Systems | Development | Analytics | API | Testing

Last week, we got a question from one of our users asking us how to use Kotlin Apply. Specifically, the reader wanted to know whether it was best to use the apply function in their Android application, or another of the many Kotlin scope functions. So we got to thinking: Why not write an article about the whole topic of Kotlin scope functions? After all, they’re awesome: they let us write readable, concise code in Kotlin, and work with an object without the need for repeated references.

Today, we’re excited to unveil Kong AI Gateway 3.10! This release introduces new functionality to enhance AI governance, reduce LLM hallucinations, and accelerate developer productivity when working with AI. Read on to learn more.

The excitement around large language models (LLMs) and agentic AI is justified. These systems can summarize, generate, reason, and even take actions across APIs — all with minimal human input. However, as enterprises race to integrate LLMs into real-world workflows — especially when those enterprises operate in regulated environments and/or deal in sensitive data — one fundamental question looms large.

AI is quickly transforming the way businesses operate, turning what was once futuristic into everyday reality. However, we're still in the early innings of AI, and there are still several key limitations with AI that organizations should remain aware of to ensure that AI is being leveraged in a safe and productive way.

High-scale, high-frequency messaging and burst activity can lead to significant costs and performance challenges for realtime applications. To help with this, we’ve introduced server-side batching and conflated subscriptions—two powerful optimizations in Ably Pub/Sub that significantly reduce message count, lower bandwidth usage, and optimize processing overhead. These features allow you to process high-frequency updates more efficiently without sacrificing accuracy, reliability, or latency.

API audit logging is essential for tracking API interactions, securing sensitive data, and meeting compliance standards like GDPR, HIPAA, and PCI DSS. It records who accessed data, what actions were taken, when it happened, and more. Proper logging can reduce data breach risks by up to 70% and improve regulatory compliance by 90%.

So, you've built a Flask application — congratulations! You've crafted routes, connected databases, and perhaps even deployed it to a server. But have you tested it thoroughly? Testing isn't just a checkbox on a developer's to-do list: it's an essential part of building robust and reliable applications. So, in this article, we'll describe why testing is important for Flask applications and how you can effectively implement tests.