Operations | Monitoring | ITSM | DevOps | Cloud

A major new study from Harvard Medical School and Beth Israel Deaconess Medical Center has found that a large language model (LLM) outperformed physicians across a wide range of clinical reasoning tasks, including making emergency-room triage decisions from messy, real-world patient data. The findings, published April 30 in Science, represent one of the largest comparisons yet between AI and physicians on clinical tasks.

Many security teams are looking to move off Splunk, whether to reduce licensing costs, consolidate their SIEM, or take advantage of Google SecOps' built-in threat intelligence and YARA-L detection capabilities. But migrations aren’t easy, and no one wants to run blind while they evaluate and move to a new platform. With OpenTelemetry and Bindplane, you can easily make the switch to SecOps without impacting your existing stack.

How do you make search faster, smarter, and more scalable? During our recent webinar, I sat down with Luke Patterson, senior product manager at WP Engine, and Delphin Barankanira, independent software vendor partner engineering lead and data & AI specialist at Google Cloud, to answer that question. We dug into the mechanics behind WP Engine’s ability to deliver near-instant updates to over 2 million users.

Most platform and observability teams have logs they know are noise. These could be throwaway health check logs, forgotten DEBUG logs, or verbose INFO logs from little used services that only serve to inflate your bill. Regardless of what they contain and why they're there in the first place, the hard part is getting rid of them. Centralized teams want to easily and quickly prevent these logs from being ingested, without having to work with toilsome infrastructure change management to do so.

A few weeks ago, a group of engineering leaders I trade notes with got into it over a question none... A few weeks ago, a group of engineering leaders I trade notes with got into it over a question none of us has a clean answer to: How much should you let an engineer spend on AI? One SVP at a company of similar size and stage is in calibration mode and capping engineers at $200 per month. Hit the cap, you can self-bump by $100. Hit that, you need your manager. I told the thread our number. $5,000.

Your version and deploy health data no longer lives on separate pages. Versions and Deploys are now Dashboard cards, visible by default the next time you open Rollbar. Errors, versions, and deploys in one view, no more tab-hopping to piece together release health.

TL;DR: Attribute. More information on one event gives us more correlation power. It’s also cheaper. When you want to add some information to your tracing telemetry, you could emit a log, create a span, or add a piece of data to your current span. Adding a piece of data to your current span is the best! Usually.

If an AI agent in your development workflow needed to spin up a test environment tonight, how many manual steps would stand between the request and the environment being ready? By early 2026, AI agents have transitioned from simple code assistants to first-class platform citizens. They are running test suites, analyzing performance, and triggering deployments.

With AI reshaping every corner of the modern business, the highest-value workloads are often locked behind complex regulatory frameworks. Yet many organizations are still running them on infrastructure they don't fully control, trusting external platforms to decide where their data lives, where workloads run, and how their AI operates. Civo was built to change that.

Modern distributed systems fail in ways that can bypass even well-designed isolation patterns. When a failure is actively propagating across services at four in the morning, the question shifts from “how do we limit the blast radius” to “how do we confirm what it actually is.” Monitoring shows which services are in the impact zone, but it cannot show what code path caused the failure to spread, or whether it has stopped.