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From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

Have you ever wondered what goes behind those annoying 'Oops, something went wrong' messages on the web? You're not alone. A simple Google search for the most common HTTP errors throws up names we've all seen at least once: Not Found, Unauthorized, Forbidden, Internal Server Error, and Bad Request. It's like a secret code that both frustrates and fascinates.

This blog post explores the technical and strategic benefits of deploying open-source AI models on Ubuntu. We’ll highlight why it makes sense to use Ubuntu with open-source AI models, and outline the deployment process on Azure.

Ever since generative AI burst into public consciousness, business executives and IT leaders have been intrigued by the potential of generative AI to transform IT. As it turns out, generative AI has already begun revolutionizing ITOps and we are excited to be at the forefront of this game-changing technology through the ScienceLogic SL1 Hollywood release.

There’s a sentence that strikes fear into the heart of every frontend developer I've ever met: Users are reporting issues, and we don't know how to replicate them. What do you do when that happens? Do you cry? Do you mark the issue as wontfix and move on? Personally, I took the road less traveled: gave up frontend engineering and moved into product management (this is not actually accurate but it's a good joke and it feels truthy).

Technology investments in today’s digital economy are crucial in achieving your business goals and gaining a competitive edge. As business strategies evolve and technology advances, IT environments are becoming increasingly complex and hybrid while budgets remain stagnant. Choosing the right solution to manage your technology stack is vital to deliver superior application experiences and meet your performance, availability, and operational requirements.

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.

We have improved our custom metrics offering with two recent Python releases. In release 1.1.1 of our Python package, we added the add_distribution_value helper, and in version 1.2.0, we added support for minutely probes, so you can measure the distribution of key data points in your Python application. In this blog post, we'll show you how to set up custom metrics in your Python application to gain valuable insights without sifting through logs or querying databases.

Palo Alto Networks High Availability has been one of our most highly requested feature integrations with Megaport Virtual Edge. Now, it’s available. Here’s how you can use it for a more reliable, redundant network.