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-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!

The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Tomasz Dybowski

04 Mar 2025
The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Introduction

Web3 backend development is essential for building scalable, efficient and decentralized applications (dApps) on EVM-compatible blockchains like Ethereum, Polygon, and Base. A robust Web3 backend enables off-chain computations, efficient data management and better security, ensuring seamless interaction between smart contracts, databases and frontend applications.

Unlike traditional Web2 applications that rely entirely on centralized servers, Web3 applications aim to minimize reliance on centralized entities. However, full decentralization isn't always possible or practical, especially when it comes to high-performance requirements, user authentication or storing large datasets. A well-structured backend in Web3 ensures that these limitations are addressed, allowing for a seamless user experience while maintaining decentralization where it matters most.

Furthermore, dApps require efficient backend solutions to handle real-time data processing, reduce latency, and provide smooth user interactions. Without a well-integrated backend, users may experience delays in transactions, inconsistencies in data retrieval, and inefficiencies in accessing decentralized services. Consequently, Web3 backend development is a crucial component in ensuring a balance between decentralization, security, and functionality.

This article explores:

  • When and why Web3 dApps need a backend
  • Why not all applications should be fully on-chain
  • Architecture examples of hybrid dApps
  • A comparison between APIs and blockchain-based logic

This post kicks off a Web3 backend development series, where we focus on the technical aspects of implementing Web3 backend solutions for decentralized applications.

Why Do Some Web3 Projects Need a Backend?

Web3 applications seek to achieve decentralization, but real-world constraints often necessitate hybrid architectures that include both on-chain and off-chain components. While decentralized smart contracts provide trustless execution, they come with significant limitations, such as high gas fees, slow transaction finality, and the inability to store large amounts of data. A backend helps address these challenges by handling logic and data management more efficiently while still ensuring that core transactions remain secure and verifiable on-chain.

Moreover, Web3 applications must consider user experience. Fully decentralized applications often struggle with slow transaction speeds, which can negatively impact usability. A hybrid backend allows for pre-processing operations off-chain while committing final results to the blockchain. This ensures that users experience fast and responsive interactions without compromising security and transparency.

While decentralization is a core principle of blockchain technology, many dApps still rely on a Web2-style backend for practical reasons:

1. Performance & Scalability in Web3 Backend Development

  • Smart contracts are expensive to execute and require gas fees for every interaction.
  • Offloading non-essential computations to a backend reduces costs and improves performance.
  • Caching and load balancing mechanisms in traditional backends ensure smooth dApp performance and improve response times for dApp users.
  • Event-driven architectures using tools like Redis or Kafka can help manage asynchronous data processing efficiently.

2. Web3 APIs for Data Storage and Off-Chain Access

  • Storing large amounts of data on-chain is impractical due to high costs.
  • APIs allow dApps to store & fetch off-chain data (e.g. user profiles, transaction history).
  • Decentralized storage solutions like IPFS, Arweave and Filecoin can be used for storing immutable data (e.g. NFT metadata), but a Web2 backend helps with indexing and querying structured data efficiently.

3. Advanced Logic & Data Aggregation in Web3 Backend

  • Some dApps need complex business logic that is inefficient or impossible to implement in a smart contract.
  • Backend APIs allow for data aggregation from multiple sources, including oracles (e.g. Chainlink) and off-chain databases.
  • Middleware solutions like The Graph help in indexing blockchain data efficiently, reducing the need for on-chain computation.

4. User Authentication & Role Management in Web3 dApps

  • Many applications require user logins, permissions or KYC compliance.
  • Blockchain does not natively support session-based authentication, requiring a backend for handling this logic.
  • Tools like Firebase Auth, Auth0 or Web3Auth can be used to integrate seamless authentication for Web3 applications.

5. Cost Optimization with Web3 APIs

  • Every change in a smart contract requires a new audit, costing tens of thousands of dollars.
  • By handling logic off-chain where possible, projects can minimize expensive redeployments.
  • Using layer 2 solutions like Optimism, Arbitrum and zkSync can significantly reduce gas costs.

Web3 Backend Development: Tools and Technologies

A modern Web3 backend integrates multiple tools to handle smart contract interactions, data storage, and security. Understanding these tools is crucial to developing a scalable and efficient backend for dApps. Without the right stack, developers may face inefficiencies, security risks, and scaling challenges that limit the adoption of their Web3 applications.

