Author: Arunangshu Das

In the dynamic world of software development, where market demands shift overnight, methodologies that foster agility and adaptability are the need of the hour. Adaptive Software Development (ASD) is one such methodology that not only helps teams build robust software but also significantly enhances team collaboration. What is Adaptive Software Development? Adaptive Software Development, as the name suggests, is a framework built on the principles of flexibility and collaboration. It’s rooted in three main phases: Speculate, Collaborate, and Learn. Unlike traditional methodologies that follow a rigid plan, ASD thrives on continuous learning and adapting to change. It shifts the focus…

Software development has come a long way since its early days, evolving not only in technology but also in methodology. From the rigid, linear Waterfall model to the dynamic and flexible Adaptive approaches of today, the journey reflects how the industry has grown to meet ever-changing demands. The Waterfall Model: A Linear Beginning In the 1970s, the Waterfall model was the gold standard of software development. It was straightforward: complete one phase before moving to the next. You’d start with requirements gathering, then move to design, implementation, testing, deployment, and finally, maintenance. This linear approach made sense at the time.…

In the fast-paced world of technology, software development methods must keep up with constant changes in requirements, market demands, and user expectations. Enter Adaptive Software Development (ASD), a methodology designed to thrive in uncertain, high-change environments. Unlike traditional approaches like Waterfall, which rely on rigid plans, ASD embraces fluidity and encourages teams to adapt as they learn more about the problem they’re solving. But what makes ASD so effective? 1. Speculation Over Planning In traditional software development, projects begin with a detailed plan outlining every step, feature, and deadline. However, in adaptive environments, uncertainty reigns supreme. ASD replaces rigid planning…

In the fast-evolving world of software development, agility isn’t just an advantage—it’s a necessity. Agile methodologies have long been the go-to framework for teams striving to deliver high-quality software quickly and efficiently. But as customer expectations grow, technologies advance, and markets shift, one question looms large: Is Agile enough? Enter Adaptive Software Development (ASD), a methodology designed to embrace uncertainty, prioritize learning, and deliver value in a world where change is the only constant. Here’s why Adaptive Software Development isn’t just an evolution of Agile—it’s the future. What Is Adaptive Software Development? Adaptive Software Development, pioneered by Jim Highsmith, is…

In the world of software development, change is the only constant. Requirements evolve, markets shift, and technologies emerge at a breakneck pace. Adaptive Software Development (ASD) is one of the methodologies designed to thrive in this ever-changing environment. It’s not just another buzzword—it’s a mindset that encourages teams to embrace uncertainty and deliver value, even in the face of ambiguity. What is Adaptive Software Development (ASD)? ASD is a flexible software development methodology born out of the need for agility and innovation. Created by James A. Highsmith and Sam Bayer in the 1990s, ASD focuses on adapting to change rather…

Have you ever tried to register for your favorite app, only to see the dreaded message: “This username is already taken”? While it may seem like a small annoyance, checking the availability of a username is a technical marvel when dealing with billions of users. In this article, we’ll discuss three powerful methods to efficiently check username availability: Traditional Database Queries Redis Cache Strategy Bloom Filters: The Memory-Efficient Marvel Not only will we break down how each approach works, but we’ll also dive into practical examples using Node.js—because who doesn’t love hands-on coding? Method 1: The Traditional Database Query Approach…

Deep learning has revolutionized various fields by enabling machines to learn complex patterns from data. Among its many facets, regression stands out as a cornerstone technique, often overshadowed by its classification counterpart. While classification deals with categorizing data points, regression focuses on predicting continuous values, making it indispensable in domains like finance, healthcare, and weather forecasting. What is Regression in Deep Learning? Regression in deep learning involves training a model to predict a continuous value. Unlike classification tasks, which output a label, regression provides a numerical result. For example, predicting house prices, stock market trends, or temperature are classic regression…

The introduction of deep convolutional neural networks (CNNs) has dramatically improved image recognition capabilities. Among the seminal architectures, the VGG (Visual Geometry Group) Network, proposed by Karen Simonyan and Andrew Zisserman in 2014, was a breakthrough. It demonstrated the effectiveness of deep, simple, and uniform layer structures in achieving state-of-the-art performance on the ImageNet dataset. VGG Architecture Overview The hallmark of VGG lies in its simplicity and depth. The architecture systematically increases the depth of the network by stacking small convolutional filters (3×3 kernels) while maintaining a consistent structure across layers. This design enables the network to learn hierarchical features…

Introduction In recent years, large language models have become a crucial part of software development, providing an array of functionalities that enhance user interactions and automate tasks. The Tiny LLaMA 3.2 1B model, a smaller yet powerful variant of the LLaMA series, allows developers to implement advanced capabilities, such as function calling, to improve functionality without the need for extensive computational resources. 1. What is Function Calling in LLaMA Models? Function calling in language models refers to the capability of a model to interact with various functions or APIs during conversation. By doing this, the model can perform specific operations…

Artificial Intelligence (AI) has undergone remarkable advancements in recent years, with deep learning standing at the forefront of these breakthroughs. The journey to create machines that can understand, learn, and perhaps one day emulate human cognition has captivated scientists, researchers, and tech enthusiasts alike. The quest to bridge the gap between human intelligence and artificial intelligence raises fundamental questions: Can machines think like humans? Can AI systems truly understand and respond to our world with the nuance and adaptability that characterizes human cognition? At the core of this discussion is deep learning—a powerful subset of machine learning that has demonstrated…