, here anyone can become one. 

Profits for liquidity

To become a liquidity provider 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.

Slippage on Automated Market Makers

Different Automated Market Makers may encounter different issues. Yet the risk of slippage is something we should always keep in mind while planning our own DEX. 

Why does it occur?

As I mentioned earlier, asset pricing is determined by an algorithm and a mathematical formula. We can say that it's determined by the ratio between the assets in the liquidity pool. Or more specifically, it is the change in this ratio that occurs after a trade. The larger the transaction, the wider the margin of change, and the greater the amount of slippage. 

Indeed, when a large order is placed in AMMs and a sizable amount of coin is removed or added to a liquidity pool, it can even cause a notable difference between the market price and the pool price. 

More liquidity = less slippage 

In the Automated Market Maker model, more liquidity means less slippage that large orders may incur. Ultimately, this may attract more volume to your DEX. That's why if you want to use Automated Market Maker on your platform, you need to have a solid strategy for encouraging your users to deposit funds in liquidity pools.

You need to remember that to stay competitive in the decentralized finance market, you should offer liquidity of at least a sufficient level. 

Generally, 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. When users want to leave the pool, they simply exchange their tokens for their share of transaction fees. 

Yield Farming

Yield farming is one of the most important opportunities that can attract new users to your DEX platform. How does it work? What does it even mean? 

LP tokens

We often say that liquidity has a pivotal value in the DeFi space. Creating tokens that are awarded in exchange for providing liquidity is a great idea to increase it. 

Normally when a token is staked or deposited somehow, it cannot be used or traded, which decreases liquidity in the whole system. In the case of Automated Market Makers, implementing easily convertible liquidity provider tokens solves the problem of locked liquidity. Their mechanism is simple: users get them as proof of owing tokens that they have deposited. 

With LP tokens, each token can be used multiple times, despite being invested in one of the liquidity pools. Additionally, we can say that LP tokens open up a new, indirect form of staking. This means that instead of staking tokens themselves we just prove that we own them. 

What is Yield Farming? 

Yes, on multiple exchanges users can stake their LP tokens and profit from them. Essentially, this is what we call yield farming. The main idea behind it is to maximize profits by moving tokens in and out of different DeFi protocols.

How does it work on DEXs? 

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.

What is impermanent loss?

Impermanent loss occurs when the price ratio of two assets changes after traders deposit them in the pool. The higher the shift in price, the more significant the impermanent loss. Impermanent loss mostly affects liquidity pools with highly volatile assets. 

However, this loss is impermanent: there is a probability that the price ratio will revert. Permanent losses can only occur if liquidity providers withdraw their digital assets before the price ratio reverts. 

Conclusion 

Of all the solutions that we can currently observe on decentralized exchanges, the Automated Market Maker offers the highest liquidity. Today most DEXs are running on AMM or plan to implement it in the nearest future. That's why Automated Market Maker has crucial importance for the DeFi ecosystem.Do you want to know how to apply Automated Market Maker in your project? Don't hesitate to ask our specialists for a free consultation.

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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