Systems | Development | Analytics | API | Testing

In the race to deliver software faster, QA teams often face an impossible trade-off: run exhaustive test runs to mitigate risk or cut corners to meet deadlines. The hard truth: Neither option is sustainable. Running every possible test wastes time and resources and skipping tests without considering their relevance and business risk introduces defects that could harm your business. The root cause of the problem? Most testing tools miss the context that makes results relevant.

In an era where 74% of companies struggle to achieve and scale AI value despite widespread adoption of integration tools, a fundamental disconnect has emerged between enterprise data infrastructure and business execution velocity. On one hand, enterprises need professionals who can own data integration outcomes without becoming engineers.

In this post, Kong Champion Sachin Ghumbre shares his journey of transforming a complex GenAI application from a state of operational challenges to streamlined control. Discover how Kong AI Gateway provided the enterprise-grade governance needed to secure, optimize, and scale his GenAI solution, tackling issues from escalating LLM costs to prompt injection risks.

OpenAI's new Agent Builder is revolutionizing how businesses create AI-powered workflows with its intuitive drag-and-drop interface. But here's the challenge that every enterprise faces: how do you give your agents access to internal databases without exposing credentials or compromising security?

In an era when every second counts, enterprises that can act on information the moment it arrives are positioned to win—and real-time streaming data is the fuel that brings artificial intelligence (AI) to life. Powering agentic AI and advanced analytics can’t be done with static or delayed data; organizations need a comprehensive, reliable supply of streaming data representing their entire businesses.

If you have ever searched for a crowdsourced testing partner, you have probably seen the same promise repeated: “thousands of devices, hundreds of geographies.” While impressive at first glance, these vanity metrics rarely reflect the true quality of a QA partnership.

AI doesn’t just learn from data, it learns from us, and we are far from perfect. When it scrapes the internet for knowledge, it also absorbs our biases, blind spots, and noise, shaping how it interprets the world..

Deterministic masking is essential for any businesses that need to secure application data across multiple non-production environments — since it ensures data is masked consistently everywhere it appears. As CTO of Perforce Delphix, I’ve worked with many companies who need to protect sensitive data while providing realistic data for testing and development. This is especially important in industries like insurance, healthcare, and financial services.

APIs are most commonly and widely used in modern applications. To build the complete application frontend, backend, and databases are required, and to make proper communication, the APIs are required. But what happens when the backend isn’t ready or third-party APIs are unstable?. This is the point at which the use of API mocking comes in. By using the API mocking, developers can build, test, and debug their applications or products faster without waiting for real APIs to be live.

By Erez Schnaider, Technical Product Marketing Manager, ClearML The GPU-as-a-Service market is experiencing hyper growth. Yet across telecommunications companies, cloud service providers (CSPs), and enterprise organizations, GPU infrastructure has been viewed as a necessary cost center rather than a strategic asset. This perspective is changing as energy optimization technologies and multi-tenant capabilities transform GPU infrastructure into monetization engines and competitive differentiators.