Operations | Monitoring | ITSM | DevOps | Cloud

We always knew that Airbnb’s engineering is operating on a completely different scale, and their new high-volume metrics pipeline is proof of that. This is one of those rare stories where scale and efficiency go hand in hand - they modernized their observability stack with open source components and reduced cost by an order of magnitude. Airbnb is now processing more than 100 million samples per second on a single production cluster.

AWS delivers CloudWatch metrics in OpenTelemetry format via Firehose, but AppSignal uses its own internal format. Building the parser to bridge these two formats presented several technical challenges. The metrics arriving through this pipe power AWS automated dashboards. When AppSignal detects metrics from a supported AWS service, it creates a dashboard for it automatically, with pre-built charts grouped by category: compute, databases, networking, messaging, storage, and others.

Here are the three records most teams forget to monitor — and what happens when they break.

Every week, thousands of engineers, SREs, and IT leaders type questions about anomaly detection into ChatGPT, Reddit, and Stack Overflow. They are all trying to answer the same underlying question: why do production incidents keep catching us off guard, and how do we stop them?

Shai-Hulud is back - this time being lighter, faster and more automated than before. This new wave, termed as Mini Shai-Hulud, has affected a number of packages from tanstack, uipath, opensearch-project and mistralai among others over the past few weeks, with the latest series of major compromises coming on 19th May, 2026 on major organizations openclaw-cn and antv. Check an extensive list of affected packages here.

Three weeks into a platform modernization project, this question landed in my inbox: "Why does our deployment pipeline take 40 minutes instead of four?" This is artifact repository sprawl in practice, and it does more than slow pipelines. It fragments your security posture, your compliance evidence, and your ability to answer basic questions like "what's actually running in production right now?".

Continuous integration (CI) costs can escalate quickly as engineering teams scale. While most organizations focus on cloud bills, the true cost of CI includes slow build times, developer wait time, inefficient test execution, and overprovisioned infrastructure. CI cost optimization is the practice of reducing the total cost of CI pipelines by improving build efficiency, minimizing compute usage, and eliminating unnecessary work without slowing down development.

There’s a growing ecosystem of AI coding agents: Claude Code, Cursor, Copilot, Codex, Gemini CLI, Windsurf, and others. They’re good at writing code, but they don’t inherently know how to instrument that code for observability, configure monitoring infrastructure, or troubleshoot production systems using real telemetry data. That knowledge lives in documentation, runbooks, and the heads of your senior SREs.

Certificate automation does a lot of work on your behalf. Agents running on your servers, talking to certificate authorities, deploying certs to your infrastructure. At some point someone (your CISO, your auditor, or your own brain at 3am) is going to ask: what exactly happened, and when? Today we’re shipping audit logs. Every action taken in CertKit is now recorded: logins, invitations, certificates added, issued, renewed, revoked, and deployed. Agent registrations, approvals, and config changes.

Technology companies are being pushed to deliver faster outcomes while justifying growing investment in AI, SaaS, and digital infrastructure. But productivity does not improve just because new tools are deployed. It improves when employees can use those tools without the constant drag of slow devices, unstable applications, and fixes that do not fully solve the problem. That is the productivity tax of digital friction.