Operations | Monitoring | ITSM | DevOps | Cloud

Steve is joined by Cindy Gross to discuss leadership coaching in the technology industry – what it is, how it works, and the many benefits it brings. Recorded on-site at PASS Data Community Summit 2025 in Seattle. Cindy is an executive coach and Adaptive Leadership Expert with 25 years in the US tech industry. As a former SQL Server Master (MCM) and expert in cross-organizational navigation at Microsoft, she transformed her focus from complex technical systems to empowering leaders within systemic chaos.

Most enterprise SaaS products, like Komodor’s Autonomous AI SRE Platform, require installing a remote agent on the customer’s infrastructure, which varies significantly from one organization to another, in terms of architecture, configurations, permissions, processes, and more. This “unmanaged” model creates major blind spots, making daily operations, observability, debugging, and incident response challenging. When failures occur, limited visibility and bespoke systems make root-cause analysis slow, incomplete, or impossible.

Millions of metrics. Zero clarity. That’s the reality many IT teams are facing today. As environments grow more complex, telemetry explodes. Millions of records generated every hour. Dozens of specialized tools for network, storage, Kubernetes, cloud, AI workloads. Each tool is good at its domain. But none of them answers the real question: Where should I focus right now? Fragmented visibility creates predictable failure modes.

Spec-driven development with AI coding agents could change how you build software. In this GitKon 2025 talk, Erik Hanchett, Senior Developer Advocate at AWS, breaks down why AI coding assistants perform dramatically better when they start with structured specifications instead of raw prompts. If you've been vibe coding your way through complex features and wondering why your AI keeps going off the rails, this is the video for you.

Slow computer? Watch RITA diagnose and fix it in real time. Zero Ticket IT.

Building AI applications often means juggling multiple models, scattered datasets, and version chaos across local systems. But what if you could bring it all together — securely and efficiently — in one place? In this walkthrough, Shibam Dhar, DevRel Engineer at Harness, demonstrates how Harness Artifact Registry makes it easy to manage and govern your AI/ML assets — from models and datasets to prompts and agents — with built-in support like Hugging Face and generic registry types.