Operations | Monitoring | ITSM | DevOps | Cloud

When I first started using AI (Cursor, to be more specific) for coding, I was very impressed to see how it could generate such high-quality code, and I understand why it's now one of the most widely used tools for software engineers. As I continued to use them more regularly, I realized they are far from perfect. Their effectiveness depends heavily on how they are used and the context in which they are applied.

From cloud on-ramps to virtual edge services, Megaport’s expansion into India from March 2026 will support modern network design in the market. India’s digital economy is growing at incredible speed. Cloud and AI adoption is accelerating, data volumes are scaling, and more businesses are designing architectures that extend well beyond a single data center or cloud region.

Last year, we collaborated with two design student teams from Loughborough University in the UK. These students were challenged to work on open source project briefs. Team 1 focused on non-code contributions, while Team 2’s brief was to create a unified documentation experience, giving them a chance to apply their design skills to real-world problems within the open source ecosystem.

TL;DR: Prometheus pays cardinality costs at write time (memory, index). ClickHouse pays at query time (aggregation memory). Neither is "better":they fail differently. Design your pipeline knowing which failure mode you're accepting. -- Every month, someone posts "just use ClickHouse for metrics" or "Prometheus can't handle scale." Both statements contain a kernel of truth wrapped in dangerous oversimplification.

Since Honeycomb started, it has had a weakness: too many choices. Every field, custom or standard, hundreds of them, all are free to group, filter, and visualize in dozens of ways. Which ones are interesting? Honeycomb exists to help people understand custom software. It doesn’t pretend to know what matters in your application. That’s an interpretive task, not programmatic. Hey, computers can do interpretation now!

Modern security operations face a growing architectural challenge: collect telemetry from everywhere, process it in real time, and route it to multiple platforms while maintaining data sovereignty, avoiding agent sprawl, and keeping costs under control. Single-model collection strategies force security teams to make compromises. Agent-only models create operational overhead and maintenance risk. Agentless-only approaches simplify operations but limit depth and flexibility.