Blockchain for the Planet: The Rise of Eco-Friendly Cryptocurrency Initiatives

Paulina Lewandowska

08 Mar 2023
Blockchain for the Planet: The Rise of Eco-Friendly Cryptocurrency Initiatives

Introduction

Innovative solutions, especially in the cryptocurrency industry, are developing as people throughout the world become more aware of the effects of climate change. This essay will examine three various cryptocurrency initiatives that seek to promote environmental preservation. Blockchain technology is being used in these initiatives to encourage and track carbon emission reductions, boost ecosystem regeneration, and offset carbon footprints. By offering creative ways to fund sustainable projects, these initiatives have the potential to have a big impact on the fight against climate change. But, before making an investing decision, it is crucial to conduct careful study.

Flowcarbon

A cryptocurrency startup called Flowcarbon appears to be trying to combat climate change by using blockchain technology to reward projects that reduce carbon emissions.

On their website, Flowcarbon claims that their blockchain technology tracks the reductions in carbon emissions and turns them into carbon credits that can be exchanged on a carbon market. Individuals and organizations can purchase and sell the credits, and the money raised can be used to fund eco-friendly initiatives and reduce carbon emissions.

The Proof-of-Reduction (PoR) consensus mechanism is used by the Flowcarbon platform to verify carbon emission reductions and record them on the blockchain. The business asserts that this procedure guarantees transparency and completely disallows double-counting or fraud.

The Flowcarbon team consists of experts in carbon markets, blockchain technology, and sustainable development. The project is still in its early stages, but by offering a new approach to encourage and finance carbon reduction projects, it has the potential to have a huge impact on the fight against climate change.

Klima DAO

A decentralized autonomous organization (DAO), Klima DAO, promotes carbon emission reduction as a means of preventing climate change. The concept is based on the Ethereum blockchain and was introduced in 2021.

The main goal of Klima DAO is to establish a system where individuals and companies can contribute money to buy carbon credits, which are then retired (or taken out of circulation) to reduce the carbon emissions caused by their operations. They want to create a decentralized, open, and transparent market for carbon credits.

KLIMA tokens, which stand for a percentage of a tonne of carbon emissions that have been reduced through the retirement of carbon credits, can be bought by people and businesses through the Klima DAO system. These credits are obtained from initiatives that have been shown to lower carbon emissions, like those involving renewable energy or forestry.

The offset is permanent since once the carbon credits have been retired, no one else can use them. Additionally, Klima DAO makes sure that the initiatives from which they buy carbon credits follow stringent environmental and social guidelines, avoiding initiatives that can have a negative impact on regional residents or wildlife.

Additionally, Klima DAO has a governance structure that enables KLIMA token owners to suggest and approve changes to the project. This gives the community a voice in how the project runs and develops over time.

Regen Network

By providing verified carbon sequestration and other ecosystem services, the decentralized Regen Network, founded on blockchain technology, intends to aid in the regeneration of the planet's ecosystems. Through this project, a new economic paradigm that values and rewards ecological health will be established.

The Cosmos network, which permits interoperability across several blockchains, serves as the foundation for Regen Network, which was introduced in 2018. The REGEN token, which powers the project and is intended to reward and motivate network actors, is utilized to operate it.

Regen Network's main objective is to develop a platform where ecological data can be safely stored, validated, and shared. Information on soil health, biodiversity, carbon sequestration, and other ecosystem services is included in this data. Participants can gain REGEN tokens on the network by adding to the data pool by monitoring and validating ecological data.

Also, Regen Network makes it easier to create and trade ecosystem service credits that are supported by actual ecological data. Individuals, groups, and governments can acquire these credits to reduce their carbon footprint or aid in ecosystem regrowth.

The platform has a governance structure that lets token owners suggest and decide on adjustments to the way the network runs and develops. As a result, decisions can be made decentralized and the network will develop in a way that is consistent with the community's beliefs.

Conclusion

In conclusion, cryptocurrency initiatives are becoming cutting-edge responses to the battle against climate change. These projects use blockchain technology to encourage carbon emission reductions, track them, aid in ecosystem regeneration, and offset their carbon footprints. These ventures could have a big impact since they offer creative methods to finance environmentally friendly projects. These initiatives show the potential of technology to support international efforts to lessen the effects of climate change.

Also, check out 3 eco-friendly blockchains which revolutionize the industry!

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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