Systems | Development | Analytics | API | Testing

APIs have long been the backbone of modern software systems, architectures, and businesses. They now dominate the web, accounting for 71% of all internet traffic. Generative AI is accelerating this trend especially as we pivot our interaction with common web-based capabilities, like “search” in favour of AI-enriched variants. More AI leads to more APIs, and with that, APIs act as an important mechanism to move data into and out of AI applications, AI agents, and Large Language Models (LLMs).

This article originally appeared on Software Testing News. We’re sharing it here for our audience who may have missed it. An overlooked API can expose customer data, trigger multi-million-dollar fines, and sink a FinTech product launch. And now, the FinTech industry is at a crossroads, driven by innovation yet bounded by intensifying regulatory demands.

This article originally appeared on DevPro Journal. We’re sharing it here for our audience who may have missed it. For years, APM tools were everyone’s go-to solution for understanding how software behaved in production. And for a time, they worked, because architecture was simpler. Developers owned the backend, the frontend, and the data layer. Everything lived inside a monolith. If something went wrong, they could trace it through their codebase and fix it.

Testing has evolved far beyond scattered spreadsheets and disconnected tools. Yet even with modern platforms in place, teams still run into bottlenecks, especially when fundamental tasks like test case creation are handled manually. It involves combing through acceptance criteria, writing out each step, and reviewing everything for gaps. Repeating that across multiple user stories quickly drains time and slows progress – it’s repetitive, time-intensive, and prone to inconsistency.

The new bugsnag-kmp SDK is a unified error monitoring solution for Kotlin Multiplatform (KMP) projects, enabling developers to track and monitor errors across Android, iOS, and web platforms from a single codebase. The new SDK works seamlessly on Android, iOS, and web browsers Native Integration, each one linking directly with our existing platform SDKs.

Software release cycles are accelerating. In fact, 85% of organizations now release at least once per month, with a 51% increase in automated testing spend last year alone. Yet API quality still breaks down. Why? APIs sit at the center of this acceleration, but as velocity increases, many organizations face a persistent challenge.

Everyone thinks AI is getting cheaper. But is it really? At first glance, the economics of AI seem to be improving for everyone. Thanks to continued model optimization and advances in hardware, the cost of running LLMs (also known as inference) is steadily decreasing. Developers today can access incredibly powerful models at a fraction of what it cost just a year ago. But there’s a catch.

Another day, another API. Fueled by AI-assisted coding and agile workflows, the speed of innovation has never been higher. But for the compliance team? It’s panic mode. Every new API must follow a minefield of internal rules: security protocols, naming conventions, reuse policies, documentation standards. And while the dev team is flying forward, compliance is stuck doing manual reviews, chasing specs, and untangling inconsistencies often after the code is already written.