Systems | Development | Analytics | API | Testing

The rise of AI agents has reshaped software economics; businesses have been increasingly adopting them for efficiency, scale, and delivering values faster. However, pricing them has remained a hard problem. By the established norms, you would tie cost to headcount or access, but that doesn’t fit; traditional methods misalign with how agents deliver value. And newer approaches often create more confusion than clarity.

AI coding tools like ChatGPT, GitHub Copilot, and Claude have completely changed how we write software. From humble beginnings where non-AI-enabled code assistants made intelligent code suggestions, like Intellisense, the latest agentic tools can generate entire functions, suggest optimal algorithms, and even scaffold complete applications in minutes. However, as any developer who’s worked with AI-generated code knows, the output isn’t always perfect.

Yes, you’ve heard it all before: the frequency of cyberattacks and their devastating aftermath, organizations’ gaps in protecting sensitive data, and the financial consequences of not complying with GDPR and the likes. I am not here to share any old news. But there is a risk that is not discussed frequently enough in the news. And it should be. How often do you suppose data in non-production environments is compromised or fails compliance audits?

To use synthetic test data or to use test data masking — that is the question. But the answer may not be what you expect. Before we dive into that, what’s happening in today’s business landscape that’s prompting the question around synthetic vs. masking? Delivering high-quality applications at lightning speed is expected in today’s CI/CD world. Fast time-to-market is at odds with security and compliance requirements.

Imagine that you need to know the exact location of the train that you’re about to catch. Trains can be equipped with IoT sensors that can publish location coordinates via Kafka streams in real time. You will be using a mobile application to view a train’s location. Mobile applications and web applications are compatible with WebSocket, a web-friendly protocol, but the location data is received via a Kafka stream. How do we convert the real-time Kafka feed into a real-time WebSocket feed?

Want to secure your OT systems and share data safely? NIST 800-82 and ISA-62443 are two key frameworks that help organizations manage operational technology (OT) security and data governance. Here's what you need to know.

Learn how to connect DreamFactory to SAP HANA on-premise and get access to HANA and other SAP ERP’s on HANA with a REST API connector. Instantly auto-generate secure REST APIs, integrate with apps, BI dashboards, and AI pipelines—without custom coding.

The education sector is undergoing one of the fastest digital transformations in history. By 2025, the global eLearning market is projected to surpass $400 billion, with mobile learning alone expected to grow by over 20% annually. From K-12 to higher education and corporate training, digital platforms are now the backbone of how students learn, teachers teach, and institutions operate.

The Centers for Medicare & Medicaid Services (CMS) finalized the CMS-0057-F Interoperability and Prior Authorization Rule, which marks a pivotal shift in U.S. healthcare, requiring payers, providers, and health IT vendors to enable seamless data exchange through FHIR APIs. The rule is designed to reduce administrative burden, improve care coordination, and empower patients with timely access to their health information.