Low-Code Development: Everything You Need to Know

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Low-Code Development: Everything You Need to Know

low code development

Low-Code Development: Everything You Need to Know

In the past, when we would build software, we would talk about long timelines, large budgets and complex code. But things have shifted, and businesses are demanding faster results, meaning teams need to leverage simpler tools to build applications without completely relying on developers. This dramatic change would not be possible without low-code development. Low code provides a more intelligent way for people to develop applications through visual, straightforward logic and less coding. The hope is that this will translate into shorter timeframes, lower costs, and faster teams. Doesn’t that sound exciting?

So, many companies are using these platforms to build apps for websites, mobiles, and internal use that can be launched without waiting months. Are you interested in finding out more about this technology? Great, because this blog is going to teach you everything you need to know about low-code development! 

Want to modernize your business applications without long development cycles or heavy coding? Contact us to get the needed expertise from Mindpath to turn your low-code strategy into results.

What is Low-Code Development?

Low-code development refers to the simplest and fastest way to build software. Instead of writing long lines of code, developers can now use visual tools to create apps by just clicking, dragging and choosing from ready-made features that are available. This method can help in saving a lot of time and making the whole process easier, even for teams without deep coding experience.  

It provides developers with a very flexible and user-friendly space to build anything starting from small tools to full business apps. It has many features like drag-and-drop editors, built-in templates and easy-to-use interfaces, which make it possible for low-code platforms to design and launch apps quickly. This can contribute in businesses growing faster, working more efficiently and getting better results from their software projects.

Must Read: Low-Code vs. No-Code Development

Don’t know what is a low-code platform? Let’s learn about that too!

A low-code platform is a tool that helps people build software without writing a lot of code. It uses a simple visual interface where users can drag and drop elements to create apps. This makes it much easier for both developers and non-technical users to build digital solutions. These platforms come with ready-made features like templates, forms, and automation tools. They reduce the need for complex coding and speed up the development process.  

An interesting study according to Fortune Business Insights reveals that the global low-code development platform market size was valued at USD 28.75 billion in 2024. It is expected to grow to USD 264.40 billion by 2032, showing a strong CAGR of 32.2% during the forecast period. This rapid growth highlights how more businesses are turning to low-code development platforms for faster and smarter software development. 

Low Code Development Platform Market

Benefits of Low-Code

Low-code is shaping how organizations approach application and software development. By streamlining the process and reducing the need for manual coding, they offer practical advantages for both technical and business teams. Here are some of the key benefits: 

1. Speeds Up App Delivery

Low-code tools come with drag-and-drop features and built-in parts, so building apps is much quicker. Both tech teams and non-tech users can build apps quickly with the help of these pre-built components. This means businesses can launch apps in weeks, not months. 

2. Boosts Team Productivity

With low-code, business teams can build their own apps without always needing help from developers. This means less waiting around and more work getting done. It’s a great way to turn ideas into real solutions quickly. 

3. Makes Updates Simple

Low-code apps are easier to manage and update because they use pre-built parts that work well together. Changes can be made without breaking existing features, which means the business can stay up to date without major disruptions. 

4. Reduces Development Costs

Since low-code tools require less coding, businesses don’t need to hire large teams of developers. Projects take less time to complete, which saves both time and money. This makes it a cost-effective choice, especially when building multiple apps. 

5. Improves Business Agility

Low-code helps businesses respond quickly to changes in the market. When a new tool or feature is needed, it can be built and launched without delay. This keeps companies flexible and ahead of the competition. 

6. Enhances Decision Making

Low-code apps connect with real-time data, giving teams fast access to the insights they need. This allows business leaders to make informed decisions more quickly. Having accurate information often leads to stronger results. 

7. Enable the Use of New Technologies

Low-code makes it easier to add modern features like AI and automation into applications. These advanced tools can be integrated without needing complex coding skills. It helps businesses stay up-to-date and competitive in a fast-moving digital world. 

Curious how low-code platforms are integrating cutting-edge technologies like artificial intelligence to build smarter apps? Explore the latest AI trends to see how your business can leverage automation and intelligence for more innovative solutions.

