Can we …like… add a bit more context to posts here? Is it entirely vibe coded or does it work? What’s with Pewdiepie? Did anyone try it?

Lmao. Thanks. Seems this is the issue report then, to stay informed about the outcome: https://github.com/RsyncProject/rsync/issues/915

There’s maybe still hope. If this really is a botched AI security fix, it’d be a great opportunity to fail early and learn something. Like to re-think the AI approach 😆

Difficult to tell. I don’t see anything too obvious or offensive in the commits. They also write like a human in the associated pull requests. Not sure what Claude’s role is here. Also the added code comments are kinda on point, use contractions… Not really what I’d expect from an AI.

Is there more info on this? A blog post or some statement by the project? At first glance this doesn’t look to me like other vibe-coded projects.

Uh. I guess people have random opinions and blast them on the internet. I can see how someone would misconfigure their computer and then blame it on the software. Or use software they don’t need, which just adds unnecessary complexity and more issues. Other than that, I don’t think there’s anything wrong with SELinux.

I have internet at home, an electricity bill. A few domains and a VPS.

Uh yeah. That is more information… Sorry, I’m not that familiar with Snaps. It looks to my untrained eye a bit like the report on the Snap itself, maybe it advertises to support running in strict confinement. Which it could… but doesn’t do. (Alike the other channels, which you could install, but didn’t… It’s kind of buried with that kind of information.)

It’s confusing at least. And the user definitely wouldn’t expect it from that wording. So I’d view it as a separate bug as well. And dropping confinement without notice would be the third thing, I’d consider a bug.)

Uh. Blog is down. All I get is an 404 for the link in the Mastodon post.

Edit: Here’s a link that works: https://github.com/eleboucher/towonel

No need. It’s already reported. And known since Dec 2019. 👀

#947325

Yeah. And I’d say with the SELinux problems and with what OP wrote, the security model including things like a failure mode to fall open, …silently… There’s more things to be wary of, than what they wrote in those 4 sentences.

It may not be wise to use a Snap without first understanding the reputation/limitations of Snap.

seems the Debian Wiki has pretty much your take on it 😅

If I had to guess, this isn’t a bigger issue because Snap is mostly pushed by Canonical. And in a bit of a weird way (proprietary backend, exclusive apps) so… reception in the rest of the Linux community is …mixed. To put it charitably. It’s probably not that relevant for most people outside of the Ubuntu ecosystem. And probably also not a priority for Canonical or the proprietary software vendors.

Ah, thank you very much for explaining! I missed that. Makes perfect sense.

Sure, I read a few examples of the actual questions in the Github repo as well. I just don’t understand how/why models refuse the legitimate anchor, and the significance of that. Is their metodology flawed or did I misunderstand something? Does the dataset with the requests contain a third “wrong” questions? Or do some models just like to not fulfill user requests at all? IMO there should be an almost 100% acceptance rate with L1 and it should go progressively down from that. Ideally towards mostly refusal past L3. But that’s not their result?!

Interesting. Why is L1 somewhere around 65%? Isn’t that the control? (They call it “Anchor”.) Like develop an internal team chat, or a bluetooth exposure tracking API in an ethical way… And already a 35% baseline of requests that get flat out refused anyway, no matter if they’re legitimate?

Also kind of question the choice of wording with the “escalation”. There’s no escalation in the traditional meaning of the word in there. The requests get progressively more morally wrong. But it’s not like there’s put on more pressure to fulfill them.
Which would be another interesting question. Is using pressure, urgency or using certain manipulation strategies more effective than others? I bet that’s the case, since I followed some of the earlier “jailbreaking” attempts.

Online classified ads, your neighbour/relative/friends attic, the access road towards the recycling center/landfill, a refurbished PC shop… From my experience the world is filled with old hardware that’s plenty good enough, just doesn’t run Windows 1X anymore so it becomes e-waste. You can of course also buy a Mini-PC on Amazon or use your existing computer if it has enough RAM to host a virtual machine.

I don’t think that’s any new insight 😂. That’s how the AI game works. There’s always been two classes: Big corpo. And the GPU poor. Of course the big AI companies get to shape AI. Economy of scale also works in their favour. They’ve bought most of the skill. And they have all the money. They simply buy a 4x EPYC +3TB RAM connected to 16 Nvidia AI cards. And then a few hundred nodes more. You don’t even buy one. It’s just a very unequal environment if you want to compare the two.

Nice write-up. Thanks for also including all the numbers. If I might ask: What is the thermal/throttling behaviour you mention? Is it still within the laptop’s thermal budget? Or does it reach throttling territory when doing inference on a long context window?

… don’t forget about the backups.

And if your major issue is putting things in wrong locations… Maybe learn about some abstraction layers, so next time you’re able to just move it, instead of tearing it down?

Sure. I should have phrased it a bit differently. My point was more or less, why is the curl developer’s review of the performance in a hypothetical scenario a decisive factor here… That feels like super random information. Same with the other two people. I’m fairly sure this is true and all… There’s just no context given, nor is there a connection being made between the statements and the rest of the content of the article.

I usually start with the Wikipedia Article when I’m interested in new things. It’ll have many references at the bottom to read more about a concept.

Interestingly enough, there’s zero mention of Claude in there. And when I google it, there’s many very convoluted blog posts. And I can’t tell whether it’s above my head or hallucinated stories. They go on for like 20 pages but don’t really explain anything with all those words. Or what they actually found in Claude’s code.

Symbolic-AI in itself isn’t too hard. That’s stuff from the 1980s and in every computer science textbook. Just no clue how something like an expert system is supposed to be connected to a Chatbot or programming agent.