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Uh oh, looks like you didn’t read far enough:

On Monday, two days after the incident occurred, Mr Crane confirmed that the data had been recovered. The Independent has reached out to Anthropic and Cursor for comment.

So again… Well, do I need to paste it again?

So again, because we make backups before letting AI touch anything:

“Hey slop machine, you fucked up. Restore the last backup and document the mistake you made in AGENTS.md so we avoid making that mistake again. Im going to go make a coffee, brb”

Oops, slop machine broke something.

“Hey slop machine, you fucked up. Restore the last backup and document the mistake you made in AGENTS.md so we avoid making that mistake again. Im going to go make a coffee, brb”

Hardware I’m running:

• 8/16 AMD CPU
• 32GB system RAM
• 12GB GPU VRAM

I’m mainly using these MoE models:

• Qwen-3.6-35B-A3B

• Q4_K_M quant quality

• 128k context (conversation length before it compacts)

• Gives me about 260 prefill and 19 token gen speeds

• Gemma4-26B-A4B

• Q8 quant quality

• 128k context length

• Gives me about 190 prefill and 14 token gen speeds

I created git repos on my main workstation for each homelab server/service I maintain that keeps:

• documentation
• notes
• lessons learned
• scripts, configs
• runbooks
• backup details
• security audit details
• log items that need attention
• infrastructure

I just point a local LLM (offline model that runs on my workststion) into those repos and ask it to perform certain things on those servers.

I have it run pre backups before making changes, then post backups once its done.

This question is how new apps are born

Honestly, thanks for writing all that out. I think I understand where you are coming from. You’re basically saying that this is an attribution issue and the LLMs cant determine that.

But at the same time, now you arent able to use properly attributed software either. Any open-source project that allows any user to contribute has a chance to be using unattributed code. Upstream code that your operating system uses to function could also be using unattributed code that your code runs through and could output tainted results.

The genie is out of the lamp, and it has already made the wishes of the user come true and that wish has landed in your repository. I just don’t see how its feasible to expect any open-source codebase to be fully compliant with full attribution. It never was to begin with, whether LLMs were used or not, but it was expected for people to abide by it.

Now that its easier to generate code off of the training data that went into the model, you have to be real selective on what code is used.

My suggestion for open-source projects is to recommend an open-weight model that has had its training data thoroughly audited so its not a black box. Have it cite code responsibly. Maybe only allow pull requests from users who use that particular model if they must use LLMs. That doesnt mean users are going to abide by that recommendation, but we should start guiding people towards a solution. People are going to use LLMs, thats not going away.

Im trying to understand.

If I learned python from python’s own manuals, then wrote some proprietary python functions in a closed source proprietary project, but then also wrote that same function in an open source project, is it free and open source code, or is it proprietary?

Solar flares and even the occasional random neutron hitting your equipment can cause some weird issues. If its just a one time occurrence and it doesn’t happen again, I wouldn’t worry too much about it.

Firmware update utilities, host OS file system encryption packages, HBA management tools, temperature monitoring, and then a lot of the packages had bugs that were resolved with newer versions, but proxmox only provided old versions.

I thought about running something like proxmox, but everything is too pooled, too specialized, or proxmox doesn’t provide the packages I want to use.

Just went with arch as the host OS and firejail or lxc any processes i want contained.