Operations | Monitoring | ITSM | DevOps | Cloud

When a new cybersecurity vulnerability is discovered, it gets a snazzy name, media coverage, and major accolades for the researcher. But what about the person who stays up late writing, testing, and deploying the patch to fix it?

Learn how to deploy Canonical Managed Kubeflow on Microsoft Azure step by step. Canonical's Managed Kubeflow on Azure gives enterprise and startup AI teams a fully operational, open source MLOps platform in under an hour. It is managed 24/7 by Canonical's engineers. This means you can focus entirely on building models rather than running infrastructure.

Not all open source projects gain traction -- but a few become movements. In this talk, Nariman, Founder of Puter, shares what actually separates the two, based on his experience of growing Puter to 40K+ stars, gaining hundreds of contributors, and over 500K installations. He breaks down how to gain momentum from a project's foundation, attract contributors, and design projects that capture the imagination.

AI spend doubled to $297B in two years — and most companies can't tell you what any of it shipped. Token spend is disconnected from outcomes on the dev side. Agents in production? The invoice is the only signal. Harness Cloud & AI Cost Management (CACM) gives teams unit economics at the inference level, cross-provider visibility across OpenAI, Anthropic, Bedrock, and Vertex AI, and request-level attribution to the agent, session, or workflow that triggered the spend.

Tempo 3.0 introduces a major architectural shift that decouples the read and write paths, with Kafka handling durability on the write side and a new live store serving recent traces on the read side. Blocks are now written at a replication factor of one instead of three, significantly reducing storage overhead. This release also brings TraceQL metrics to general availability, adds comparison operators for filtering metric results at query time, and introduces a new Tempo CLI redact command for removing sensitive trace data on demand without waiting for retention to expire.

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann demos how he integrated AI Observability into the OTel demo, complete with the guards feature he introduced last week, and Principal Software Engineer Yas Ekinci gives a rare glimpse of LLMSpec, the internal counterpart of the o11ybench benchmark that we use to evaluate Assistant.

When a nurse calls for help, every second counts. ER nurses juggle a lot: admission decisions, discharge approvals, orders, physician consults. When they need support fast, they can't afford to chase down the right person manually. Here's how one physician-led medical group solved it using OnPage: Nurses leave a voicemail on a single intake line It's automatically routed into OnPage as an alert to the on-call triage coordinator.

You already use Cribl to tame your telemetry data. Now you can turn that data into apps your teams actually want to use. In this video, we walk through the Cribl app platform and show how real apps solve real problems: guided troubleshooting for noisy incidents, opinionated security views, and exec-friendly ROI dashboards. You’ll see how apps sit on top of Cribl Stream, Edge, Search, and Lake, so you reuse the data and logic you already have instead of building custom tools from scratch.

Does your organization achieve 95% or greater patch deployment success, as demanded by SLAs and regulatory frameworks? Odds are you don't, as most organizations only hit the 90% mark due to common issue: Devices that miss scheduled maintenance windows leave IT teams like yours scrambling to manually, reactively close compliance gaps.

Uber burned through its entire annual AI budget in under 4 months. Here's what went wrong — and what every engineering org should be doing instead. The data: 80% more code is getting pushed with AI… but only 18% of AI-written code actually ships to production. That's not a productivity story. That's a spend problem. If you're scaling AI tooling without real-time monitoring and guardrails, you're Uber.