Operations | Monitoring | ITSM | DevOps | Cloud

I had the pleasure to be invited by Canonical’s AI/ML Product Manager, Andreea Munteanu, to one of the recent episodes of the Canonical AI/ML podcast. As an enthusiast of automotive and technology with a background in software, I was very eager to share my insights into the influence of artificial intelligence (AI) in the automotive industry.

With more developers working remotely, traditional code reviews have begun to shift. Classic water cooler conversations have turned into pings on Slack, and collaborative office spaces have transformed into stand-alone home setups. Remote work clearly has many advantages, but it can also leave developers feeling isolated. Asynchronous communication introduces massive bottlenecks for efficient feedback and creative brainstorming, particularly during code reviews.

While many of us have already heard of Kubernetes, or may even be leveraging it within our technology stacks, it’s still important to remember that the platform is undergoing massive adoption and evolvement. Due to its relative infancy, Kubernetes is ripe to allow for integrating new technologies, like Artificial Intelligence (AI) and Machine Learning (ML). As an open-source platform, Kubernetes orchestrates containerized applications, ensuring they run efficiently and resiliently.

Friends of this blog know that I am a big fan of building internal tools, or as we call them, "tools that help scale people". As the name suggests, internal tools are used, well, internally and as such usually will require their access to be restricted to the company's staff and network. In the past, I've written about how to use Tailscale for authentication of internal tools using HTTP. In this post, I will show you how to use Tailscale for gRPC authentication in Golang.