Algorand for Beginners 1 – How to set up a development environment for Algorand? | Nextrope Academy

Paulina Lewandowska

27 Sep 2022
Algorand for Beginners 1 – How to set up a development environment for Algorand? | Nextrope Academy

In this article we start our series of articles that form the Algorand course for beginners. From the series of articles you will learn, among other things, how to set up a development environment for Algorand, how to deploy a smart contract on the Algorand network of your choice, how to write a simple smart contract, and what tools and frameworks you can use to work with the Algorand blockchain.

Algorand is a "green blockchain" launched in 2019, with the overarching goal of solving the blockchain trilemma through transaction speed, security, and a consensus algorithm that ensures full decentralization of the network.

A list of the necessary tools and components:

  • Visual Studio Code
  • Python 3.6 or later
  • pyTeal library and py-teal-sdk
  • Docker Desktop
  • Algorand Sandbox
  • Skeleton project repository

Visual Studio Code installation

Visual Studio Code will be your IDE, with the help of this program you will be able to write application code.

Visual Studio Code (known as VS Code) is a free and open source text editor from Microsoft. VS Code is available for operating systems: Windows, Linux and macOS. Although the editor is relatively lightweight, it includes several advanced features that have made VS Code one of the most popular development environment tools in recent times.

  • Go to https://code.visualstudio.com/download  and download the installation file compatible with your operating system.
  • Install Visual Studio Code on your computer and proceed to the next step in this tutorial.

Installing Python

Python is a general-purpose, high-level interpreted programming language commonly used for web development, data analysis and automation.

One way to write smart contract logic for the Algorand network is with the Python library pyTeal ( https://pyteal.readthedocs.io/en/stable/ ), which allows you to write smart contract logic in python and compile the code into the TEAL code required by the AVM (Algorand Virtual Machine).

  • TEAL is an assembly language syntax for specifying a program that is eventually converted to AVM (Algorand Virtual Machine) byte code.
  • In VS Code, go to the "Extensions" tab used to install add-ons for Our IDE.

In the search bar, type "Python" and install Python extension for VS Code.

The next step is to install the interpreter for Python. This process varies depending on the operating system you are using.

Windows:

  • Download the installer for the language version of your choice from the official Python website https://www.python.org/downloads/ then go through the standard installation process,
  • An alternative to the above method is to install Python from the Microsoft Store, all the latest versions of python are available there.

MacOS:

  • To install the Python interpreter on macOS, we need to use the Homebrew package manager,
  • If Homebrew is already installed, open the command line and enter the command brew install python3.

On Linux distributions, the Python 3.x interpreter is installed by default.

After installing Python, it's a good idea to check that everything went as expected. To see the currently installed version of the interpreter, enter the command at the command line

python -v

Installing Docker Desktop

Docker Desktop is an easy-to-install application for macOS, Linux and Windows environments that allows you to create and share containerized applications and microservices.

Docker Desktop is required by the Algorand sandbox, a toolkit provided by the Algorand developers that is, a must-have, for any Algorand developer no matter what his or her level of expertise. Without Docker Desktop, you won't be able to run the most important tool for Algorand developers.

Installation files for each environment are available here.

After installation, Docker Desktop will start automatically.

Downloading Algorand Sandbox

Algorand sandbox is a set of tools that facilitate communication and interaction with the Algorand blockchain. Components of the sandbox include indexer, goal and algod.

With sandbox you can run betanet, testnet and mainnet in network mode, create tokens, nodes, execute transactions, create wallet addresses, check account balances, or deploy your applications on the network of your choice.

  • Go to https://github.com/algorand/sandbox and copy the link to the sandbox repository
  • Then open the command line on your computer, navigate to the desired location and enter the command git clone <repository address>
  • The sandbox repository will be cloned to your computer and you can start using it right away

Downloading the repository skeleton

To get started with Algorand easily enough, you should use the project skeleton, prepared by the Algorand development team.

Running Python (venv)

Python venv is a virtual Python environment in which the Python interpreter, libraries and scripts installed in it are isolated from those installed in other virtual environments and (by default) any libraries installed in "system" Python, i.e. one that is installed as part of the operating system.

To run a virtual Python environment follow a few simple steps:

  • Open the previously downloaded project skeleton in VS Code and start the terminal,
  • Make sure you are in the root directory of the project and enter the command python -m venv venv, the python virtual environment will be created with the name "venv",
  • Then depending on the operating system you are using, enter another command to activate the virtual environment:
  • MacOS: source ./venv/bin/activate
  • Windows: source ./venv/Scripts/activate,
  • To make sure you are working on the correct virtual environment, check that the name of your virtual environment appears before the command line in the terminal.

Installing additional libraries

In the example project in the requirements.txt folder, there are additional libraries that you need to install to start writing code for your application using the pyTeal library.

PyTeal is a Python language library for constructing Algorand smart contracts. It was created as a community project. The main goal of this library is to make writing contracts even easier and more accessible for programmers who prefer programming in Python.

  • To install additional libraries into your virtual environment you need to put is in the project's root directory in the requirements.txt file,
  • The most important libraries that we will use when writing smart contracts for Algorand are pyTeal and py-teal-sdk,
  • Once you have the requirements.txt file enter the following command to start installing additional libraries, pip install -r ./requirements.txt .

Linking your project to a sandbox

When creating your blockchain application, after some time you may need, for example, to deploy it on a network of your choice. For this task you will need the Algorand sandbox, but first you need to properly connect your project to the sandbox by pointing it to the location of Our Project, among other things.

To do this, navigate to the folder where the Algorand sandbox you downloaded earlier is located and follow the instructions below:

  • Open the docker-compose.yml file and in the services.algod section enter the additional volumes key with three additional parameters:
  • -type: bind,
  • source: <path to your project>,
  • target: /data,
  • example in the graphic below:

Starting the Algorand sandbox

To interact with the Algorand blockchain using the sandbox, you must first launch the sandbox container in Docker Desktop application.

To do so, follow the instructions below:

Open the command line on your computer and navigate to the folder of the sandbox you downloaded earlier,

Then enter the command ./sandbox up to start the sandbox container and place it in Docker,

By default, the sandbox will be started with betanet support. To run the sandbox with support for another network enter  ./sandbox up testnet or ./sandbox up mainnet .

Summary

With all the above steps completed, you have a ready-made environment to start working with the pyTeal library. You can now start writing your first smart contract, and deploy it on the network of your choice.

In the next article, we will introduce you to writing the simplest smart contract, along with deploying it on the Algorand network.

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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

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!