Operations | Monitoring | ITSM | DevOps | Cloud

Schedules are a core part of any on-call system. In ours, they define who to page and when. But people use them in lots of other ways too: checking their next shift, asking for cover while at the gym, keeping a Slack user group up to date, or updating a Linear triage responsibility. For many of our customers, they’re one of the main ways they interact with our product, and as they’re such a foundational part of On-call, it’s very important they work well.

I live in Manchester, England now. I moved here from Texas last summer (which is its own story), but the thing I wasn't prepared for is how the Industrial Revolution isn't history here. It's the city itself. And if you're American like me, you might need to hear this: the Industrial Revolution didn't start in the US. It started here. Manchester is where the modern world was born. You see it everywhere. The old cotton mills converted into apartments.

As far back as 2020, Barclays found that 43% of enterprise CIOs were already planning to bring workloads back from the public cloud to on-premises or private cloud infrastructure1. Since then, IDC, Gartner and a host of vendor surveys have tracked an increase in this intention.

Argo Rollouts exposes Prometheus metrics on port 8090 — but the docs lie about which labels exist. Here's how to scrape them into Last9, build a canary dashboard, and use Last9 as an automated AnalysisTemplate gate, including the auth and base64 gotchas. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Kubernetes is an open-source container orchestration engine that is used to automate containerized application deployment, scaling, and administration. It is an open-source management platform that can be used to manage containerized workloads and services, as well as declarative configuration and automation. Kubernetes is a framework for running distributed systems in a resilient manner. It handles scaling and failover for your application and provides deployment patterns and other features.

Application security testing tools promise coverage and accuracy, but teams often struggle just to get started. One of the biggest friction points in dynamic application security testing is configuring authentication correctly so a scanner can even access a target application, let alone API endpoints that power the functionality. Whether it’s API keys, bearer tokens, or custom auth flows, setting up authentication for scans frequently requires trial-and-error and engineering support.

llm-d is an open source solution for managing high-scale, high-performance Large Language Model (LLM) deployments. LLMs are at the heart of generative AI – so when you chat with ChatGPT or Gemini, you’re talking to an LLM. Simple LLM deployments – where an LLM is deployed to a single server – can suffer from latency issues, even with just one user. This can be because of lack of memory-bandwidth on the server, or because of KV cache pressure on system memory.