Systems | Development | Analytics | API | Testing

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.

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.

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.

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.

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.

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.

Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions. The rising integration of artificial intelligence (AI) and machine learning (ML) in many business intelligence (BI) solutions has enabled new innovative approaches in combining natural language techniques with self-service analytics, resulting in helpful NLQ tools.