Although GoMock is one of the most popular ways to create mocks in Golang, it’s important to understand the broader landscape of mocking methods in the language to fully leverage its potential value in testing. Mocking is a powerful technique that allows developers to isolate their code, improve test reliability, and ultimately enhance overall code quality.

The healthcare system is always on the cutting edge of new technologies—but it's also one of the most heavily breached industries. The HIPAA Journal reported that the largest healthcare information data breach between 2009 and 2024 occurred in 2015 when hackers illegally obtained the health records of nearly 79 million Anthem Health subscribers1.

Learn about some of the top mobile testing tools and frameworks available, including how you can leverage them to improve your app quality and deliver exceptional customer experiences.

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A little-known fact about me is my love for scuba diving. There’s nothing quite like the feeling of being so close to nature, exploring the mysteries of the underwater world. Every chance I get, I dive into the ocean’s depths, uncovering hidden wonders that most people never get to see. That got me thinking about the similarities between data and the ocean. Just like the vast, uncharted waters of the sea, the world of data holds incredible potential—if you know how to navigate it.

Large Language Models (LLM) are changing the way developers and QA engineers solve problems. They allow for quicker code generation, debugging, and automated testing, reducing development time by up to 40%. This shift has prompted 67% of senior IT leaders to focus on generative AI, with 33% planning to make it a top priority within the next 18 months. However, while LLM models offer immense potential, understanding how to get the most out of them while maintaining quality is important.

According to Gartner, 87% of organizations are classified as having low business intelligence and analytics maturity, meaning they struggle to extract value from their data. This alarming statistic highlights a common struggle—turning raw data into actionable insights. Product teams often find themselves overwhelmed by the sheer volume of information they collect. Extracting meaningful patterns, deciphering user behavior, and predicting market trends from this sea of customer data can seem daunting.

When it comes to managing AI projects, the Command Line Interface (CLI) can be a powerful tool. With ClearML, the CLI becomes an essential resource for creating job templates, launching remote for JupyterLab, VS Code, or SSH development environments, and executing code on a remote machine that can better meet resource needs. Specifically designed for AI workloads, ClearML’s CLI offers seamless control and efficiency, empowering users to maximize their AI efforts.

Spring Boot is an open-source micro framework maintained by a company called Pivotal. It provides Java developers with a platform to get started with an auto configurable production-grade Spring application. With it, developers can get started quickly without losing time on preparing and configuring their Spring application. This post will guide you through understanding what the micro framework is and what it’s used for.