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In this video, we explore two major improvements to the Harness Resilience Testing documentation designed to help you build and manage chaos experiments more efficiently. What's new: ChaosHub Integration AI Prompt Library for Harness MCP These updates make it significantly easier to discover chaos experiments and leverage AI throughout your chaos engineering workflow. If you're using Harness Resilience Testing, this walkthrough will help you get started quickly and make the most of the new documentation experience.

• Disaster recovery planning is essential for ensuring digital services remain online in the face of catastrophic failures or outages. When a major digital infrastructure outage occurs, systems need to be set up to automatically respond and restore functionality as quickly as possible. But no matter how in-depth your disaster recovery plan is, it’s still only theoretical until it’s thoroughly tested under realistic failure conditions, which is why testing is often mandated by leadership and regulators.

Want to start chaos engineering? Don't randomly break stuff and hope for the best. Real chaos engineering starts with defining your system's steady state metrics like latency, throughput, and error rates. Then you form a clear hypothesis about what should happen when failures occur. Next, you inject controlled failures, starting small with single pod kills or network drops, not production meltdowns. Finally, you limit the blast radius by running experiments in safe environments first.

Stop treating system outages like surprises and start preparing for them. While traditional software testing is the bedrock of development, using unit, integration, and regression tests to verify that code meets specific requirements, it only accounts for what we expect to happen. Chaos Engineering takes a different approach by shifting the focus from bug prevention to system resilience. Instead of asking "does this work?", Chaos Engineering asks "how does this survive?" by injecting real-world turbulence like network latency or pod failures directly into production-like environments.

Discover how leading engineering teams proactively build rock-solid applications using Chaos Engineering. Learn why waiting for real outages is risky and how intentionally injecting controlled failures like pod crashes, network latency, and node restarts helps uncover hidden weaknesses before they impact your users. In this short, explore the simple yet powerful practice that turns fragile systems into resilient ones and how Harness makes running chaos experiments effortless and safe with its intuitive Resilience Testing module.

Are myths about chaos engineering preventing your team from building more resilient systems? In this video, Matt Schillerstrom, Director of Product Management at Harness and founding engineer of the chaos engineering program at Target.com, breaks down the three most common misconceptions about chaos engineering. Drawing from his experience building large-scale programs, Matt explains how to move past these myths to build confidence in your infrastructure.

Training is at the heart of every LLM model, but it’s still an application running on an infrastructure, which means it can fail. Our GPU test helps you test your training GPUs so you don’t lose that valuable work. TRANSCRIPT: One of the things we built recently was the GPU Gremlin. So if you are training a bunch of models and you're doing a bunch of GPU testing. You know, we want to give you the tools to be able to go test that, to understand how training the model could fail.