Systems | Development | Analytics | API | Testing

Modern enterprise architecture faces a three-body problem. Three distinct traffic patterns pull your teams in different directions. External APIs serve mobile apps and partner integrations. Internal microservices communicate within Kubernetes clusters. AI and LLM calls flow to OpenAI, AWS Bedrock, and self-hosted models. Each pattern looks API-like on the surface. Yet many organizations handle them with separate tools. The result?

Appian’s latest updates deliver powerful new tools to consolidate legacy systems, automate complex knowledge work, and scale data integration. Modernization projects are notoriously high risk, but Composer derisks the start of your journey by ensuring total stakeholder alignment before development begins.

The SwiftUI vs UIKit question may seem like a sticky web of pros, cons and competing nuances. But ultimately, it boils down to one thing: Which framework is best for my specific app? The answer shouldn’t be based on hype or trends. It should be based on your own real-world parameters, like team size, UI complexity and long-term maintenance. Understanding your own development realities is crucial to making the right choice between SwiftUI and UIKit.

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.

Service virtualization tools have become indispensable for organizations seeking to streamline their testing and development processes. These tools allow teams to simulate the behavior of critical software components, enabling more rapid development with overall cost reduction and improved collaborative outcomes. As demand mounts for service virtualization solutions, identifying the best tools to support this workflow in the software development lifecycle has never been so important.

For teams using CI/CD, the specs of the build machine can have a significant impact on development productivity. Faster builds mean shorter fix-and-verify cycles, which speed up the overall development process. However, it’s hard to know how fast each CI/CD service actually is without comparing them under the same conditions. In this article, I compare the iOS build speeds of GitHub Actions, Bitrise, and Codemagic using the same Flutter project, and compare them in terms of cost-performance as well.

Self-service analytics lets SaaS operators ask a business question and get a trusted, metric-backed answer without waiting on an analyst. Here’s what that requires in practice.

Both methods have their place, but identity passthrough is the go-to for modern, secure, and compliant data access.

AI APIs don't get enough credit for how much weight they're actually carrying. These AI APIs aren't merely technical connectors. They're, in fact, cost drivers and potential revenue engines. And when something goes sideways, they're ground zero. In production, they behave nothing like the traditional APIs your teams have been running for years; they introduce a whole new set of hurdles around operations, security, and governance that most organizations are still struggling to understand.

From optimizing startup times to simulating real-world network chaos, discover how to build an automated mobile performance testing strategy that scales across thousands of real devices and protects your user experience.