Operations | Monitoring | ITSM | DevOps | Cloud

Recently, I’ve been playing around with the ansible promise type by Fabio Tranchitella. It’s amazing how easy it is to leverage the benefits of agents with all the existing automation from the ansible community. Hence, I wanted to share it in a blog post, with an emphasis on easy. Let’s start off by creating a new cfbs project in a cfengine-ansible directory. Make sure to answer yes on the prompt to build on top of the default policy set.

The Blackwell architecture arrived in 2024 as NVIDIA's answer to the next era of AI compute. The B200 set a new standard for inference performance, memory capacity, and training throughput, and many teams are still ramping up their use of it today. Then came Blackwell Ultra. The B300 is built on the same silicon foundation as the B200: same dual-reticle die design, same TSMC 4NP process node, same NVLink 5 interconnect.

Coherent optics is an advanced optical transmission technology that uses the modulation of light amplitude and phase, combined with digital signal processing, to transmit massive amounts of data over existing fiber optic cables, dramatically increasing network capacity by maximizing spectral efficiency without requiring new physical fiber deployments. Next-generation transport networks face a constant challenge: scaling capacity to keep up with relentless data growth.

Guest post by Markus Opolka, Senior Consultant at NETWAYS. Originally published on the NETWAYS blog as “Icinga und Prometheus integrieren” and “Alertmanager-Icinga-Bridge – Ein Signalilo Fork”, combined and adapted for the Icinga blog with permission. Icinga and Prometheus are both great monitoring solutions, however, their focus is different. In this article, we look at how to integrate both monitoring systems and utilize the strengths of both tools.

In July’s episode of This Month in Datadog, Ruxanda Lueck joins Jeremy for a conversation about how you can confidently evaluate and release features that contain AI-generated code. She also discusses her career trajectory from containers to AI, how agentic workflows impact trust during feature development, and the challenges of testing nondeterministic agent behavior.

Python is the native language of AI-assisted development. Models are really good at writing it, and a lot of people are now shipping it without ever having written much Python themselves. The whole thing is really simple. You prompt an app, Claude Code or Cursor produces a working Flask or FastAPI backend, and you’re live in a few hours. However, there’s still a big difference between “it works on my machine” and “it works in production”.

The hidden cost of multicloud lives in operations and governance. Each provider develops its own console, runbooks, and audit posture, and the gaps widen quietly. The bill comes due at the next migration, audit, or incident.

Software supply chain engineering has reached a critical inflection point. As autonomous AI coding agents transition from generating autocomplete suggestions to planning, writing, reviewing, and deploying entire software pipelines without humans in the loop, the connection between human intent and production binaries is fracturing.

When a Terraform run feels slow, most teams are flying blind. The run log in HCP Terraform tells you what happened, but not what took so long—and it certainly doesn't roll up across hundreds of runs so you can spot a trend.