Systems | Development | Analytics | API | Testing

Understanding how your APIs handle and process data drives better decision-making. Different kinds of data flow through APIs and the services that consume them. But the actual information lives inside the HTTP request and response bodies. If you can effectively leverage that data with the contextual API metrics like status codes and routes, you can better understand how your customers consume your services.

The rise of AI has transformed how businesses operate, creating a surge in demand for AI-driven APIs, particularly those that leverage Large Language Models (LLMs). These APIs are at the heart of many modern applications, driving automation, customer interaction, and sophisticated data analysis. However, with this increased use comes a need for organizations to effectively monitor and manage the costs and consumption of these APIs.

Kafka is a highly popular realtime data streaming platform, renowned for handling massive volumes of data with minimal latency. Typical use cases include handling user activity tracking, log aggregation and IoT telemetry. Kafka’s architecture, based on distributed partitions, allows it to scale horizontally across multiple brokers. But although Kafka excels at high data throughput, scaling it to manage thousands of client connections can be costly and complex.

Digital evidence plays a pivotal role in roughly 80% of criminal investigations. From text messages and emails to social media posts and GPS data, digital evidence offers law enforcement insights that majorly impact police investigations. Smartphones, mobile devices, computers, surveillance cameras, and other digital devices have become ubiquitous in daily life. Digital footprints are now critical to establishing timelines, tracking movements, and identifying suspects.

At API Summit 2024, a common theme covered was how there's no AI without APIs. This couldn’t be more true for Writer, the full-stack generative AI platform for enterprises — and a notable user of the Kong Konnect unified API platform. Writer recently launched AI Studio, billed as “the fastest way to build generative AI apps.” AI Studio uses Writer API, Writer Framework, and Writer No Code to give people enterprise-grade generative AIs they can put in their own apps and services.

We’re happy to announce that we’re consolidating all of our documentation into one easy to use site! Yep, we’re sunsetting the wiki and guide, and we’re moving it all to Docs.dreamfactory.com. This new site will be the go-to site for all technical documentation for DreamFactory.

We understand how complex app distribution can be, especially when juggling multiple tools for mobile app testing and store releases. This is what prompted us to build Release Management back in 2022. Originally tightly coupled to Bitrise CI, we're excited to announce that Release Management can now be used with any CI tool. This means all mobile teams can use Release Management, and we're offering it with a generous free tier, regardless of their CI tool.

Today, WSO2 launched updates to the open source API management platform for building and managing full lifecycle APIs with new releases of its core WSO2 API Manager offering and WSO2 API Platform for Kubernetes (WSO2 APK).

Many developers focus on developing core application functionalities and pay little or no attention to memory management until they run out of memory, and their apps start crashing, freezing, or experiencing random performance downgrades.

API observability plays a crucial role in making sure that your API delivers its fullest potential and consistency. Therefore, it’s important to choose the right tool for your observability requirements. In this article, we take a look at two API observability tools—Moesif and Treblle. We briefly discuss the key components of API observability. Then we dive into how Moesif and Treblle compare with each other.