Systems | Development | Analytics | API | Testing

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.

In case you missed it, we recently made a big announcement around beta GCP support for Kong’s Dedicated Cloud Gateways (DCGWs). There’s a lot of good stuff in there, but TL;DR DCGWs now support all three of the major cloud service providers (CSPs): AWS, Azure, and GCP at a 99.95% SLA with support for over 25 regions around the globe. Being the first API management vendor to support managed gateway deployments with all three CSPs has a lot of folks excited, for obvious reasons.

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.

You’ve probably heard of the concept of ‘Frontend decides, backend delivers’ in app development. On the off-chance that you haven’t, it means that the frontend defines the data it needs, and the backend acts on this instruction. This makes the data-fetching process more efficient, simplifies the error handling process and frees us, the devs, from the need to constantly make backend changes. The GraphQL query language for APIs, developed by Facebook, is a vital tool in this regard.

Do you ever feel like you need a superpower to get the information you need? Especially when you’re really into Python? APIs are pretty much that superpower! APIs (Application Programming Interfaces) let your code "talk" to other systems and get exactly what you need. They can help you come up with a new app, find the next big market trend, or even automate your morning weather report. This guide?

Struggling with data consistency in sharded APIs? Here's what you need to know upfront: Data sharing improves performance by dividing data across multiple databases, but it introduces challenges in maintaining consistency. Consistency models matter: Choose between strong consistency (immediate accuracy, higher latency) and eventual consistency (temporary inaccuracies, higher performance).

In this episode, Emmanuel Paraskakis, CEO Level 250 breaks down the core responsibilities of an API product manager. Speaking from his experience in product management for over fifteen years, Emmanuel distinguishes an API product manager’s focus from conventional product roles, underscoring their critical importance in building scalable digital platforms.

NGINX One provides a modern solution for enterprises to manage infrastructure at scale across globally distributed systems. The platform has built-in tools for essential performance and uptime metrics, giving DevOps teams visibility into the health of their NGINX instances. But for effective API observability and analytics, you have to go beyond infrastructure metrics.

To be honest, most Python developers are not using list comprehensions. Even I, who is writing this blog, never used list comprehensions before. But when I saw some examples, I felt I had to try and use them in my Python code. The reason for this change of mind is that there are a few advantages we get if we implement list comprehensions. Let’s see what these are in this blog today.