in_my_honest_opinion

https://torrentfreak.com/game-pirates-beat-denuvo-with-hypervisor-bypasses-irdeto-promises-countermeasure/

Case in point…

Thing 0 you can use qm terminal

Thing 1 use ZFS no need for LVM

Thing 2 see above for zfs set share nfs

Thing 3 spice vnc is built in

Thing 4 you should check out QubesOS if you haven’t already…

You should especially look at https://pve.proxmox.com/wiki/Install_Proxmox_VE_on_Debian_13_Trixie

What’s the GPU have to do with proper sandboxing of a crack? Or are you just being opinionated and offering unsolicited advice?

Also the DRM absolutely has no fucking clue if it’s running in a qemu or on a spinning rust IDE SCSI from 1992.

Lastly the whole point of a bypass crack is that the DRM is bypassed… it’s being told what system it’s running on.

Claude? That you?

Proxmox --> GPU passthrough --> Gaming VM

Don’t log into anything while you’re playing the game.

Oh absolutely! I try to always start from cli and config files first personally so keycloak made a lot of sense to me. Good luck with whatever you choose.

What’s the difficulty with keycloak? I just set this up last weekend

https://oneuptime.com/blog/post/2026-03-18-run-keycloak-podman-container/view

Sounds good. I’ll pull the latest build to my graphenOS test mule.

I’ll target a secured db as a vault for contacts. That’s a really good idea.

How can I contribute?

It’s a dark pattern but you can use any MFA provider with Microsoft services.

Always buy refurbished laptops, including MacBooks.

Doubt

There’s many good articles out there if you have the time. It boils down to stolen code, forced identification and enshittification.

https://sfconservancy.org/GiveUpGitHub/

https://laoutaris.org/blog/codeberg/

https://blog.joergi.io/posts/2025-09-20-migrate-from-github-to-codeberg/

Did you document the setup? I’m interested in hosting this.

You scoff but this is already being done in China. They desolder good chips from bad cards and add them to a mule card.

https://overclock3d.net/news/gpu-displays/chinese-developers-create-modified-48gb-nvidia-rtx-4090d-and-32gb-rtx-4080-super-gpus-for-the-ai-cloud/

Almost like an LLM wrote it…

I mean what you’re proposing was the initial push of gpt3. All the experts said, these GPTs will only hallucinate more with more resources and they’ll never do anything more than repeat their training data as a word salad posing as novelty. And on a very macro scale, they were correct.

The scaling problem
https://arxiv.org/abs/2001.08361

The scaling hype
https://gwern.net/scaling-hypothesis

Ultimately, hype won out.

will never achieve AGI or anything like it

On this we absolutely agree. I’m targeting a more efficient interactive wiki essentially. Something you could package and have it run on local consumer hardware. Similar to this https://codeberg.org/BobbyLLM/llama-conductor but it would be fully transform native and there would only need to be one LLM for interaction with the end user. Everything else would be done in machine code behind the scenes.

I was unclear I guess, I was talking about injecting other models, running their prediction pipeline for the specific topic, and then dropped out of the window to be replaced by another expert. This functionality handled by a larger model that is running the context window. Not nested models, but interchangeable ones dependent on the vector of the tokens. So a qwq RAG trained on python talking to a qwen3 quant4 RAG trained on bash wrapped in deepseekR1 as the natural language output to answer the prompt “How do I best package a python app with uv on a linux server to run a backend for a …”

Currently this type of workflow is often handled with MCP servers from some sort of harness and as I understand it those still use natural language as they are all separate models. But my proposal leverages the stagnation in the field and leverages it as interoperability.

Ah I see, however you do bring up another point. I really think we need a true collection of experts able to communicate without the need for natural language and then a “translation” layer to output natural language or images to the user. The larger parameters would allow the injection of experts into the pipeline.

Thanks for the clarification, and also for the idea. I think one thing we can all agree on is that the field is expanding faster than any billionaire or company understands.

Sure, but giant context models are still more prone to hallucination and reinforcing confidence loops where they keep spitting out the same wrong result a different way.