Now let us have a quick look at the features of low-code: 

Key Features of Low-Code Development

Low-code stands out because of the powerful features it brings to the table. These features make it easier to build, customize, and manage applications with less effort. Let’s take a look at what makes low-code so efficient and user-friendly: 

1. Simple Drag-and-Drop Design

The drag-and-drop feature is one of the most useful parts of low-code. It lets users add buttons, fields, and other elements by simply placing them on the screen. This is helpful for both business users and developers who want to build apps quickly as it saves time and keeps things organized. 

2. Easy Visual Tools

Low-code platforms come with visual tools that help show how an app works in a way that everyone can understand. These tools use built-in blocks to represent processes and data clearly. Whether someone is a beginner or an experienced developer, the visual approach is helpful. It makes the development process simple and easy to follow. 

3. Ready-to-Use Templates

Many parts of an app are already built into the platform and can be reused. These pre-made modules make it faster to build new apps without starting from scratch. Teams can also save and share parts of their apps with others, which improves teamwork and speeds up future projects. 

4. Model-Based Development

Model-based development helps users create apps faster by using visual flows and logic. It allows business teams to build what they need by following models they already understand. This makes the process smoother, especially for those who are not expert coders. A good low-code platform supports this method, so users can focus on logic, not code. 

5. Cross-Platform Accessibility

Low-code makes it easy to build apps that work on different devices like phones, tablets, and desktops. This means teams can use the app from anywhere, whether in the office or on the move. It also helps companies stay connected with their teams. A flexible app is always more useful in today’s fast-paced work environment. 

Cross-platform apps are more powerful when they leverage AI to engage users intelligently. Check out these real-life AI chatbot examples to see how automation and smart interactions can enhance your app’s reach and performance.

Want to Boost Your Business Growth with Low-Code?

Low-code development is changing the way software is built by making it faster and easier. It helps teams create apps quickly using simple visual tools instead of writing complex code. This approach is helping businesses stay flexible and move ahead in a fast-changing world. 

At Mindpath, we offer powerful application development services using low-code platforms. Whether it’s for internal tools or customer-facing apps, our team makes development smooth and affordable. Let us help turn your ideas into working solutions faster than ever before. 

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ReactJS Developer Hiring Costs in New York
Hiring a ReactJS developers in New York involves several pricing considerations, including expertise level, project complexity, and engagement approach. The cost of employing a freelancer, in-house developer, or outsourcing to an agency varies greatly. Understanding these aspects allows you to efficiently manage your budget and select the best staff for your project. Here’s an overview of the major factors that determine the cost of hiring ReactJS developers in New York.

1. Experience Level

The cost of hiring a ReactJS developer in New York varies according to their expertise level. Entry-level developers often charge between $60 and $80 per hour, making them an excellent alternative for modest tasks. Mid-level developers, with greater hands-on expertise, charge between $80 and $120 per hour. Senior developers, who have substantial experience with React and complicated application development, may charge between $120 and $180 an hour. While senior developers are paid more, they deliver high-quality, scalable, and optimized solutions that save time and money in the long term. Choosing the appropriate expertise level is determined by your project’s objectives and budget.

2. Engagement Model

The engagement model you choose affects the price of hiring a ReactJS developer in New York. Freelancers charge between $50 to $120 per hour, providing flexibility but less consistency for long-term work. In-house developers make $90,000 to $150,000 per year, offering specialized assistance but incurring additional expenses like perks and office space. Agencies charge $100 to $200 per hour and provide a team of professionals for end-to-end development. Each model has advantages and disadvantages, so firms should evaluate project duration, complexity, and money before making a selection.

3. Project Complexity

The complexity of your ReactJS project has a direct influence on development expenses. Simple applications, such as basic web pages, cost between $5,000 and $15,000. Medium-complexity projects, such as bespoke dashboards or eCommerce websites, cost between $20,000 and $50,000. Highly complicated programs, such as business software or AI-powered platforms, might cost more than $100,000 owing to additional functionality, API interfaces, and security needs. More sophisticated projects necessitate developers with greater skill, raising expenses. Identifying your project’s complexity allows you to budget more wisely while also hiring the proper ReactJS talent for a scalable and efficient solution.

