Operations | Monitoring | ITSM | DevOps | Cloud

In case you don't know the back story, in order for me to play with radios and label it "work," I've created a PostgreSQL database running on AWS Aurora. The db is fed from API calls to aprs.fi through Lambda functions on AWS. Some of the DDL & code is mine. Some is from Claude. Neither of us paid much attention to indexing when we were putting things together.

Engineering teams are great at innovating and delivering products, but the work that's required to maintain them over time and keep them running well tends to get deprioritized. Planning processes are designed to move features forward, not to catch whether those features are generating too many alerts, degrading in performance, or creating compliance exposure over time. As a result, that class of work accumulates quietly.

DCIM software supports edge computing, IDF closets, and distributed IT environments by providing centralized asset management, real-time power and environmental monitoring, 3D digital twin visualization, capacity planning, and physical security management across every site from core data centers to remote sites and IDF closets.

Spreadsheets and Visio diagrams may work in small, static environments, but they cannot maintain accurate, real-time records at the port level, track relationships between assets, or support the pace of change in modern operations. DCIM software is purpose-built for those demands. In this blog post, we'll cover what network documentation actually requires, where Excel and Visio fall short, and how DCIM software addresses those gaps.

E2E Testing Has a New Bottleneck, and It's Not the Code End-to-end (E2E) testing has always been the hardest part of a QA strategy. You're simulating real users, navigating real flows, validating real outcomes across browsers, environments, and data states that never hold still. Traditional test automation tackled this with scripts: rigid, deterministic sequences tied to element selectors and hard-coded values. They worked until the UI changed. Or the data changed.

It's 4:17 PM on a Friday. You've been working on this feature for three days. The code is clean, the tests pass locally, and you're ready to ship. You push to staging and... nothing works. The API returns 500s. The database schema doesn't match. The feature flags are in some impossible state. You open Slack. Someone in-environment has already posted: "Hey, who deployed to staging? My tests are all failing now." Three other developers respond.

This blog is based on the webinar, “AI panel: The next wave of AI technology”. You can watch the full recording by clicking here! The pace of AI innovation is reshaping research, business, and everyday life. However, as breakthroughs in Large Language Models (LLMs) and high-performance computing accelerate, they bring new technical challenges around scale, efficiency, and reliability.