Systems | Development | Analytics | API | Testing

Every enterprise contract I’ve signed in the last two years has the same clause. “Vendor will not use Customer Data to train machine learning models.” Sometimes it’s a paragraph. Sometimes it’s a whole section. The language varies but the intent is identical: don’t feed our production data into your AI. I get it. I sign the same clause as a vendor. But here’s what’s been bothering me: that clause is a promise, not an architecture.

Mobile crash reporting tools don’t just tell us when our app’s broken down. They help us pick up the pieces and build better next time. As such they play a vital role in our quest to deliver excellent user experience, so it’s important we choose the right tool for our team, users and operating systems. In this guide, we’ll compare the best mobile crash reporting tools in 2026, including Android-focused and cross-platform solutions.

Each change to Kong Gateway's codebase triggers a comprehensive test suite that runs more than 17,000 * 2 = 34,000 test cases among the two primary architectures (x86 and ARM) we support. This process takes about 23.5 hours on a single machine. But we don't wait that long. A large fleet of machines runs the suite in parallel, and we shard the work aggressively so each commit finishes in a fraction of that time. That setup works well, right up until flaky tests get involved.

There is a specific kind of confidence that comes with deploying software. Not just “the tests passed” confidence, but the kind that comes from knowing the services your application depends on still behave the way you expect them to. Preserving that integrity becomes harder as systems grow, teams move faster, and AI agents become active participants in delivery workflows.

In fast-moving markets, interoperability means you’re never trapped by yesterday’s decisions.

The most dangerous multilingual AI failure is a fluent output that feels native enough to trust, but is wrong enough to mislead.

As AI-aided software development lifecycles pick up speed, performance teams are left with the familiar challenge of too much work, too few specialists, and results that take too long to analyze. Over the past year, Tricentis NeoLoad has shipped capabilities designed to address each of these problems directly. What started with Augmented Analysis accelerating root cause identification grew into a fully connected Model Context Protocol (MCP) architecture.

Finance leaders are operating in one of the most demanding macro environments in recent memory. Interest rates are moving faster than most models anticipated, reshaping the cost of capital almost overnight. Supply chain fragility has also turned working capital management into a moving target, and geopolitical uncertainty is changing how you plan for the future. Yet for many finance functions, the analytics stack hasn't kept pace with that urgency.

At Ably we've been able to make a transformational change in how we build SDKs as a result of what's now possible with LLMs. In this post, I explain how.