Systems | Development | Analytics | API | Testing

Living near Frankfurt in central Germany, I often drive through the bustling streets of this financial hub, where every business seems to pulse with ambition. A few months ago, I helped a friend who works at a small logistics company in Offenbach. His ERP system was struggling to keep up with growing orders, causing delays that frustrated his team and clients.

Automation has long been a key driver of efficiency. Traditional RPA and IDP technology promised to relieve carriers from rekeying, extracting data from forms, and other repetitive tasks. At Appian, we saw early that automation in isolation doesn't achieve transformative outcomes. Why? Because AI has too often been deployed at the edges of workflows: copilots, chatbots, or analytics dashboards that assist us when prompted.

You know the feeling, you push a new feature, and your tests should catch any issues. But instead, they fail inconsistently, slow down your pipeline, or worse… pass when they shouldn’t. End-to-end testing is always a risk. That's precisely the reason why more teams are turning to end-to-end testing with Playwright. Unlike clunky legacy frameworks, the Playwright testing framework was built for speed, reliability, and cross-browser accuracy all out of the box.

Amazon Web Services (AWS) had a well-documented failure of the S3 service in its US East region this week. The knock-on effects of the S3 outage took out many of Amazon’s other services and caused serious and long-lasting outages for many high-profile services including Quora, Trello, GitLab, and many others. Overall Amazon provides an outstanding set of services, and as anyone knows these things happen, the key is to architect your solution so that it can tolerate these kinds of failures.

Let’s say you’re renovating a house. It’s a big project, and you’ll need a lot of planning, expertise, and equipment. Now imagine you live in a world where the hammers you bought at one store don’t match up with the nails you bought at another, and certain wrenches wouldn’t work on anything but the parts they came with. What a pain, right?

With constant releases, testing on multiple devices, and users scattered across the globe, internal teams alone can fall short. To address these issues, companies are increasingly resorting to crowdsourced testing.

When developing iOS applications, creating an IPA file is a crucial step in distributing and testing your app. But managing this process—especially across development teams—can become tedious and error-prone if done manually. In this post, we’ll explain what an IPA is, why it matters in CI/CD pipelines, and how you can seamlessly generate, sign, and distribute IPA files using Bitrise automation.

Build smart, innovative data products by pairing context-aware AI with automated data integration.

For Taohao, being a Senior Software Engineer means never standing still. Whether it’s picking up a new skill, diving deeper into a complex system, or exchanging ideas with teammates, he thrives in an environment where curiosity, innovation, and collaboration come together. Let’s find out what makes Confluent the place where engineers like Taohao aren’t just solving problems but are constantly learning, challenging each other, and building what’s next.

Not every failure is a bug and not every bug is what it seems. Sometimes, a test fails without warning. No code changes, no environment issues, just a red mark where there should be green. You rerun it, and it passes. These are flaky tests. And they do more than create noise. They drain team time, stall releases, and make it harder to trust automation at all. Left unchecked, they quietly become one of the most expensive problems in testing.