7 Free AI Tools that You Have to Know as a Developer

Paulina Lewandowska

07 Feb 2023
7 Free AI Tools that You Have to Know as a Developer

Introduction

There has never been a greater need for skilled developers than there is now, as the AI sector continues to grow. It can be difficult to decide which AI development tools are the most helpful given the variety of tools available. In this post, we'll examine 7 of the most crucial tools for developing AI, guaranteeing that you have the resources necessary to generate creative and useful solutions. From computer vision tools to deep learning libraries, we'll emphasize what makes each tool unique and how it might help your development process. These tools will assist you in achieving your objectives more quickly and effectively, whether you are an experienced AI developer or just getting started.

TensorFlow

A well-liked AI development tool among programmers, academics, and data scientists is called TensorFlow. It is favored for a variety of AI applications because to its adaptable architecture and quick computing capabilities. Python, C++, and JavaScript are just a few of the many programming languages that TensorFlow supports, making it useful for developers of all skill levels. For anyone interested in entering the field of AI development, its extensive documentation, vast community, and broad use in both industry and academics make it a great resource. 

Category: Free

Scikit-learn

The library enables developers to quickly interact with data and create machine learning models by offering seamless connectivity with other well-known Python libraries like NumPy and pandas. Additionally, it provides a user-friendly interface that enables developers with little to no machine learning knowledge to get started and create their models right away. 

Category: Free

Keras

You can use TensorFlow, CNTK, or Theano's power with Keras to develop unique and successful deep learning models. It's like having a sophisticated blueprint at your disposal, enabling you to quickly build cutting-edge neural networks. You may create and experiment with a variety of network designs thanks to the intuitive API, which makes the challenging process of training and assessing models simpler. Along with practical tools for visualizing and saving your models, Keras offers a multitude of pre-processing methods for diverse data formats. Keras provides the flexibility and functionality you need to realize your deep learning vision, regardless of your level of AI development experience.

Category: Free

OpenCV 

OpenCV is a complete computer vision library with a huge selection of image and video processing techniques. It is well suited for usage in a range of industries, including robotics, security, and entertainment since it is highly tuned for real-time computer vision applications. Because the library is open-source, developers can easily adapt the algorithms to meet their own requirements and even contribute to the library's development.

Category: Free

NLTK

NLTK offers a huge variety of pre-processed corpora and lexical resources in addition to a user-friendly interface, making it easier for developers to integrate these resources into projects and saving them time and effort. Tools for complex NLP tasks, such as part-of-speech tagging, parsing, semantic analysis, and coreference resolution, are also available in the collection. The NLTK library is a useful tool for developers who want to create robust and effective NLP applications because of its open-source nature and dedication to continual growth. 

Category: Free

PyTorch

The user-friendly, adaptable, and highly modular design of PyTorch makes it simple for developers to create and test out complex models. Additionally, it works nicely with other well-known Python libraries like NumPy, pandas, and Matplotlib, enabling programmers to deal with data and complete visualization jobs with ease. PyTorch is ideal for use in a variety of practical applications, including computer vision, natural language processing, and reinforcement learning. It is also performance-optimized. PyTorch is a promising and potent tool for developers working in the field of AI and machine learning thanks to its expanding community and backing from industry heavyweights.

Category: Free

Watson Studio

Watson Studio offers data scientists a complete platform for creating and scaling up AI models. Data connections, notebooks, and model builders are just a few of the many tools available for data preparation, modeling, and deployment. For data scientists wishing to deploy their models in production, Watson Studio also connects with other IBM Cloud services like Watson Machine Learning and Watson Knowledge Catalog. Additionally, the platform offers a flourishing user community where members can work together, share resources, and get access to a plethora of instructional materials to aid them in their endeavors to develop AI. Data scientists may expedite their work and produce effective AI solutions with the help of the powerful and user-friendly Watson Studio platform. 

Category: Both freemium and premium plans.

Conclusion

In conclusion, the demand for talented engineers is greater than ever since as the adoption of AI is increasing and becoming more visible in many industries. In this quickly changing area, the capacity to develop novel and practical solutions is crucial. Having access to the appropriate tools is essential for success, regardless of your level of expertise in artificial intelligence (AI). You'll be able to streamline your work and reach your full potential as a cutting-edge AI developer with the variety of resources at your disposal. So get ready to enter into the fascinating realm of AI and start developing solutions that have the potential to transform it.

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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