Self-Hosting

Here’s a step-by-step walkthrough of installation of a 3-node K3s cluster on bare-metal servers (1 master + 2 workers).

Comparing self-hosting Kubernetes distributions for hosting on bare-metal or home servers, focusing on ease of installation, performance, system requirements, and feature sets.

I’m comparing self-hosted Kubernetes variants that suit the Ubuntu-based homelab with 3 nodes (16GB RAM, 4 cores each), focusing on ease of setup and maintenance, support for persistent volumes and LoadBalancers.

GitHub Actions is an automation and CI/CD platform within GitHub, used to build, test, and deploy your code based on events like pushes, pull requests, or on a schedule.

Here’s a Docker Compose cheatsheet with annotated examples to help you master Compose files and commands quickly.

Here’s a detailed breakdown of Obsidian as a powerful tool for personal knowledge management (PKM), explaining its architecture, features, strengths, and how it supports modern knowledge workflows.

Nvidia is about to release NVIDIA DGX Spark - little AI supercomputer on blackwell architecture with 128+GB unified RAM and 1 PFLOPS AI performance. Nice device to run LLMs.

Let’s have a quick look at Matomo, Plausible, google and other web analytics providers and systems available for self-hosting and compare them.

Here is my favorite set of steps when doing fresh install of the Ubuntu 24.04. What I like here - not need to install NVidia drivers! they are installed automagically.

Dokuwiki is a selfhosted wikipedia which can be hosted easily on premises and not requiring any databases. I was running it dockerised, on my pet kubernetes cluster.

Let’s compare prices for top-level consumer GPUs, that are suitable for LLMs in particular and AI overall. Specifically have a look at RTX 5080 and RTX 5090 prices. They have slighty dropped.

Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows, - entirely in Python code, offering a flexible and powerful alternative to traditional, manual, or UI-based workflow tools.

Since standard Ollama doesn’t have a direct rerank API, you’ll need to implement reranking using Qwen3 Reranker in GO by generating embeddings for query-document pairs and scoring them.

In this test I’m comparing how different LLMs hosted on Ollama translate Hugo page in English to German. Three pages I tested were on different topics, had some nice markdown with some structure: headers, lists, tables, links, etc.

This little Reranking Go code example is calling Ollama to generate embeddings for the query and for eache candidate document, then sorting descending by cosine similarity.