How to develop secure and optimized blockchain smart contracts? – 5 rules | Nextrope Academy

Paulina Lewandowska

10 Oct 2022
How to develop secure and optimized blockchain smart contracts? – 5 rules | Nextrope Academy

Why is the security of smart contracts important?

Smart contracts are a major part of applications based on blockchain technology. In the development process of smart contracts, we should maintain the highest security standards because of factors such as:

  • in many systems, they are responsible for the most critical functionality, the incorrect operation of which can be associated with a number of very unpleasant consequences, including irreversible loss of funds, a logical error ruining the operation of the entire application/protocol,
  • a smart contract that has already been published on the web cannot be modified. This feature means that bugs and vulnerabilities that are diagnosed after the contract is launched productionally cannot be fixed. (There is an advanced technique to create "upgradeable contracts," which allows the contract logic to be modified later, but it also has a number of other drawbacks and limitations that do not relieve the developer from writing secure code. For the purposes of this article, we will skip a detailed analysis of this solution).
  • The source code of most contracts is publicly available. It is good practice to publish the source code in services such as Etherscan which significantly increases the credibility of the application data or defi protocols. However, making the code publicly available entails that anyone can verify such code for security, and use any irregularities to their advantage.

Learning to write secure smart contracts is a process that requires learning many advanced aspects of the Solidity language. In this article, we will present 5 tips to simplify this process and secure our software from the most common mistakes.

1. Accurate testing of smart contracts

The first, and at the same time the most important factor that allows us to verify that our contract works properly is writing automated tests. The testing process usually allows us to reveal various security gaps or irregularities at an early stage of development. Another advantage of automated tests is protection against code regression, i.e. a situation when during implementation of new functionalities bugs are created in previously written code. In such tests we should check all possible scenarios, 100% code coverage with tests should not be a goal in itself, but only a measure to help us make sure that tests scrupulously check every method on our contract.

2. Configuration of additional tools

It is worthwhile to make use of tools that are able to measure and check the quality of the software we provide. Tools you should use in your daily work are:

  • A plugin for measuring code coverage e.g. solidity-coverage. Expanding on the thought from the first point that code coverage should not be an end in itself, it is nevertheless worth having such analytics in the testing process. By analyzing code coverage with tests, we are able to easily see which code fragments require us to write additional tests.
  • Framework for static code analysis e.g. slither, mythril. These are tools that, with the help of static analysis, are able not only to point out places in our code where a vulnerability exists, but also to offer a number of tips. Following these tips can improve not only the security, but also the quality of our software.

3. Openzeppelin smart contract library

There are many libraries and ready-made contracts that have been prepared for later use by developers of blockchain applications. However, each of these libraries needs to be verified before use to see if it has any vulnerabilities. The most popular library at the moment is openzeppelin. It is a collection of secure, tested smart contracts used in many of DeFi's most popular protocols such as uniswap. It allows us to use the most commonly used implementations of ERC (Ethereum Request For Comments) standards and reusable contracts.

The library has a large range of components that can be used to implement the most popular functionalities on the smart contract side. I will give two applications of the library as examples. However, we believe it is worth exploring all the capabilities and contracts that are provided there.

  • Ownable and AccessControl extensions

These extensions allow us to very easily add access control to functions that, according to business requirements, should only be available for execution to authorized addresses. An example from the documentation showing the use of the Ownable extension in practice:

pragma solidity ^0.8.0;
 
import "@openzeppelin/contracts/access/Ownable.sol";
 
contract MyContract is Ownable {
    function normalThing() public {
        // anyone can call this normalThing()
    }
 
    function specialThing() public onlyOwner {
        // only the owner can call specialThing()!
    }
}

As you can see, using the openzeppelin library is not only very easy, but also allows you to write more concise code that other developers can understand.

  • Implementations of the popular token standards ERC-20, ERC-721 and ERC-1155

Many decentralized applications and protocols are based on ERC-20 or NFT tokens. Each token must have an implemented interface that works according to the specification. Implementing a token entirely on your own is associated with a high risk of error, so our token may have security holes or problems with operation on various exchanges and wallets. With the help of openzeppelin library we are able to prepare a standard, functional token and enrich it with the most popular extensions with little effort. A good place to start is the interactive token configurator in the openzeppelin documentation, it allows us to generate token source code that will meet functional requirements and security standards.

4. Using new versions of the Solidity language

An important safety tip is that projects should use new versions of the Solidity language. The compiler requires us to include Solidity version information at the beginning of each source file with a .sol extension:

pragma solidity 0.8.17;

Along with new versions of the language, new features are introduced, but in addition to this, it is also important that fixes are added to various kinds of known bugs. A list of the bugs found in each version can be found in this file. As you can see, with newer versions of the language the number of bugs decreases and is successively fixed.

The language's developers in the official documentation also recommend using the latest version in newly implemented smart contracts:

When deploying contracts, you should use the latest released version of Solidity. Apart from exceptional cases, only the latest version receives security fixes”.

5. Learning from other people's mistakes

An essential factor for delivering secure software is the sheer knowledge of the advanced aspects of the Solidity language, as well as awareness of potential threats. In the past, we have witnessed many vulnerabilities where multi-million dollar assets fell prey to the attacker. Many examples of such incidents can be found on the Internet, along with detailed information on what mistake was made by the developers and how it could have been prevented. An example of the above is an article explaining the "reentrancy" attack, with the help of which the attacker stole $150 million worth of ETH. The list of possibilities for attacking smart contracts is definitely longer, so it is worth reading the list of the most popular vulnerabilities in Solidity. A good way to learn security is also to take on the role of an attacker, for this purpose the Ethernaut service is worth a look. There you will find a collection of tasks involving hacking various smart contracts, these tasks will help consolidate previously acquired security knowledge and learn new advanced aspects of the Solidity language.

Summary

In conclusion, software security of decentralized applications is a very important, but also difficult issue requiring knowledge of not only the programming language itself. Also required are testing skills, a willingness to constantly explore the topic of smart contract vulnerabilities, knowledge of new libraries and tools. This topic is vast and complicated and the above 5 points are just guidelines that can help improve the security of our code and with the associated learning. Also take a look at other articles in the Nextrope Academy series, where we take a closer look at other technical issues.

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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