Operations | Monitoring | ITSM | DevOps | Cloud

Ep 41: The cost of not thinking: Who's responsible when AI agents get it wrong?

In this episode of Masters of Data, we get into the messier side of AI adoption, tackling questions like who actually owns the output when AI gets it wrong, and whether chasing efficiency is making us forget what it means to be human in the first place. We discuss tech CEOs proudly announcing they no longer think for themselves and debate whether AI is quietly eroding our critical thinking skills. We make the case that purpose-built, narrow AI is genuinely exciting, but that no efficiency gain is worth losing the human touch that makes work, connection, and creativity meaningful.

How Scalability Works in SolarWinds Observability Self-Hosted

Cheryl Nomanson, SolarWinds staff technical trainer, provides a comprehensive overview of SolarWinds architecture and scaling options for self-hosted deployments. She explains the centralized deployment model starting with a single SolarWinds server that handles polling, web console, and database connections. The presentation covers key scaling indicators including polling thresholds that warn users at 85% capacity and alert at 100%. She demonstrates how to add up to 100 polling engines per server and additional web servers to handle more concurrent users.

Learn these 4 Chaos Engineering Principles Before You Break Anything | Resilience Testing | Harness

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.

Harness Lives Inside Cursor Now - Plus Everything Else That Shipped in April

April was a big month at Harness. AI is changing how code gets written — and the rest of the SDLC is catching up. In this update, Dewan Ahmed walks through Harness product releases across three themes: AI in the developer workflow, security and governance for AI assets, and self-service maturity for developers and platform teams. What's covered (with timestamps): Found this useful? Subscribe for monthly product updates, and drop a comment telling us which release you want a deep dive on next.

The most debated DORA metric (even Google debates this)

What's the most debated DORA metric? Nathen H from Google's DORA team breaks down the change lead time debate — and why even the experts can't fully agree on when a change is "committed." Is it at commit? After merge? The answer matters more than you think. Subscribe for more DevEx and DORA insights from our Web Summit series.

AI Enablement for Dev Teams: The 6-Pillar Flywheel

AI adoption is already happening on your team, whether you have a strategy or not. Tracy Lee (CEO of This Dot Labs, Microsoft MVP, Google Developer Expert) breaks down the AI Enablement Flywheel — a 6-pillar framework used by successful engineering organizations to move from scattered experimentation to scalable, ROI-positive AI workflows.

Federated Search | From Silos to Insight | Unified Datasets in AWS S3 with Ingest Processor

Are storage costs and data silos slowing down your investigations? In this video, we dive into the Unified Dataset Experience to show you how to search data where it lives. Learn how to use the Splunk Ingest Processor to route high volume logs directly to AWS S3 while maintaining instant visibility via Federated Search. No more re-hydrating data, just fast cost-effective insights.