What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Karolina

18 Mar 2024
What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Enter Berachain: a high-performance, EVM-compatible blockchain that is set to redefine the landscape of decentralized applications (dApps) and blockchain services. Built on the innovative Proof-of-Liquidity consensus and leveraging the robust Polaris framework alongside the CometBFT consensus engine, Berachain is poised to offer an unprecedented blend of efficiency, security, and user-centric benefits. Let's dive into what makes it a groundbreaking development in the blockchain ecosystem.

What is Berachain?

Overview

Berachain is an EVM-compatible Layer 1 (L1) blockchain that stands out through its adoption of the Proof-of-Liquidity (PoL) consensus mechanism. Designed to address the critical challenges faced by decentralized networks. It introduces a cutting-edge approach to blockchain governance and operations.

Key Features

  • High-performance Capabilities. Berachain is engineered for speed and scalability, catering to the growing demand for efficient blockchain solutions.
  • EVM Compatibility. It supports all Ethereum tooling, operations, and smart contract languages, making it a seamless transition for developers and projects from the Ethereum ecosystem.
  • Proof-of-Liquidity.This novel consensus mechanism focuses on building liquidity, decentralizing stake, and aligning the interests of validators and protocol developers.

MUST READ: Docs

EVM-Compatible vs EVM-Equivalent

EVM-Compatible

EVM compatibility means a blockchain can interact with Ethereum's ecosystem to some extent. It can interact supporting its smart contracts and tools but not replicating the entire EVM environment.

EVM-Equivalent

An EVM-equivalent blockchain, on the other hand, aims to fully replicate Ethereum's environment. It ensures complete compatibility and a smooth transition for developers and users alike.

Berachain's Position

Berachain can be considered an "EVM-equivalent-plus" blockchain. It supports all Ethereum operations, tooling, and additional functionalities that optimize for its unique Proof-of-Liquidity and abstracted use cases.

Berachain Modular First Approach

At the heart of Berachain's development philosophy is the Polaris EVM framework. It's a testament to the blockchain's commitment to modularity and flexibility. This approach allows for the easy separation of the EVM runtime layer, ensuring that Berachain can adapt and evolve without compromising on performance or security.

Proof Of Liquidity Overview

High-Level Model Objectives

  • Systemically Build Liquidity. By enhancing trading efficiency, price stability, and network growth, Berachain aims to foster a thriving ecosystem of decentralized applications.
  • Solve Stake Centralization. The PoL consensus works to distribute stake more evenly across the network, preventing monopolization and ensuring a decentralized, secure blockchain.
  • Align Protocols and Validators. Berachain encourages a symbiotic relationship between validators and the broader protocol ecosystem.

Proof-of-Liquidity vs Proof-of-Stake

Unlike traditional Proof of Stake (PoS), which often leads to stake centralization and reduced liquidity, Proof of Liquidity (PoL) introduces mechanisms to incentivize liquidity provision and ensure a fairer, more decentralized network. Berachain separates the governance token (BGT) from the chain's gas token (BERA) and incentives liquidity through BEX pools. Berachain's PoL aims to overcome the limitations of PoS, fostering a more secure and user-centric blockchain.

Berachain EVM and Modular Approach

Polaris EVM

Polaris EVM is the cornerstone of Berachain's EVM compatibility, offering developers an enhanced environment for smart contract execution that includes stateful precompiles and custom modules. This framework ensures that Berachain not only meets but exceeds the capabilities of the traditional Ethereum Virtual Machine.

CometBFT

The CometBFT consensus engine underpins Berachain's network, providing a secure and efficient mechanism for transaction verification and block production. By leveraging the principles of Byzantine fault tolerance (BFT), CometBFT ensures the integrity and resilience of the Berachain blockchain.

Conclusion

Berachain represents a significant leap forward in blockchain technology, combining the best of Ethereum's ecosystem with innovative consensus mechanisms and a modular development approach. As the blockchain landscape continues to evolve, Berachain stands out as a promising platform for developers, users, and validators alike, offering a scalable, efficient, and inclusive environment for decentralized applications and services.

Resources

For those interested in exploring further, a wealth of resources is available, including the Berachain documentation, GitHub repository, and community forums. It offers a compelling vision for the future of blockchain technology, marked by efficiency, security, and community-driven innovation.

FAQ

How is Berachain different?

  • It integrates Proof-of-Liquidity to address stake centralization and enhance liquidity, setting it apart from other blockchains.

Is Berachain EVM-compatible?

  • Yes, it supports Ethereum's tooling and smart contract languages, facilitating easy migration of dApps.

Can it handle high transaction volumes?

  • Yes, thanks to the Polaris framework and CometBFT consensus engine, it's built for scalability and high throughput.

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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