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LLM observability is broken There are too many new libraries floating around, but they don't follow accurately the OpenTelemetry conventions. OTel isn’t perfect for LLMs yet—but extending a proven standard beats inventing another one. Why not use the same standard (OTel) which works so well for rest of the apps, and just work on top of it? This is what I was ranting with Pranav Raj S, co-founder at Chatwoot and we thought there must be other folks facing similar issues.

AI is changing everything. At Datadog, our approach is two-fold: empower you with complete observability across your entire stack, including AI as you incorporate it, and harness emergent technologies to make Datadog even more powerful. Join VP of Product Michael Whetten to see how Datadog is accomplishing these two approaches. He'll share the latest feature updates and new products designed to help you thrive in an AI-powered world. Plus, get a look at our long-term vision for the future of AI and its impact on your work.

Watch how AI is reshaping observability for the years ahead. In this fireside chat, Logz.io founders Tomer Levy and Asaf Yigal reveal how the most innovative AI-first companies are breaking free from dashboards, avoiding common RFP mistakes, and building future-ready stacks. You’ll see: Watch and learn how autonomous AI eliminates noise, slashes costs, and gives engineering teams back their velocity.

AI is boosting flow, job satisfaction, and productivity for devs. But here’s the twist: it’s not actually giving us more time to do valuable work. In his GitKon talk, Nathen Harvey digs into the real impacts of AI adoption in technical teams. Yes, AI reduces toil and improves satisfaction. But it also shifts how we spend our time and sometimes that means less “valuable work” gets done. So the question becomes: how can we use AI not just to work faster, but to work better?

Ever found yourself saying, "But it works on my machine!" when a bug pops up in a microservices environment? It's a common and frustrating problem. Unlike a monolithic application, microservices are a collection of independently deployed services that communicate with each other. This complexity makes it difficult to reproduce real-world issues on your local machine, as you may not have all the necessary services and dependencies running. But what if you could take a snapshot of a running application's behavior and bring it home for debugging?