So, here’s something interesting, there’s an API in Go’s runtime package called runtime.SetFinalizer. This little feature lets you set a “finalizer” for an object.

Machine learning models are rapidly becoming more capable; how can we make use of these powerful new tools in our Go applications?

For top-of-the-line commercial LLMs like ChatGPT, Gemini or Claude, the models are exposed as language agnostic REST APIs. We can hand-craft HTTP requests or use client libraries (SDKs) provided by the LLM vendors. If we need more customized solutions, however, some challenges arise. Completely bespoke models are typically trained in Python using tools like TensorFlow, JAX or PyTorch that don't have real non-Python alternatives.

In this post, I will present some approaches for Go developers to use ML models in their applications - with increasing level of customization. The summary up front is that it's pretty easy, and we only have to deal with Python very minimally, if at all - depending on the circumstances.

when is a natural language date/time parser with pluggable rules and merge strategies

The errorx library provides error implementation and error-related utilities.

In this post, we'll explore the profiling tools we use. Golang provides a rich set of profiling tools to help uncover code bottlenecks.

For applications deployed in orchestrated environments (e.g., Kubernetes), graceful handling of termination signals is crucial.

What is the smallest thing in Go? Well, I’m glad you asked!

A Go library that generates concise, unambiguous, URL-safe UUIDs. Based on and compatible with the Python library shortuuid.

This is a chapter from my book on Go concurrency, which teaches the topic from the ground up through interactive examples.

Let's skip the long talk about concurrency and parallelism and jump right into writing a concurrent program in Go!