Systems | Development | Analytics | API | Testing

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.

Let’s take a moment to think about how far we’ve come. Remember when opening a bank account meant sitting at a branch, filling out stacks of paperwork? Or when transferring money meant writing a cheque and hoping it cleared in a few days? Now, you can do all of that and so much more, with just a few taps on your phone. So, what changed? And more importantly, what’s driving this massive shift in how we bank today? The answer lies in one word: AI in Banking.

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.

Common Vulnerability Scoring System (CVSS) and the National Vulnerability Database (NVD database) help you to properly assess which software vulnerabilities should be your top priority. Here, we explain what is the National Vulnerability Database (NVD), what is the Common Vulnerability Scoring System, and how CVSS is used to calculate risk. Read along or jump to the section that interests you the most.

In this post, we'll run through testing a Node-MongoDB app, step by step. You can test MongoDB using mongodb-memory-server, an in-memory version of MongoDB that runs independently of a persistent database. A freshly spun-up mongod process starts at roughly 7 MB of memory, providing a lightweight, self-contained environment for running tests. Let's get going!

Our previous article in this series established that Solid Queue is an excellent choice if you need a system for processing background jobs. It minimizes external dependencies — no need for Redis! — by storing all jobs in your database. Despite that, it is incredibly performant. But just being performant is not enough for a production-ready background job system. Rails developers have come to expect a lot over the years. We don't just want to enqueue jobs to run in the background.

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).

AI is a groundbreaking technology that is ready to modernize the way federal government agencies operate. By automating tasks and optimizing workflows, artificial intelligence (AI) promises to enhance efficiency, minimize errors, and boost productivity without adding resources. But as with any change—and especially one as transformative as AI—leaders need to take deliberate and cautious steps to ensure a smooth integration of these innovations and to gain the buy-in of government employees.

To be good stewards of taxpayer dollars, state and local governments conduct market research and perform due diligence before purchasing a software solution. Commercial off-the-shelf (COTS) products are often positioned as offering the best price tag and the fastest deployment. However, the promise of a speedy installation often goes unmet.