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Introducing the Bitrise Desktop App; a small native app that lives in your macOS menu bar and shows the live status of your builds in the one spot you already glance at all day. There's nothing to open, refresh, or check: the status of your builds is right there for you to glance at. Meaning you can push builds, keep coding, and look up only when something needs you. Available today for macOS: Download for Mac.

Summary Kafka has become the backbone of the real-time enterprise. The streams it carries are not only time, but business critical: a fraud event isn't processed, a sales order not fulfilled, a trade not settled. Yet we heard a recurring theme from Kafka teams: their business is running critical streaming applications without proper Kafka resiliency.

If you replicate data between two distinct Kafka clusters, you already know the payloads can match while the offsets might not. This post is about how K2K 2.0 now also keeps consumer committed offsets in sync between the source and the target so consumer groups can fail over in a Disaster Recovery situation, avoiding large re-reading of data or row skips. This offers the community more choice for DR than just MirrorMaker2 and Confluent solutions have until now.

Everyone’s connecting AI to their business data right now. Fewer people are asking whether the AI actually understands what it’s looking at.

Bitrise is one of the few global AWS technology (software) partners who build on top of the Amazon EC2 Mac compute offering. We are proud to announce we have received recognition of our solution quality and expertise from AWS for our mobile-first CI/CD product, Bitrise on AWS.

Clinical documentation has become one of the biggest sources of administrative burden in healthcare. Physicians spend hours every week creating notes, updating EHRs, and completing after hours charting. As a result, healthcare organizations are increasingly investing in ambient clinical documentation solutions that can capture conversations, generate notes, and reduce manual documentation work.

When was the last time you made a major business decision and were completely certain the data behind it was accurate, complete, and trusted? For most organizations, that certainty is less common than it should be. Qlik and Amazon Quick together solve one of the biggest obstacles to AI adoption: knowing whether you can trust the output.

At its simplest, AI debugging automates repetitive coding tasks like searching logs, reading stack traces and comparing sessions. But good AI debugging is a much more challenging concept that relies on focused context, runtime evidence and structured investigation paths. In this post we’ll show you how to debug with AI, not just productively but also responsibly. Let’s get into it.

Enterprise teams depend on automated testing to sustain rapid release cycles, yet scaling quickly reveals a major bottleneck: the maintenance footprint. Consider a financial enterprise running a nightly regression suite of 3,000 scripts. A developer updates the checkout UI, renaming a submit button’s class from-submit to-primary. The core payment logic stays completely untouched, yet 400 test cases fail overnight.