Systems | Development | Analytics | API | Testing

Test case design constantly adopts new languages, architectures, and methodologies. But in the last few years, the pressure to scale while ensuring quality in a smart way, without overly increasing efforts, has changed the narrative. AI has entered the scene, promising to systematize decisions, reduce redundancy, and even create tests from scratch. Some teams have already experimented with AI, while others observe with skepticism.

Test automation is no longer a nice-to-have—it’s a baseline expectation for any team serious about delivering high-quality software at scale. Paulo Feitosa , Sr. Manager, Automation Solutions,

It is said that the special nature of software development is ever-changing so it is important to choose the right tool for the job. Whether it’s a web interface or a cross-platform mobile app you are building, React and React Native are two of the most powerful technologies available today. This blog will answer the question of "what is React and What is React Native" and show how Keploy can help improve test efficiency across both frameworks.

Chatbots are quickly becoming more sophisticated and integrated into business workflows, enhancing productivity and scalability. However, they also expand the attack surface for organizations. This new exploitation vector requires data engineers and security teams to incorporate various security guardrails when building their gen AI architecture. In this blog post, we discuss the risk of data leakage through AI chatbots.

In March 2024, the European Union passed the AI Act. This sweeping regulation reshapes how organizations deploy and manage AI systems. The law addresses AI risks that could affect both individuals and businesses, from hiring biases to critical infrastructure failures. Similar rules have started taking shape across the world, including several state-based regulations in the US. But the regulation is only the floor. We all share the responsibility of creating a safe, responsible AI future.

After two weeks of immersion at the Databricks Data + AI Summit and Snowflake Summit, it’s clear that the pace of innovation is accelerating faster than ever. Both events showcased groundbreaking advancements in AI, data orchestration, and table formats, with Iceberg, catalogs, and hybrid customer personas emerging as the key themes. Below are some analysis on the trends shaping the modern data ecosystem.

We’re at it again, bringing more incremental improvements to Kong Mesh!

Devoxx Poland 2025 felt different. Not because of revolutionary new frameworks or another "this changes everything" moment, but because of what didn't happen. The conference had an unusual dose of pragmatism, skepticism, and – dare we say it – common sense. Maybe it's because developers are asking the right questions: "Does this solve a problem?" and "What happens when this inevitably breaks?" Here's what emerged from the sessions we watched, and the people we spoke to.

The era of generative AI is upon us, and large language models (LLMs) like Claude are demonstrating incredible potential to revolutionize how businesses operate and interact with customers. However, to truly unlock this potential, AI needs secure and standardized access to the wealth of information and services locked within enterprise systems. This is where standards such as Model Context Protocol (MCP) come in, offering a powerful way to make enterprise resources AI-consumable.