4. Location and Market Demand

The location and market demand in New York have a substantial impact on recruiting expenses. As a tech hub, New York has a strong demand for ReactJS engineers, resulting in higher wage expectations. Local developers often make $90,000 to $150,000 per year in-house, or $100 to $200 per hour as top freelancers and agencies. To cut expenses, several organizations are exploring recruiting remote workers from Eastern Europe or Latin America, where competent engineers are more affordable. While local developers make cooperation simpler, employing experienced remote workers can be a cost-effective choice without sacrificing quality.

Looking for expert ReactJS developers in New York? Mindpath, offers top-tier talent for your project, whether you need freelancers, in-house experts, or a full development team. We deliver cost-effective, high-quality solutions tailored to your needs. Let’s build something great together!

Contact us today

Discover the ultimate guide to hiring Node.js developers in London. Learn about the skills, benefits, and cost factors involved in finding the right talent for your project.
Web Scraping with Node.js

Have you ever needed data from a website but didn’t know how to get it without manually copying everything? What if there was a way to automate this process? Web scraping with Node.js makes it possible! Curious to learn how? In this blog, we will explore the basics of web scraping with Node.js which can be the solution to questions.  

Web scraping is the process of utilizing a specialized program to mechanically collect information from websites. Instead of manually copying and pasting data, web scraping allows you to instantly gain pricing, photos, and news items from the internet. It operates by making requests to websites, capturing the necessary data, and organizing it in an understandable format. This is very useful when you need to collect a large amount of information from many pages. 

Web scraping with Node.js involves a way of automatically gathering data from websites. Node.js simplifies this process using tools like Axios for making queries and Cheerio for reading HTML and extracting information such as text or images. This method allows you to collect data fast without having to do it manually, making Node.js an excellent choice for web scraping. 

So, let’s dig in to explore the basics of web scraping with Node.js! 

How to Conduct Web Scrapping with NodeJS

1. Set Up a NodeJS Project

First, create a folder for your web scraping project and name it anything you like. Then, enter this folder and use npm to create a new Node.js project. This will generate a file called package.json that stores your project’s information as well as dependencies. Once your project is set up, make a primary file, such as index.js, in which you will write your scraping code. To make sure everything works properly, include a simple script in package.json to run your main file. Run the script to ensure that your Node.js configuration is right, and you’re ready to start developing web scraping programs! 

2. Install Axios and Cheerio

Now it’s time to install the Node.js web scraping tools. We will use two libraries: Axios for obtaining web pages and Cheerio for simply reading and extracting data from HTML. You may evaluate the target page to determine which libraries are most suited to your web scraping needs. Right-click an empty space on the page, pick “Inspect,” and navigate to the Network tab, especially the Fetch/XHR section. If this section is mainly empty, it means that the data you need is available directly in the page’s HTML, without the need for JavaScript or a headless browser. 

3. Download Your Target Website

Connect to the website you wish to scrape via Axios. This entails requesting the website’s URL to retrieve its HTML content. Since many websites have measures to prevent scraping, include a proper User-Agent header to make your request appear from a legitimate web browser. To do this, include the User-Agent header when delivering the request using Axios. This will allow you to avoid being blocked by the website. Once you’ve received the website’s HTML, you’ll be able to extract the necessary information. Next, examine the HTML of the target page to determine how to locate and extract the precise data you’re interested in. 

4. Examine The HTML Page

To find the data you want to scrape, first inspect the target website’s HTML code. For example, if you want to learn more about industry on a page, right-click on an item and pick “Inspect” to get the DevTools. This will display the HTML code of the selected element. Look at the HTML structure to see how the data is organized. Examine the tags and CSS classes used on the components you wish to scrape. This information will assist you in writing the appropriate CSS selectors for extracting the data. 

5. Use Cheerio to Select HTML Elements

Cheerio can be used to extract data from a webpage’s HTML content. First, load the HTML you acquired via Axios into Cheerio. This configures Cheerio so you can start selecting items from the page. Cheerio lets you select HTML components with CSS selectors, like how jQuery works. For example, you can choose components based on their class or ID. You may also reduce your options by looking for components within other elements. Once you’ve selected the items you’re interested in, you may loop through them to extract the information you want. This approach involves utilizing Cheerio’s methods to filter and iterate over the components based on your scraping objectives. 

