Tech

  The C programming language has for some time been a foundation of programming improvement, fueling all that from working frameworks to implanted gadgets. As man-made reasoning (artificial intelligence) upsets programming, incorporating computer based intelligence improvement devices with C has opened unrivaled efficiency and productivity. This blog investigates how devices like Copilot, Kite, and artificial intelligence driven compilers are reshaping C programming. The Role of AI in C Programming Artificial intelligence instruments smooth out work processes, mechanize tedious assignments, and improve blunder discovery, making programming quicker and more productive. For a language like C, which requires fastidious thoughtfulness regarding sentence structure and memory the board, man-made intelligence improvement devices are significant. Key Areas Where AI Development Tools Enhance C Programming 1. Code Assistance with AI Tools AI tools such as GitHub Copilot and Kite provide cont...

In the present computational world, productivity is critical. With the ascent of multi-center processors, equal and multithreaded programming in C has become fundamental to use equipment abilities completely. This approach engages engineers to compose programs that execute various undertakings at the same time, further developing rate, asset use, and responsiveness. In this blog, we'll plunge into the ideas, procedures, and viable executions of equal and multithreaded programming in C. We'll investigate why it makes a difference, how to accomplish it, and the instruments you really want to succeed. What is Parallel Programming in C? Equal programming alludes to partitioning a computational undertaking into more modest sub-errands that run simultaneously on various processors or centers. By utilizing numerous centers, equal programming lessens execution time and increments throughput. C is one of the most remarkable dialects for equal programming due to its low-level command ove...

  Artificial Intelligence (AI) and Machine Learning (ML) are no longer buzzwords; they are the engines driving innovation across industries. From personalized recommendations to self-driving cars, the impact of AI is evident. But behind these transformative applications are frameworks —powerful tools that make the complex process of AI and ML development manageable, efficient, and scalable. AI frameworks provide pre-built libraries, APIs, and tools that developers and researchers use to build, train, and deploy machine learning models. They simplify complex mathematical computations, handle hardware optimization (like GPUs or TPUs), and provide debugging and visualization tools. Let’s dive into the most popular frameworks, emerging technologies, and the future of AI development. Key Frameworks in AI and Machine Learning 1. TensorFlow TensorFlow is an open-source machine learning framework developed by Google. It helps build, train, and deploy machine learning and deep learning mod...

  Introduction Selenium has become an essential tool in the field of automation testing, widely used for verifying web applications. With companies prioritizing automation skills in their hiring, mastering Selenium is crucial for candidates aiming to land a role in QA automation. This guide highlights key interview topics, common Selenium interview questions, and tips to help you showcase your expertise confidently. Understanding Selenium and Its Components A strong foundation in Selenium basics is essential. Be prepared to answer questions about the structure and purpose of Selenium: Selenium IDE : Used primarily for recording and playback, IDE is ideal for beginners and helps create basic automation scripts quickly. Selenium WebDriver : The most popular tool, WebDriver enables direct interaction with web browsers and allows testers to write complex scripts in multiple languages like Java, Python, and C#. Selenium Grid : Grid allows parallel execution of tests across various envir...