This post is going to show you how to use Listen/Notify in your Golang applications with pgx Conn.WaitForNotification

Go bindings for Graphviz

A dead simple Go library for sending notifications to various messaging services.

SQL-first Golang ORM

Asynq is a Go library for queueing tasks and processing them asynchronously with workers. It's backed by Redis and is designed to be scalable yet easy to get started.

BemiDB is a Postgres read replica optimized for analytics. It consists of a single binary that seamlessly connects to a Postgres database, replicates the data in a compressed columnar format, and allows you to run complex queries using its Postgres-compatible analytical query engine.

What does it mean to keep security in mind when writing Go code? Answering this question in one short article seems impossible. For this reason, we will narrow it down to a few specific practices.

TUI for journalctl, logs in the file system and docker containers for quick viewing and filtering with fuzzy find and regex support.

vgt is a tool for visualising Go test results in a browser.

A blazing fast and safe Go package for serializing JSON.

The easiest way to move and transform data between PostgreSQL databases using Logical Replication.

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.