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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Building vs. Buying your platform: The honest framework nobody discusses

Most organizations get the build versus buy decision wrong in the same way. They underestimate the cost of building while overestimating the cost of buying. In the recent Konstruct monthly webinar with M R Rishi (Platform Engineer at Civo), we explored the discussion surrounding whether you should build or buy your platform. If you want to watch the full discussion, watch the recording here.

Language AI to physical AI explained

What is physical AI? Physical AI embeds machine learning directly into hardware, enabling algorithms to interact, move, and perform autonomous tasks in the physical world. Traditionally, robots relied on precise, hardcoded coordinates; if an object shifted by a single millimeter, the entire system failed. Today, robotics is moving past rigid automation toward truly adaptive architecture. Neural networks help machines process raw sensor data in real time. Consequently, machines can dynamically reason through the unpredictable physical world.

Should you self-host your Git repos? We are, and here's why.

Git is decentralized by design, yet most organizations have funneled their entire source of truth (repositories, CI triggers, access control, release tooling) onto one third-party platform that nobody on the team operates or can inspect. That concentration was a fair trade when self-hosting meant adding massive capex overhead, but with modern architectures and platforms, that's no longer the case.

High Cardinality in ClickHouse at Scale: What Actually Breaks

ClickHouse swallows high-cardinality telemetry at ingest, then breaks at query time weeks later. Here is what fails, and how we keep it fast in production. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

The debugging crisis nobody's talking about: AI, abstraction, and the skills gap

Here's a scenario that's playing out in engineering teams across the industry right now. A developer uses AI to rapidly prototype a microservice. The code works. They deploy it to production. Six months later, something breaks. The system is under load, a database connection pools, and the service starts failing in subtle ways. The engineer pulls up the code, but here's the problem, they didn't write it. An AI assistant did. They don't understand the flow deeply. They don't know where to look first.

Cortex catalog data now flows into Rootly

Incident response is a context problem. The first minutes of any incident are spent reconstructing what the affected service is, what it depends on, and who owns it. That reconstruction happens during the worst possible window. The Cortex catalog already holds this data: services, teams, domains, and the relationships between them, maintained by the engineers who run those systems.