Systems | Development | Analytics | API | Testing

We’ve all been there. When engineering teams evaluate AI-powered QA tools, the same questions come up again and again. Some are rooted in genuine technical curiosity. Others stem from experiences with earlier-generation tools that earned a healthy dose of skepticism. After hundreds of these conversations, I’ve identified the seven most common misconceptions. Contents Toggle.

Every successful PropTech product combines accurate data with thoughtful engineering. This is the goal of the partnership between ORIL and BatchData. The BatchData platform provides extensive U.S. property data and predictive analytics. ORIL uses this data to design and build practical, production-ready real estate platforms.

Replacing an API platform while partners depend on live integrations requires disciplined evaluation, precise compatibility planning, and a rollout that avoids downtime. This guide provides a practical playbook for IT and project managers to assess readiness, choose a target platform, and migrate with confidence. You will learn how to baseline current behavior, design a versioning and compatibility strategy, and stage a controlled cutover.

Modern software architectures have rendered traditional QA obsolete. In an era of distributed microservices and serverless functions, bugs are no longer just code errors; they are systemic interaction failures. While Agile successfully accelerated delivery, it left a critical gap in quality assurance. The industry's initial response, splitting focus between "Shift Left" and "Shift Right", created a fragmented safety net.

AI isn’t just some side project anymore. These days, it’s a real budget line for big companies, something boards talk about all the time. Global investment in AI is about to break $300 billion a year. McKinsey says AI could add up to $4.4 trillion to the economy every year. That’s huge. But even with all this promise, a lot of businesses still have trouble figuring out if their AI projects are actually paying off. That’s the spot most CXOs are stuck in now.

Author: Adam Wolf Efficient resource allocation is a foundational requirement for scaling AI workloads, particularly as organizations move from isolated experiments to shared infrastructure supporting multiple teams, models, and environments. GPUs, CPUs, and high-performance storage are costly and finite, and without coordination, utilization often degrades as usage grows.

Learn what reverse ETL is and how it enables operational analytics. Explore its process and applications within sales, marketing, and support.

We're excited to announce that Kong just took home the AI Innovator of the Year award from SiliconANGLE Media's 2026 Tech Innovation CUBEd Awards. SiliconANGLE Media runs this annual awards program to recognize companies, technologies, and people moving the needle in B2B tech. Winners go through a review process by industry analysts and experts.

Modern enterprises need to spin up APIs fast without sacrificing control. This guide explains architectural patterns that increase delivery speed while keeping security and governance intact. You will learn how an API abstraction layer, implemented with DreamFactory, decouples experience delivery from systems of record, enables identity passthrough, enforces role-based access, and supports on-prem LLMs.

Software development has never been faster, yet it has never felt more fragmented. QA, development, and product teams often chase the same goals from different directions. Deadlines tighten, requirements shift, and communication gaps lead to rework or misaligned expectations. While DevOps practices have bridged some of those gaps, true collaboration remains a challenge.