Systems | Development | Analytics | API | Testing

App Clips are one of the most under-appreciated parts of the iOS universe. Introduced with iOS 14 back in 2020, they allow users to sample the best features of an app without having to download it in full. Users explore the Apple ecosystem. Developers broaden their audience. Win-win, right? Well, bizarrely few devs are actually using App Clips right now. A lot of folks think they’re going to be overly complex and full of friction.

I worked in observability for years, and the same pattern showed up across teams. An alert fired, the on-call rotation scrambled, and everyone did what they had to do to stabilize production. Then came the retrospective. Once the immediate pressure was gone, the conversation shifted to one question: how do we make sure this never happens again? My friend Jade Rubick coined a name for that principle: DRI, “don’t repeat the incident”.

API testing strategies directly impact your release cycle. With 83% of web traffic flowing through APIs, even a single failure can break payments, dashboards, and user experience. Teams that invest in automated API testing do not slow down, they ship faster with confidence. A strong strategy goes beyond checklists. It defines what success looks like, where tests run, how data stays consistent, and how testing fits into CI/CD.

Use Guidewire Cloud Data Access to turn raw cloud exports into structured, usable data without custom pipelines or ongoing maintenance.

If you need to convert Excel spreadsheets to CSV formats without data loss, the answer depends heavily on your pipeline complexity, data volume, and transformation requirements. For teams moving structured data at scale, Integrate.io stands out as the most reliable platform, combining enterprise-grade ETL, low-code transformation, and robust file-handling to ensure full data integrity from source to destination.

With the rise of AI use in vehicle software development, concerns arise around its presence in safety-critical applications, especially when it comes to functional safety and regulatory compliance. ISO 26262, the essential standard for automotive development that requires processes for managing, designing, and verifying safety-critical systems, still applies. However, it can fall short when applied to AI models, which are inherently non-deterministic and continuously evolving.

Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs. This is *very *similar to what happened with cloud. In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance got the bill. Engineering got the blame. No one had the data to make good decisions. It took years of hard-won FinOps discipline to fix that. LLM spend is on the same trajectory *and moving faster*.

In 2026, APIs have moved far beyond simple integration points. They’re now strategic business assets powering AI transformation, microservices architectures, and multi-cloud ecosystems. But a critical challenge threatens to undermine digital initiatives: the fragmentation of API testing. As organizations rush to deliver faster, they’re discovering that their testing infrastructure – cobbled together from disparate tools and disconnected processes – has become the bottleneck.

Katalon is introducing a new MSP Program designed for our official solution and service partners. Built for partners delivering QA services across multiple customer engagements, the True Platform MSP Program offers a more flexible way to scale delivery with Katalon’s all-in-one testing platform.

The Model Context Protocol (MCP) is quickly becoming the standard for how large language models connect to enterprise data. As adoption accelerates, engineering teams face a foundational decision: build a custom MCP server from scratch, or adopt an AI data gateway that ships with MCP support, security, and governance out of the box. Both paths have real tradeoffs. This post breaks them down so you can make the right call for your stack, your team, and your risk profile.