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WSO2 Advances Developer Productivity With Latest API Management and Integration Offerings

Developers can now manage data planes for WSO2 API Manager and WSO2 API Platform for Kubernetes with the same control plane, develop integrations in VS Code using Micro Integrator for VS Code, and utilize AI assistants to streamline efforts.

Unveiling WSO2 APK 1.1: Kubernetes-Native API Management

We're excited to introduce WSO2 API Platform for Kubernetes (WSO2 APK) 1.1, a key update to our Kubernetes-native API management solution. This release leverages the strengths of Kubernetes to offer high-performance and cloud-scale API management, ensuring automation, reliability, and improved developer experiences. Traditional API management solutions aren't built for Kubernetes environments, leading to issues with performance, scalability, and manual configuration.

Introducing WSO2 API Manager 4.3: Stronger, Streamlined API Management

We’re delighted to announce the release of WSO2 API Manager 4.3, a significant upgrade that enhances your API ecosystem's performance, security, and scalability. WSO2 API Manager, an industry-leading open source API management solution, offers a unified platform for creating, publishing, and overseeing APIs, enabling organizations to securely expose their services with both internal and external stakeholders.

Introducing Insomnia 9.0 with Pre-request Scripting, Improved Offline Access, API Mocking, and 100+ Improvements

Kong Insomnia 9.0 is now generally available for download. It ships with many new features and improvements — over one hundred improvements in total! This new version of Kong Insomnia also makes it much easier to migrate from other products with a more reliable “import” experience, while improving the speed of using the product and making it more customizable. Let’s dive into all the notable features.

Snowflake Arctic: The Best LLM for Enterprise AI - Efficiently Intelligent, Truly Open

Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. As researchers, we have grappled with the constraints of efficiently training and inferencing LLMs for years.