Systems | Development | Analytics | API | Testing

What is Sauce Labs AURA? The AI-Unified Release Assurance Platform

AURA: AI-Unified Release Assurance is the world's only closed-loop agentic platform that authors, runs, and analyzes tests with human oversight, informed by 8.7B+ test executions. It takes you from business intent to production confidence at AI speed. From the founders of Selenium and Appium.

Duplicate API Tests Are Slowing You Down. Here's How Keploy Deduplicates them

Ever wondered why your test suite keeps growing even though you're testing the same few flows over and over? 👀 In this video, we break down how Keploy automatically detects and removes duplicate tests - so your suite stays fast, lean, and actually trustworthy to review.

Announcing Confluent Platform 8.3: Powerful Apache Flink SQL operations, Easier KRaft Migrations, Expanded Monitoring and more.

Platform teams want to maximize the value of data in motion, but separate workstreams for stream processing, monitoring, KRaft migrations, and data governance create friction as environments grow. As a result, teams spend more time managing operational overhead and less time building. Today, we’re excited to announce Confluent Platform (CP) 8.3.0, built on Apache Kafka 4.3.0, reinforcing our core capabilities as a data streaming platform.

Orchestrating Runtime Resilience: The Definitive Guide to Self-Healing Test Frameworks

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.

AI Debugging: How to Use AI to Find and Fix Bugs Faster

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.

Serious About Process Ep. 1 | Modernizing Insurance Pricing with Catrin Townsend

Episode 1 of the Serious About Process podcast is live! Insurance pricing has evolved from slow, static updates to fast, dynamic decision-making. But have your systems caught up? In our first episode, host Gijsbert Cox is joined by Catrin Townsend, Director of Education at Price Writers, to discuss: Why pricing is an interconnected system, not just a sequence How legacy systems stifle innovation and frustrate teams The real promise of AI as a workflow partner.

Unlocking the Power of Trusted Data Intelligence: Amazon Quick Meets Qlik MCP Server

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.

Ep 83 | From KPIs to Action: What Comes After The Dashboard?

Your dashboard can tell you sales are down, but it can't tell you why or what to do next. Dashboards have become the default way to monitor a business. Bhaskar Sunkara argues they're only the starting point. The next step is AI that understands business context and helps leaders move from insight to action.

How to Build an Ambient Clinical Documentation Solution (AI Medical Scribes) - The 2026 Build Playbook

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.

eBPF: Preventing Garbage HTTP Payloads When Reading Kernel Scatter-Gather Buffers

Recently someone on our team opened a traffic snapshot and found an HTTP request that was captured with our eBPF capture agent, nettap. Our protocol dissector parsed most of the response correctly, but that correctness ended once the response headers were processed. What they ended up with was a recording of an HTTP request/response where the response body was just an incorrect collection of garbage binary data when it should have been JSON text.