Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

The Flare CLI connects to your Flare performance monitoring data and uses AI to turn it into actionable insights, right from your terminal. In this video, you'll see how a single command pulls your real performance data from Flare, then generates a full review: identifying slow endpoints, spotting error trends, and suggesting concrete fixes. Links.