For quite some time now, React has been the most popular technology. According to Statista, in 2023, React was used by 40.6% of software developers worldwide, which makes it the most popular frontend framework. And we're not talking about some niche React enthusiasts here. It was adopted by such prominent companies as Meta, Netflix, Uber, Airbnb, and many others.

Daily interactions between businesses and customers generate tons of data. It can help companies shift from intuitive to data-driven decision-making when collected and handled right. However, vast raw data datasets are uneasy and barely possible to analyze.

Generative AI has become a hot topic after the ChatGPT release. The reason is mainly because previous groundbreaking technological leaps, such as the wide distribution of personal computers or the Internet, could only help realize one's potential. In their turn, generative platforms introduced a dramatic change to what a specific individual can do. Such solutions can not only improve the way you perform routine tasks but also allow you to do things beyond your skill set, such as drawing or writing code.

Implementing a perfect software solution on the first try is impossible. At some point, developers may start suspecting their codebase is a complete mess and doesn't correspond to what they had in their heads at the beginning of the project. It happens often and sometimes forces developers to make urgent code adjustments even before the product is launched, and users begin to find undetected bugs. As Fred Brooks wrote in The Mythical Man-Month, "The management question, therefore, is not whether to build a pilot system and throw it away.

Sometimes, you have to deal with complex or unique entities that exist only in a single copy. Suppose you build an experimental wind turbine that has the potential to revolutionize green energy. The next step is to determine how well different parts interact with each other and find possible ways of improving their efficiency. Say you have some ideas on improving your invention, but building another model to validate your ideas is way too expensive. What could be the best way to solve this problem?

Earlier in our blog, we analyzed the latest changes that were happening in the technology world of 2023 and highlighted top 10+1 technology trends that will shape 2024. The future is so unpredictable that everything may change any day. For example, look at how fast ChatGPT blew up when no one was expecting. In some way, this blow up could be predicted because of the increment of using various Artificial Intelligence and Machine Learning technologies in recent years.

The weather outside is frightful, but the upcoming tech trends are so delightful that no one can resist reading another article about what awaits us in 2024. We completely understand that urge. Therefore, today, we'll share 10 (plus a bonus one) tech trends worth your attention in 2024.

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Often, companies spend a large part of their IT budget on maintaining the legacy applications their businesses rely on. Such apps can still help cope with business processes. Unfortunately, they heavily contribute to IT complexity and may expose the organization to increased costs and countless security risks. For example, at some point, it'll take a lot of work to find a seasoned IT specialist familiar with the technologies in use on a level required to maintain and make changes to the code base.

Tech industry giants spend enormous resources on top-notch solutions development to cover the needs of an average person from a vast user base. However, you're not an average person, and your business's requirements may lay far beyond what an average company in the market needs. Thus, the question may arise about how to expand the functionality of familiar office applications, for example. Rebuilding such complex systems as Microsoft Excel from scratch to access additional functionality is quite an extensive task in terms of time and resources.

The term "quantum computing" may sound like something you usually hear and see in a sci-fi movie. However, the first models of such a machine were introduced in the 1980s, so at least the quantum computer concept is not new. Operating with classical bits, traditional computing systems have served us well for decades, but they encounter limitations when confronted with highly intricate problems.