Systems | Development | Analytics | API | Testing

Organizations are increasingly adopting AI to make quick decisions, understand data, and automate processes. However, this innovation comes at the cost of navigating complex data and AI compliance regulations. While AI regulations are still evolving worldwide, existing privacy laws and regulatory frameworks already apply to AI implementations. These laws, such as GDPR, CCPA, and HIPAA, create a complicated landscape for businesses.

Prior to coming to ThoughtSpot, I was at Salesforce where my team and I built Einstein Analytics. I fundamentally believe the role of Analytics and BI is not just to give you a chart that shows what happened—it also needs to show you why it happened, what could happen, and tell you what you can do about it. That's what being data-driven means.

Today’s software developers depend on suitable tools to perform better in their work. People working as developers during 2025 seek text editors which offer comprehensive functionalities and improve their work efficiency by incorporating intelligent functions. The dev community embraces two innovative editors – VSCode and Cursor.

REST APIs are now the backbone of modern systems, powering 83% of APIs, while SOAP lags behind at 15%. Why? REST is faster, simpler, and better suited for today’s needs, including AI and MCP (Model Context Protocol). DreamFactory makes this transition easy with automated REST API generation, robust security, and scalability. Here’s what you need to know.

Monitoring Node.js applications effectively is no longer optional—it’s essential for ensuring performance, reliability, and a smooth user experience. With a range of observability and APM tools available, choosing the right one for your stack and team can be challenging. Whether you're tracking memory leaks, CPU spikes, or asynchronous bottlenecks, the right observability stack can save you hours of debugging and protect your user experience.

Performance testing is crucial for ensuring that software systems perform reliably under varying loads. Whether you’re a business owner, product manager, developer, or devops engineer, understanding the different performance testing types and their benefits can help you prevent downtime, optimize user experience, and achieve overall system scalability.

Since Generative Artificial Intelligence (GenAI) captured mainstream attention a few years ago, businesses have been looking for ways to implement AI into their operations. There are some obvious reasons for this shift: saved time, increased productivity, and decreased need for manual effort. But there’s also another factor at play—the realization that not embracing AI now means getting left behind by the competition.

We’ve all heard of digital assistants that perform specific tasks based on our requests. But what if these digital assistants could operate with ever more autonomy? While this requires an intelligent system, such as an autonomous AI agent, capable of recognizing opportunities and acting on them without constant human input or explicit instructions, the good news is that organizations no longer need specialized developers to build their own agents.

Scrolling through the build log to find what caused your CI pipeline to fail isn’t fun. Or even spending hours inventing custom logic to automate workflows based on who triggered a build or what files changed? There might be a better solution. Enhanced Environment Variables have recently arrived – a suite of new variables designed to give you better control over Bitrise CI.

Imagine that you have built an Apache Flink job. It collects records from Apache Kafka, performs a time-based aggregation on those records, and emits a new record to a different topic. With your excitement high, you run the job for the first time, and are disappointed to discover that nothing happens. You check the input topic and see the data flowing, but when you look at the output topic, it’s empty. In many cases, this is an indication that there is a problem with watermarks.