Systems | Development | Analytics | API | Testing

The manufacturing industry can benefit from AI, data and machine learning to advance manufacturing quality and productivity, minimize waste and reduce costs. With ML, manufacturers can modernize their businesses through use cases like forecasting demand, optimizing scheduling, preventing malfunctioning and managing quality. These all significantly contribute to bottom line improvement.

2025, developers use advanced tools and strategies to debug apps running in distributed systems, microservices, and modern frameworks like Flutter, React, and SwiftUI. Here’s a quick summary of the best practices to make remote debugging faster and more secure.

Modern reporting tools like Tableau and Power BI have transformed how end users visualize and analyze data. But for developers and analysts relying on REST APIs to connect these platforms to their data sources, frustrations often mount. From inconsistent data formats to performance bottlenecks, REST APIs—while powerful—weren’t built to handle the complex, high-volume demands of modern BI environments.

I'm commonly asked by customers for advice on how they can build a good platform cross-charging model for their organization. And my gut reaction is nearly always "don't." We'll come back to why I think that later, but first let's look at what cross-charging means, why you might want it, and how it can be designed.

As AI progresses to shape the future of software development, platforms such as Claude 3.5 Sonnet are making significant strides as programming powerhouses when it comes to coding, debugging, and testing. Created by Anthropic, Claude 3.5 Sonnet has impressed with its streamlined coding process, outstanding reasoning potential, and outstanding context memory.

Artificial Intelligence (AI) is no longer a futuristic ambition — it’s the strategic reality of today’s most competitive Independent Software Vendors (ISVs). In our recent Seamless Intelligence: Real-World AI Success Stories from Innovative ISVs webinar, I broke down how AI is transforming the ISV landscape, offering a blueprint for how organizations can move beyond dashboards, and into the age of predictive and generative intelligence.

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

Learn the latest best practices for knowing which mobile devices to test on, plus our recommended devices to test on by region and business size.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.

JSON (JavaScript Object Notation) is a fundamental component of modern web development. Its simplicity and readability have made it a universal data interchange format, used across a wide range of industries and applications. The straightforward structure of JSON, which is both human-readable and machine-parseable, has contributed to its widespread adoption.