Unlike traditional backend development, Web3 requires additional considerations, such as decentralized authentication, smart contract integration, and secure data management across both on-chain and off-chain environments.

Here’s an overview of the essential Web3 backend tech stack:

1. API Development for Web3 Backend Services

  • Node.js is the go-to backend runtime good for Web3 applications due to its asynchronous event-driven architecture.
  • NestJS is a framework built on top of Node.js, providing modular architecture and TypeScript support for structured backend development.

2. Smart Contract Interaction Libraries for Web3 Backend

  • Ethers.js and Web3.js are TypeScript/JavaScript libraries used for interacting with Ethereum-compatible blockchains.

3. Database Solutions for Web3 Backend

  • PostgreSQL: Structured database used for storing off-chain transactional data.
  • MongoDB: NoSQL database for flexible schema data storage.
  • Firebase: A set of tools used, among other things, for user authentication.
  • The Graph: Decentralized indexing protocol used to query blockchain data efficiently.

4. Cloud Services and Hosting for Web3 APIs

When It Doesn't Make Sense to Go Fully On-Chain

Decentralization is valuable, but it comes at a cost. Fully on-chain applications suffer from performance limitations, high costs and slow execution speeds. For many use cases, a hybrid Web3 architecture that utilizes a mix of blockchain-based and off-chain components provides a more scalable and cost-effective solution.

In some cases, forcing full decentralization is unnecessary and inefficient. A hybrid Web3 architecture balances decentralization and practicality by allowing non-essential logic and data storage to be handled off-chain while maintaining trustless and verifiable interactions on-chain.

The key challenge when designing a hybrid Web3 backend is ensuring that off-chain computations remain auditable and transparent. This can be achieved through cryptographic proofs, hash commitments and off-chain data attestations that anchor trust into the blockchain while improving efficiency.

For example, Optimistic Rollups and ZK-Rollups allow computations to happen off-chain while only submitting finalized data to Ethereum, reducing fees and increasing throughput. Similarly, state channels enable fast, low-cost transactions that only require occasional settlement on-chain.

A well-balanced Web3 backend architecture ensures that critical dApp functionalities remain decentralized while offloading resource-intensive tasks to off-chain systems. This makes applications cheaper, faster and more user-friendly while still adhering to blockchain's principles of transparency and security.

Example: NFT-based Game with Off-Chain Logic

Imagine a Web3 game where users buy, trade and battle NFT-based characters. While asset ownership should be on-chain, other elements like:

  • Game logic (e.g., matchmaking, leaderboard calculations)
  • User profiles & stats
  • Off-chain notifications

can be handled off-chain to improve speed and cost-effectiveness.

Architecture Diagram

Below is an example diagram showing how a hybrid Web3 application splits responsibilities between backend and blockchain components.

Hybrid Web3 Architecture

Comparing Web3 Backend APIs vs. Blockchain-Based Logic

FeatureWeb3 Backend (API)Blockchain (Smart Contracts)
Change ManagementCan be updated easilyEvery change requires a new contract deployment
CostTraditional hosting feesHigh gas fees + costly audits
Data StorageCan store large datasetsLimited and expensive storage
SecuritySecure but relies on centralized infrastructureFully decentralized & trustless
PerformanceFast response timesLimited by blockchain throughput

Reducing Web3 Costs with AI Smart Contract Audit

One of the biggest pain points in Web3 development is the cost of smart contract audits. Each change to the contract code requires a new audit, often costing tens of thousands of dollars.

To address this issue, Nextrope is developing an AI-powered smart contract auditing tool, which:

  • Reduces audit costs by automating code analysis.
  • Speeds up development cycles by catching vulnerabilities early.
  • Improves security by providing quick feedback.

This AI-powered solution will be a game-changer for the industry, making smart contract development more cost-effective and accessible.

Conclusion

Web3 backend development plays a crucial role in scalable and efficient dApps. While full decentralization is ideal in some cases, many projects benefit from a hybrid architecture, where off-chain components optimize performance, reduce costs and improve user experience.

In future posts in this Web3 backend series, we’ll explore specific implementation details, including:

  • How to design a Web3 API for dApps
  • Best practices for integrating backend services
  • Security challenges and solutions

Stay tuned for the next article in this series!