Systems | Development | Analytics | API | Testing

Agentic AI systems don’t fail like traditional software - they hallucinate facts, pursue the wrong goals, overuse tools, and forget context. These failures look “correct” to traditional test cases, but feel dangerously wrong to users. One team tested an AI support bot - it passed every check, but in production, it gave refund advice that violated company policy. Not a code error. A reasoning failure.

Apache Iceberg is transforming how organizations build and manage their data infrastructure, enabling lakehouse architectures that combine the best of data lakes and data warehouses. In this blog, we look at five real-world implementations demonstrate Iceberg's versatility and the advantages it brings to modern data management challenges. Learn more about Data Lakehouses.

Android app development is the process of building software for Android devices, such as smartphones, smart TVs, tablets and wearables. It’s a Linux-based system and open source, which means manufacturers can customize it. Android version names used to be named after desserts, like Lollipop, Marshmallow, KitKat and Oreo. From version 10, Google switched to number-based names like Android 10, Android 11, Android 12, etc, up to the most recent, Android 15, which was released in September 2024.

Bugs don’t wait, and neither should your testing tools. As software grows smarter and faster, the race is on for solutions that can keep pace, efficient, dependable, and ahead of the curve. Open source testing tools are now an essential component of organizations (especially startups) that need to automate their quality assurance process without spending a hefty amount on testing.

The complexity of software continues to increase as teams adopt microservices, APIs, and cloud-native architectures. Manual testing is no longer able to keep up with the speed of continuous releases. QA teams are dealing with increasing pressure to not only assure software quality but to do so in a shorter cycle time. This is where AI Testing Tools can provide a solution. AI-powered testing takes advantage of machine learning, predictive analytics, and self-healing features.