Systems | Development | Analytics | API | Testing

Kafka delivers massive value for real-time businesses — but that value comes at a cost. As usage grows, so does complexity: more clusters, more topics, more partitions, more ACLs, more custom tooling. But it doesn’t have to be that way. If your team is managing Kafka at scale, here are five concrete steps you can take to immediately reduce cost and operational complexity — without sacrificing performance.

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.

WSO2 Kubernetes Gateway provides a robust, Kubernetes-native platform for managing APIs. It’s purpose-built for cloud-native teams requiring fine-grained control over APIs in modern, distributed environments. With support for microservices architecture, secure ingress, and service discovery, Kubernetes Gateway solves the infrastructure side of the API equation.

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

When you use loops in Python, there are a lot of times when you don’t just want the item from a list, you also want to know where that item is in the list. For example, going through a list of names and needing to show their position, like “1. Alice”, “2. Bob”, and so on, or you could be building a menu where each option needs a number next to it. In these situations, Python’s enumerate() function is very helpful.

APIs need strong security. They often handle sensitive data like financial records or health information. To protect this data while meeting privacy rules (like GDPR and HIPAA), fine-grained access control (FGAC) is key. FGAC uses OAuth scopes and claims to manage API access.

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.

Enterprise adoption of large language models (LLMs) is surging. According to Gartner, more than 80% of enterprises will have deployed generative AI (GenAI) applications or used GenAI APIs by 2026, up from just 5% in 2023. That stark increase paints a telling picture: LLMs have evolved from a fringe technology to a cornerstone of business development and productivity. But as with any new technology, competition is fierce.