Model testing plays a vital role in building trust in responsible AI systems by ensuring that algorithms perform reliably and ethically across diverse scenarios before they are deployed. It helps identify potential biases, errors, or unintended consequences that could undermine fairness or safety
As the tech community buzzes with anticipation, Apple’s annual iPhone event looms on the horizon, promising to unveil the much-awaited iPhone 17 lineup alongside an official release date for iOS 26. While the exact date remains under wraps, the release of iOS 26 Public Beta 4 offers an exciting
Artificial Intelligence (AI) has transcended its status as a mere buzzword to become a fundamental driver of innovation across countless industries, reshaping the technological landscape in profound ways. At the core of this transformation lies a powerful new paradigm: Model-as-a-Service (MaaS)
Imagine a world where coding feels as seamless as brainstorming on a shared document, where developers across the globe collaborate in real-time without a hitch, and where AI integrates effortlessly into every keystroke to enhance productivity. This is the ambitious vision driving Zed, an
Imagine a world where entire business units are managed by autonomous AI systems, making decisions and executing tasks without human intervention. A recent survey by a leading global consultancy revealed that 73% of senior leaders envision such a reality within the next few years, but this
Imagine a quality assurance (QA) team scrambling to test a web application across multiple browsers and devices, juggling a dozen disjointed tools, and losing precious hours to setup and context switching. This chaotic reality plagues many software testing professionals, where fragmented workflows
In the fast-evolving landscape of software development, a new trend has taken hold, captivating developers with its promise of speed and simplicity: vibe coding. This approach, driven by artificial intelligence (AI) tools like large language models (LLMs), allows coders to generate code from vague
I'm thrilled to sit down with Anand Naidu, our resident development expert, who brings a wealth of knowledge in both frontend and backend technologies, along with deep insights into various coding languages. With a keen understanding of the evolving landscape of AI-driven DevOps and software supply
Imagine a digital landscape where over 11 million websites operate with seamless, responsive interfaces, delivering user experiences that keep audiences engaged across devices. This is the reality shaped by React, a JavaScript library that has redefined frontend development with its innovative
Imagine a world where an AI can digest an entire software codebase—thousands of lines of code, sprawling documentation, and intricate logic—in one go, offering insights and solutions without losing track of the smallest detail, making it an invaluable tool for developers. This is no longer a
