Operations | Monitoring | ITSM | DevOps | Cloud

Every quarter, the same scene plays out in boardrooms across the Fortune 500. The CEO asks: “What is the return on everything the company is spending on AI?” The CTO talks about productivity gains and developer velocity. The CFO points at a cloud bill that doubled but cannot isolate which line items are AI. The board nods politely and tables the discussion until next quarter, when the same question will produce the same non-answer. (If this sounds familiar, you are not alone. Keep reading.)

CloudZero originated as a way to make sense of your cloud costs. Costs spread across bills with billions of line items belonging to resources that might or might not have been tagged (or taggable), spun up by engineers working across teams, on different microservices, features, and products, that served a wide range of customers. Kubernetes. Multi-cloud. Check, check, check.

For many development teams, a load test starts with a set of assumptions. You pick 100 virtual users because it sounds reasonable. You ramp for 30 seconds because that's what the tutorial showed. You set a 500ms threshold because it feels like a good target. The test passes, you ship the release, and production falls over at 6 p.m. on a Tuesday because your synthetic load never resembled how real users interact with your application.

If you’re prepping for your first AI or MLOps interview, the hardest part usually isn’t always the hands-on element. For me, it’s the vocabulary. Interviewers sometimes lob single-word concepts at you (“what’s quantization?”) and watch how far you can carry the thread. The questions sound clear-cut, but each one is really a doorway into a bigger topic, and the interviewer is judging how cleanly you walk through it.

While physical pagers were once the undisputed gold standard for urgent communication, their technological limitations now create dangerous bottlenecks for modern healthcare and IT teams. Carrying multiple devices is not only inconvenient but increasingly inefficient, prompting a widespread shift away from legacy hardware. As of May 2026, the obsolescence of traditional pagers is undeniable.

For IT and SecOps teams, the challenge is not a lack of alerts. It is the sheer volume of noise coming from monitoring tools, security systems, and support channels. Trying to manage this volume manually is not just slow; it’s a recipe for mistakes, team burnout, and critical system failures.

Last month I focused on adding Telegram alerts to OnlineOrNot, making scripted browser checks generally available, and making it clearer to understand why checks fail.

TL;DR Picking the right log management platform in 2026 comes down to three things: how much operational overhead you can absorb, how much AI automation you need, and what you’re willing to spend.

HAProxy 3.4 is a milestone release that significantly advances HAProxy’s legendary flexibility, performance, security, reliability, and observability. Dynamic backend management simplifies integration with modern architectures, memory efficiency improves across a broader range of workloads, native cryptographic operations at the proxy layer open new possibilities for API security architectures, and OpenTelemetry support makes HAProxy a first-class participant in distributed tracing pipelines.

One of the important offerings of the RISC-V Instruction Set Architecture (ISA) is the ability to customize and extend the base instruction set. An initial reaction to hearing this is often to worry about software portability and compatibility, since if every RISC-V CPU offers a slightly different set of instructions, software won’t be portable.