6. Extract Data from the Target Webpage Using Cheerio

After you’ve configured Cheerio and selected your HTML components, the following step is to extract the necessary data. Begin by building a structure, such as an array, to store your scraped data. Use Cheerio to extract precise characteristics about each element you’ve selected, such as attributes or text content. When extracting data, be sure to remove any unnecessary information and organize the relevant parts into clear, organized objects. These items should then be added to your database structure. If your webpage has many parts or categories of information, repeat the procedure for each section, modifying your selectors and extraction logic as necessary. By the end of this stage, you should have a well-organized collection of data from the webpage that is specific to your scraping goals. 

Web Scrapping with NodeJS at Mindpath

Mindpath provides web scraping services using Node.js to help you obtain crucial information from websites. Node.js allows us to easily download and extract data from web pages. This procedure entails retrieving the website content and then locating the specific data you want, such as text or photos. 

Our team utilizes Axios to obtain the web page and Cheerio to filter through the HTML and extract the necessary information. This enables us to collect and organize information fast and precisely. Whether you need data for research, analysis, or other uses, our web scraping services using Node.js make it simple and effective to obtain the information you want. 

Wrapping Note!

Mastering web scraping with Node.js brings up a plethora of opportunities for quickly obtaining data from websites. Whether you want to extract information for market research, competitive analysis, or just to simplify data collecting, Node.js offers a robust and user-friendly framework for accomplishing these objectives. You may automate data extraction by using technologies like Axios for downloading web pages and Cheerio for parsing HTML, which saves time and reduces manual work. 

Mindpath uses Node.js to create accurate and organized web scraping solutions that are suited to your specific needs. Our experience guarantees that you receive the necessary data fast and properly, allowing you to make intelligent choices and move your initiatives ahead. 

Looking to streamline your data collection?

Mindpath specializes in efficient web scraping with Node.js to help you get accurate insights fast. 

AI programming languages

In today’s digital world, smart phones are able to recognize voices, answer your queries with virtual assistant and can even sort your photos by location or people. All these features may seem like magic but in reality, it’s the power of artificial intelligence. From chatbots to intelligent assistants, AI is revolutionizing the way we live, work and interact. But behind all these changes lies something as crucial as AI, and they are AI programming languages.

Studying these languages is crucial for technology professionals as well as anyone interested in seeing how the future is being designed. These languages can help developers create smart systems that will be able to understand, learn and react just like humans. In this guide, we will take you through everything that you need to know about programming languages for AI. So, let’s dig in! 

Planning to elevate your business and get a personalized AI solution? At Mindpath, we offer AI development services designed to deliver solutions tailored to your business needs.

What are AI Programming Languages?

Artificial intelligence programming languages refers to the tools that are used to create smart computer languages that can think and learn just like humans. These languages help developers to build systems that can understand speech, read data, and even help in autonomous driving. Each language comes with its own tools and libraries to carry out these tasks easily. 

In programming with artificial intelligence, it’s helpful to use languages that offer strong support for tasks like data analysis and machine learning. For example, Python is well-known because it has helpful libraries such as TensorFlow and PyTorch, R is also great when you are working with statistics and data. So, these languages make it easier to build AI systems that can work well and can contribute in solving real-world problems. 

Must Read: Benefits of AI 

List of Top AI Programming Languages

When you are building smart applications, the choice of programming language plays a major role in it. But with so many options out there, it is important to learn about each and decide which one can provide what benefits. The best AI programming languages can offer the features, libraries, and flexibility needed to handle tasks like machine learning, data processing, and automation. Let’s dive deep!

1. Python

Python is one of the top choices for developing smart systems due to the fact that it’s easy and beginner friendly. It’s widely used for training machine learning models, doing data analysis, and building AI tools like chatbots or image recognition apps. Being an AI coding language, Python has numerous strong libraries such as TensorFlow and PyTorch, which make complicated tasks manageable. It also enables rapid testing and development, which is useful in constructing and refining AI models. It is useful for AI in decision-making, where quick iteration on data models is crucial.

