Operations | Monitoring | ITSM | DevOps | Cloud

HAM Audit: How InvGate Asset Management Helps You Pass

A Hardware Asset Management (HAM) audit is a formal check of whether your hardware inventory reflects physical reality. It covers what devices exist, where they are, who has them, what state they're in, and how retired assets were documented out of the system. Most organizations don't fail HAM audits because their IT teams are negligent.

Works on my machine: how we use AI to reproduce reported bugs

Sentry’s SDK teams maintain and support SDKs for a vast ecosystem of languages and frameworks. See our release registry for a source of truth. We’re currently at 159 published packages across the entire ecosystem. If you use it, we probably support it. All of these SDKs are open source and have their own GitHub repositories that we maintain on a daily basis. And like any other open source project, we get tons of bug reports and issues on these.

Search and act across Datadog to resolve issues faster with Bits Chat

Finding the right information across dashboards, monitors, and telemetry sources takes time, even for experienced engineers. When something breaks, it often means figuring out where to start, rebuilding queries, and jumping between metrics, logs, and traces before you can take action. The challenge isn’t a lack of data but the effort required to surface the right information at the right moment.

Running the OpenTelemetry Collector as a Lambda

The OpenTelemetry Collector is usually deployed as a long-running process: a sidecar, a DaemonSet, an EC2 instance, a docker container on my computer. It sits there listening for telemetry. That's fine when I want to send telemetry all day, but not when telemetry is rare. Like right now, when I have an agent defined on AgentCore, and it runs a few times a week maybe. Or my website that hardly sees any traffic. Can I run the OpenTelemetry Collector as a Lambda function?

Top Considerations When Evaluating DCIM Vendors

Choosing a Data Center Infrastructure Management (DCIM) platform is one of the more consequential decisions a data center team will make. Get it right, and you gain an accurate digital twin of your physical infrastructure, a single source of truth across teams, improved operational visibility, and a platform for planning, reporting, and automation. Get it wrong, and you risk a failed deployment, a platform that doesn't fit your needs, or a shelfware investment that's hard to justify renewing.

AI Automation in Telegram: How Neuro Commenting Changes Community Engagement

In recent years, artificial intelligence has significantly transformed digital communication and social media management. One of the fastest-growing platforms benefiting from this evolution is Telegram. As communities scale and content volume increases, manual engagement becomes inefficient. This is where AI-driven solutions such as neuro commenting and automation tools play a crucial role in maintaining active, responsive, and engaging communities.

Why More SysAdmins Are Moving to aaPanel in 2026

Server management doesn't look like it did five years ago everything's moving fast, and sysadmins are under more pressure than ever to keep things smooth without blowing budgets or eating up resources. Lately, one name keeps popping up across every forum and tech chat: aaPanel. People who spent years with the same old paid panels are jumping ship. I'll break down exactly why that's happening-and why you might want to join them.

MiniMax M2 vs M3: What's Actually Different and Which One Should You Use?

If you've been following open-source AI in 2026, MiniMax has probably crossed your radar at least once. The Shanghai-based lab has been quietly releasing models that punch well above their weight - and now, with M3 dropping on June 1, 2026, the question everyone's asking is: does it replace M2, or do they serve different purposes? Let's break it down clearly, without the hype.

The algorithmic driver: navigating liability and risk in automated vehicle safety systems

Automated vehicle safety systems are reshaping how drivers, manufacturers, and legal professionals understand risk and accountability. As these systems become more advanced, questions surrounding Product liability in automated vehicles and the allocation of fault in accidents are increasingly complex. This article examines the key issues in assigning responsibility and managing risk in a landscape dominated by algorithmic decision-making within ADAS liability frameworks.