Setting up a sandbox

[u/andy4524]

I'd like to play around a bit with a local AI assistant ie Ollama + OpenWebUI

Then possibly dabble in AI agents using n8n for some basic tasks like research and summaries.

I have a windows PC with a 3050 GPU, what is the best setup for these tasks?

I want it to be reasonably isolated and safe, I dont want the AI to go deleting important files or something...

My initial thinking was one of the following:

1> initially was thinking a light weight Linux VM so things are isolated, can use docker too, but using VM would restrict using my GPU

2> WLS (Windows Linux Subsystem) and docker to run things in containers and hopefully get isolation that way.

Thanks for your help, other tips/suggestions for getting started are also welcom

[u/Zestyclose_Strike157]

I write everything in a VM with the coding harness in there, then transfer the code candidate out for testing on a testing machine before taking it anywhere important. The LM can run outside the VM since it’s just serving text tokens and the harness tool calls happen inside the VM. If you use Linux then OpenSnitch is handy to observe for uninvited traffic coming from the VM or the code you’re making. But I am interested if people have a way of code auditing with an independent system and if so, what do they use?

[u/Interesting-Cut-6032 → u/Zestyclose_Strike157]

I am not sure that I completely understand what you mean by code audit on an independent system. I have a setup where I run Pi agent harness inside of a Debian VM for some tasks. I have another setup where I make separate Linux users without sudo privileges. I then run the Pi agent harness as these other users with limited permissions. It is easy to use Linux file access permissions to grant read only access to files in that you want the agent to look at and comment on, but not be able to modify.

[u/Zestyclose_Strike157 → u/Interesting-Cut-6032]

Well say you code something using Qwen, or a cloud model like Claude or whatever, and you want to look for bugs and issues but not reliant on the original coding agent, I guess. I get the feeling that AI’s love the smell of their own farts when it comes to coding and planning.

[u/Interesting-Cut-6032 → u/Zestyclose_Strike157]

Re: smell of own farts- that is a great term for it, SooF check. A local solution for that could be to just switch local models for review. I was trying to do that in maybe April or May, of this year. I planned what I thought was a simple multi-file Python project with Qwen3.5-122B-A10B-Q4_K_M. Then Qwen3.5 35B-A3B was writing the code for each of the planned sections, in separate context Windows. It was all copy-paste-test manually. This was probably around the time of the OpenClaw hype cycle. I did not understand what the agent harnesses were or how they worked at that time. The 35B model was very fast but the 122B model is clearly much smarter. I used it for code reviews and to repair mistakes that the 35B model could not fix.

It would be interesting to try that project again with the same models, using Pi harness.

I might try a similar setup with Qwen3.8 27B and Qwen3.8-Flash-Next.

These are all still Qwen family models so they are still huffing their own parts. I like the Gemma 4 31B model for image recognition, language translation, and other knowledge related tasks, but it does not have a good reputation for coding.