This is the sequel to the previous post FreeBSD on a ROCK64 Board. Long story short, I had the chance to resurrect 4 single-board computers that were collecting dust in my office. I decided to install FreeBSD on one of them and it was a success. This time I will show you how and why I installed NetBSD on a second ROCK64 board.

Let’s add connectivity to the FreeBSD board

The process I described in the previous post was fun and I learned a lot. However, I used a USB-to-serial adapter to connect to the board, and even though it was fine to complete the installation, I wanted to have a more comfortable way to connect to the board.

This is a story about an embedded board and a BSD system. The title could have been “How to resurrect a forgotten board and fall in love with BSD operating systems, again”. It all started 6 years ago when I bought 4 Pine Rock64 boards, with a well planned project in my mind. Each board was equipped with a Rockchip RK3328 quad-core ARM Cortex A53 64-Bit processor, 4GB of RAM, 32GB of eMMC storage, and a Gigabit Ethernet port.

In the ever-changing world of artificial intelligence, a new AI assistant called Claude has arrived on the scene, and it’s turning heads. Created by a company called Anthropic, Claude is incredibly smart and can understand and communicate with humans in very natural, human-like ways.

What makes Claude so special is the way it has been trained. The folks at Anthropic fed Claude a massive amount of data, which allows it to truly grasp how we humans speak and write. So whether you’re chatting with Claude casually or asking it to tackle some complex task, it can handle it all with impressive skill.

In the realm of artificial intelligence and machine learning, efficient similarity search is a critical component for tasks ranging from recommendation systems to image recognition. In this blog post, we’ll explore the implementation of vector similarity search in Go, utilizing LinGoose framework for indexing and querying vectors in a Qdrant database.

Understanding Vector Similarity Search

Vector similarity search involves finding vectors in a dataset that are most similar to a query vector. This is fundamental in various AI applications where matching or ranking similar items is required. Qdrant, a vector database, provides a robust solution for such searches.

Introduction

Artificial Intelligence has transformed the way we handle data, and one crucial aspect of AI is similarity search. Whether it’s for image recognition, recommendation systems, or natural language processing, finding similar data points quickly and accurately is a common challenge. In this blog post, we will explore a Go code snippet that showcases how to perform efficient vector similarity search using Redis and the Lingoose Go framework, catering to tech-savvy readers interested in both Go programming and AI.

During the Second World War, the German army dominated Europe thanks in part to the cryptographic technology used in communications: the Enigma machine. To be able to decipher its codes, the British relied on a group of mathematicians, physicists, and linguists who worked in a secret location, Bletchley Park, in England. Their work was a true scientific revolution, a fundamental turning point for the Allies’ victory.