2. R

R is an excellent language to use when dealing with lots of data. It makes it easier for individuals to comprehend patterns, generate charts, and analyze results in an easy manner. It is employed by numerous scientists and developers to explore more into data and make intelligent predictions. R is also useful when creating and testing simple machine learning models. With its strong data-handling capabilities, R is particularly valuable for AI in business strategy, helping organizations analyze trends, optimize operations, and make data-driven decisions. R also has numerous tools that assist in making it simpler to verify and compare results. It gets along great with other programming languages as well, so it can be beneficial for other AI projects that require solid data analysis. 

3. Java

Java is a good option for large AI projects, particularly for businesses that require systems to function across different platforms. As an AI language, Java assists programmers in developing intelligent tools that are simple to maintain and expand. It accommodates machine learning, deep learning, and large data tasks through helpful tools and frameworks. Java is also reliable for maintaining things safe and clean, as it has the capability to delete unwanted data automatically.  

4. SQL

SQL is a helpful tool when working with a lot of structured data. It makes it easy to find, manage, and prepare data that can be used to train AI models. Since artificial intelligence needs good-quality data, SQL plays an important role in getting that data ready. It works well with other tools used in AI projects and is great for handling large datasets. As one of the trusted artificial intelligence languages, SQL is simple to use and supports many steps in the AI development process.

Curious to know how AI impacts your industry? Discover the top AI trends that drive innovation and shape the future of business.

5. Scala

Scala is a good choice for building strong AI applications, especially when dealing with big data. It can easily manage large sets of data and is made to work fast and smoothly. Scala also works well with popular tools like Apache Hadoop and Spark, which help developers build and run AI projects more easily. Since it supports multi-tasking and can run many processes at once, Scala is useful for complex AI work. It also works nicely with Java, making it even more flexible. 

6. Julia

Julia is an AI programming language known for its speed and ability to handle complex calculations. It’s great for deep data analysis and training advanced machine learning models. Julia has a user-friendly syntax which is better, making it easier to learn compared to some other languages. It also works well with languages like Python, C, and R, giving developers more flexibility. With powerful libraries, Julia is perfect for building efficient data models and neural networks. 

7. C++

C++ is a powerful language used for AI applications because it offers great performance and uses system resources efficiently. It’s great for running heavy tasks like deep neural networks quickly. Developers often choose C++ for its ability to handle complex jobs, including real-time processing and tasks like computer vision. Its speed and resource management are vital for building sophisticated systems that drive AI transforming the future of technology.

8. Haskell

Haskell is a language that focuses on writing clean and reliable code, making it ideal for AI research and building new algorithms. It is great for prototyping and teaching programming concepts. Many AI developers use Haskell for its strong typing and functional programming style, which ensures precision and reliability. Haskell is also used in roles like AI research, where its features help verify algorithms and improve safety, especially in important fields like healthcare and autonomous vehicles. 

 Important Note: Some programming languages are not the best choice for AI because they don’t have the right tools or are too slow for complex tasks. Here are a few languages that are generally avoided when building AI applications: 

  • PHP: It is mainly used for making websites and doesn’t have strong tools or libraries needed for artificial intelligence and machine learning work. 
  • Ruby: It is easy to read and write, but it runs slowly and doesn’t offer strong support for AI or machine learning tools, which makes it hard to use in real projects. 
  • C: It can be powerful, but it is too low-level for AI and needs manual memory management, which can lead to errors and unstable programs. 
  • Fortran: It is an older language that lacks modern features like dynamic memory and safety, which makes it risky and less useful for today’s AI development. 

Ready to adopt AI programming languages and transform the way websites are built? Read our blog on AI in web development to explore its impact on creating smarter, more dynamic sites.

Curious How AI Languages Are Powering Smarter Technology?

AI programming language is the key to building smart, efficient, and reliable applications. Whether it’s Python for ease, R for data analysis, or Java for large systems, each language has something unique to offer. While some languages lack the tools or speed for AI tasks, the right one can make AI development smoother and more powerful. Understanding these options helps both beginners and experts create systems that truly think, learn, and improve the way we interact with technology. 

At Mindpath, we specialize in Artificial Intelligence development, using the best programming languages like Python, Java, and more to create intelligent solutions that align with your business goals. From automating processes to enhancing user experiences and developing data-driven systems, our expert developers build AI applications tailored to deliver real-world results.