
Kubernetes cheatsheet
Some frequent k8s commands with params
Here is my k8s cheat sheet covering kubenetes most important commands and concepts from installing to running containers and cleaning up:
Some frequent k8s commands with params
Here is my k8s cheat sheet covering kubenetes most important commands and concepts from installing to running containers and cleaning up:
Some frequent docker commands parameters
Here is a Docker cheat sheet covering the most important commands and concepts from installing to running containers and cleaning up:
Awesome new AI model to produce image from text
Recently Black Forest Labs published a set of text-to-image AI models. These models are told have much higher output quality. Let’s try them out
So many models with billions of parameters..
Testing how Perplexica performs with various LLMs running on local Ollama: Llama3, Llama3.1, Hermes 3, Mistral Nemo, Mistral Large, Gemma 2, Qwen2, Phi 3 and Command-r of various quants and selecting The best LLM for Perplexica
Comparing two self-hosted AI search engines
Awesome food is the pleasure for your eyes too. But in this post we will compare two AI-based search systems, Farfalle and Perplexica.
Running copilot-style service locally? Easy!
That’s very exciting! Instead of calling copilot or perplexity.ai and telling all the world what you are after, you can now host similar service on your own PC or laptop!
Testing logical fallacy detection
Recently we have seen several new LLMs were released. Exciting times. Let’s test and see how they perform when detecting logical fallacies.
Not so many to choose from but still....
When I started experimenting with LLMs the UIs for them were in active development and now some of them are really good.
Synching bookmarks across the laptops & browsers?
I’ve tried different tools and came to conclusion I like floccus the most.
Labelling and training needs some glueing
When I trained object detector AI some time ago - LabelImg was a very helpful tool, but the export from Label Studio to COCO format wasn’t accepted by MMDetection framework..
8 llama3 (Meta+) and 5 phi3 (Microsoft) LLM versions
Testing how models with different number of parameters and quantization are behaving.
Ollama LLM model files take a lot of space
After installing ollama better to reconfigure ollama to store them in new place right away. So after we pull a new model, it doesn’t get downloaded to the old location.
It's so annoying to watch all those ads
You can install a browser adblock plugin or addon for google chrome, firefox or safari, but but you will have to do this on each device. The network-wide ad-blocker is my favorite solution.
Let's test logical fallacy detection quality of different LLMs
Here I am comparing several LLM versions: Llama3 (Meta), Phi3 (Microsoft), Gemma (Google), Mistral Nemo(Mistral AI) and Qwen(Alibaba).
Anything can happen in these difficult times
Need to backup the 1) db, 2) filestorage, 3) some other gitea files. Here we go.