Systems | Development | Analytics | API | Testing

We're excited to announce the release of Kong Gateway 3.8, a significant update that marks a major milestone in the evolution of our API management platform. This release is packed with enhancements designed to boost performance, fortify security, and provide greater flexibility and ease of use for our customers.

The main fundamental question of every business today: Are you on the right side of AI? Many businesses and investors are facing the big question: will they get AI-fied and die slowly or will they be propelled by AI and grow faster? Kong API tools and the Konnect API platform will bring our customers on to the right side of AI. Every platform shift from mainframes onward has created demand for more APIs.

Understanding and monitoring the performance and health of applications and systems is critical. This is where observability comes into play. Observability is about gaining a comprehensive understanding of a system's internal state by analyzing the data it generates within the context of real-time events and actions across the infrastructure.

When it comes to making HTTP requests in Node.js, developers are spoiled for choice: from the built-in and browser-familiar Fetch API to third-party libraries like Axios, Got, Superagent, Ky, and others. Navigating this diverse landscape can be a little daunting. This comprehensive guide aims to simplify your decision-making process by comparing the features and nuances of popular options, so you can select the ideal solution for your next project.

Rails partials have been around for years, but they can be clunky since they're just ERB snippets without a backing object structure. Recently, libraries like ViewComponent and Phlex have tried to improve the view layer by adding more semantic structure to the templates. These are great libraries and I personally reach for ViewComponent on almost every project I work on. That said, I still feel the humble Rails partial still works great for many use cases.

Local preview environments are transforming how developers test and validate code changes before merging them into the main codebase. Acting as temporary cloud environments, they provide a production-like setting where new features and bug fixes can be tested in isolation, catching issues early and streamlining the development code review process. These environments are crucial for enhancing development velocity, especially in CI/CD workflows used by DevOps engineers and QA teams.

Organizations are harnessing the power of AI to revolutionize products and services across industries. But AI-powered solutions have been getting more sophisticated. We need to redesign and amend our approach to understanding how customers experience these solutions. Unlike traditional products, AI solutions are dynamic, continuously learning and adapting. Traditional metrics may fall short of capturing the nuances of how users interact with AI.