Self-Hosting on InternetException

AI agent critique of itself

Thu, 23 Apr 2026 11:30:40 +0200

I like to self-host my own services. I have my own email server. I have my own cloud storage and sync and backup. I want to have this secure and safe while having my data private.

But I need to learn and be up to date with current technology.

I decided to use Junie with my own, self hosted models that I run on my own hardware . In the docs they say it can be run on top your own custom provider and model. But it does not work with Rider plugin. You are limited to their own limited list of models.

How I taught my AI assistant to read websites

Tue, 03 Mar 2026 22:16:00 +0100

Main problem with running your own AI assistant is speed. Nanobot is lightweight but main problem it time od the reponses generated by the model. I am running right now Qwen 3 27B parameters. Average reponse varies but it is at least few seconds. If I would have faster GPU than Radeon 7900 XTX than probably it would faster but prices of GPUs are crazy now.

One of the things I have been testing my little chatbot with was searching for stuff on the internet. For example I like to check prices of some PC parts from time to time. Just out of curiosity and to have a knowledge how crazy it is (NVMe SSDs of 8TB sizes are at least 2x times pricier than last year, on the other hand salary for my profession dropped to 60-70% last 3 years, without inflation, with inflation it would be around 58%). Things are crazy.

Self hosting your AI assistant efficiently

Sun, 01 Mar 2026 06:27:00 +0100

I wrote previously why running models that you want to use on your daily driver is not great. Basically it is not convenient and it is fine for testing but if you want to run while doing something else it not great experience.

Of course you can have more powerful device so you will not notice few models running in the background but this is like running a fan with heater under your desk.

Running LLM on your daily PC and why it is not good

Fri, 27 Feb 2026 15:11:00 +0100

Two days ago I started playing a bit with my own AI assistant. It has been very great and funny experience so far but with few wrinkles. For example:

performance of new models on previous generation GPU (Radeon RX 7900 XTX) is surprisingly good with average of 28t/s but it takes whole VRAM and put a load on PC overall.
It takes whole lot of disk space. I.e. one vllm docker image to download and extract needs about 18gb; this seems a bit excessive.
Rocm installation needed a bit like 9gb to install all of its binaries. How much code do you need to produce 9gb of compiled code? This is crazy.
I have 16c/32t CPU and it seems to be enough for most of my workload but with llm in the background sometimes most of it is in use.
32gb is not enough, but with current prices I can suffer for now. :)
If you want to swap models a lot you need a lot of disk space since bigger models takes several gigabytes.
You need decent internet speed to be able to download it.

Yes you may say that it is fine in 2026, everybody have fiber connection now! Yes but when you want to test something and new docker image is downloading in with 1mb/s speed, you may have a walk in that time.

Genereting proxies during runtime using Reflection.Emit

Fri, 07 Oct 2016 12:29:00 +0200

Reflection.Emit is very powerful tool. It creates IL code and since C# is converted into IL too, we have the same functionality as in C# and even more. It is very powerful and at the same time very complicated. Because of that it is worth to discuss how and for what it should be used. One idea is to create dynamic code with automatic implementations of interfaces - proxy types.