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10 Essential Monitoring Best Practices for DevOps & Developers

Effective monitoring is crucial for maintaining robust application performance, proactive issue detection, and efficient resource management. This guide covers ten essential monitoring best practices that developers and DevOps teams can use to improve observability and keep applications running smoothly. By following these practices, teams can ensure systems are resilient, secure, and provide a seamless user experience.

What's New in Loadero (October 2024)

The month of October has brought with it multiple notable changes, such as the ability to filter tests by their labels, as well as use UUID generation packages in Loadero test scripts. Additionally, the Nightwatch.js built-in command saveScreenshot() can now be used in Loadero scripts. Read about each of these changes in detail below!

SwiftUI Grid: Learn to build complex layouts

In this article we’ll look at Grids in SwiftUI. We’ll see what they are, how they are used, and explain tricks for how to use them in our SwiftUI app. This is a technical article that is heavily code-based. But we’ll aim to be simple and concise, so you can learn something new regardless of your technical level.

How to Align Your Sales and Marketing Teams in B2B in 2025

Since sales and marketing both drive customers down the pipeline, it’s no wonder that the two departments often work together. When we surveyed more than 170 companies about their sales and marketing alignment, the majority told us that sales have at least some influence on marketing. About 45% of the respondents reported that their sales have a significant influence on their marketing.

Mastering Developer Productivity Analytics: A Guide to Boosting Team Efficiency

Developer productivity is ultimately a measure of the efficiency and effectiveness with which software developers produce high-quality code and complete projects. Where this becomes a bit more complicated is in the relationships between those efforts and the measurements that reflect them.

Overcoming Challenges in AI Adoption

AI is no longer just a buzzword – it’s the driving force behind the next wave of innovation in the software industry. Companies that embrace AI today are automating tasks, boosting efficiency, and unlocking new levels of productivity. However, as revolutionary as AI is, adopting it within technical software teams isn’t without its challenges. From skill shortages to navigating ethical dilemmas, businesses face a steep adoption curve.

QA Revolution: Flatten the Curve and Balance Cost, Quality, and Speed

The world of quality assurance (QA) has changed, and there’s no turning back. Digital transformation is driving the need to produce high-quality software faster, while also keeping costs under control. The old idea of choosing between speed, cost-efficiency, or quality? That’s out the window. Today, you need all three to succeed, and they’ve got to work together.

Engineering Data Management for Data Analysts: A Practical Guide

Engineering data management is a key skill for data analysts who handle complex datasets across engineering systems. This field involves processes for managing, organizing, and optimizing data generated by engineering teams, such as IoT device data, operational metrics, and manufacturing details.