Operations | Monitoring | ITSM | DevOps | Cloud

As I sit down to write this, I can’t help but feel a wave of nostalgia wash over me. I’m Sara Stamas, VP of Marketing at GitKraken, and I’ve had the incredible privilege of being part of this company for over a decade (yes, before GitKraken was even a twinkle in an Axosoft developer’s eye).

GCP’s recent outage on June 12th was a reminder of just how interconnected modern architectures are. The 2 hour and 28 minute outage affected dozens of companies and spanned 80+ Google services and products. But what was really illuminating was just how far the outage spread due to hidden dependency risks. Many companies that don’t run on GCP were startled to find their services suddenly affected because they had dependencies or depended on vendors that did use GCP.

Machine learning (ML) pipelines are increasingly being treated like software; built, tested, deployed, and monitored using automated tooling. But while infrastructure as code and microservices have matured with security best practices, ML systems often lag behind. The truth is, your ML pipeline is part of your software supply chain and it is vulnerable.

Code reviews are a crucial step in maintaining code quality, but many developers find them tedious and inconsistent. What if you could get helpful feedback automatically, as soon as a pull request is opened? In this tutorial, you’ll learn how to set up and integrate an AI-powered code review agent into your Go project. The agent uses the OpenAI API to post contextual suggestions and praise directly on pull requests.

Your code passes locally, CI is green, and the deploy goes through. Then production throws a 500, and the trace isn’t helpful. And here, event logs help. A log captures timestamped records of what the app did HTTP requests, DB queries, cache misses, retries, failures. These entries give you enough context to debug without reproducing the issue locally. Especially when dealing with distributed systems, logs are often the only consistent source of truth.

In our Go projects, we rely on a consistent and battle-tested stack of libraries that help us build reliable, maintainable, and scalable systems. We started using Go in our stack many years ago (before Go v1) and therefore many of our choices have changed over the years. Here in this post, I wanted to share some of the libraries we use regularly to power our Go apps.

In a recent presentation, Verizon’s Steve Ownes discussed their strategic initiative to accelerate the decomissioning (decom) of TDM switches, underlining the significance of repurposing legacy infrastructures in favor of modern architectures. Ribbon’s guest Steve Owens kicks things off with a light-hearted reference to "Sanford and Son” showing how relics can be transformed into gold through effective management and innovation.

In recent months, my conversations with fellow technology leaders have consistently revolved around two key themes: how we leverage AI to drive innovation and efficiency, and how we mitigate the inherent risks associated with AI. However, I’ve noticed a concerning gap – while enterprises are busy strategizing the adoption of AI to enhance productivity, reduce costs, and outpace competitors, very few are addressing how AI is being actively used today by their own teams.