Seems like he’s been pushed into using LLMs as a way to cope with the deluge of LLM-generated security reports.
I hate when AI people say “things are so different in just the past few weeks, what you know from last year is meaningless” without specifying what’s so groundbreaking that us regular folks wouldn’t be able to comprehend. It just seems like a way to shut people up and feel superior.
Also, nobody actually knows if human intelligence is just finer grained stochastic prediction as well.
An interesting but valid argument. It doesn’t make AI better than it is, but any human contribution and change can and often is also faulty. People have gaps of knowledge, sometimes unwarranted confidence, other times lack of care, or just miss things. It’s not like we’re comparing the perfect human vs faulty AI.
If you don’t mind the security risk then you can of course use an older release.
I haven’t read the original rage/drama but I can imagine if from other drama instances.
This post is certainly a good, founded response.
There’s some valid concerns in AI usage, but unwarranted or inappropriate harsh criticism when it’s an established trusted developer and engineer - if we assumed good practice before then we could assume continued good practice. Maybe LLM is one point of increasing skepticism, but criticism should be open, respectful, and fair.
They invested a lot of time and effort into a public good project. In that context, they deserve at least respectful and non-worst-assumptuous criticism.
I think “stochastic parrot” is a terrible way to describe LLMs. (Not to mention most people don’t use the term “stochastic” a lot.)
“Slot machine autocomplete” might be a better choice.
He makes some fair points. However I do think the large amount of regressions in 3.4.3 should have resulted in a new release rolling back those changes.
I still like the response of the libxml2 maintainer, where any vulnerability will be disclosed openly and fixed when it’s ready. Maybe more open source projects currently drowning in CVE should take that stance instead of their maintainers burning themselves out over it.
Also, nobody actually knows if human intelligence is just finer grained stochastic prediction as well.
I think some people are stochastic parrots and some are not. I think most of our true understanding of things comes from escaping our limitations. Why so many people want to become a stochastic parrot is beyond me though.
Now to the future, because we’re not done yet by a long shot. The security reports keep rolling in. I’m working on a bunch of CVEs right now. Luckily I’ve been joined by some other very good developers with great systems development skills and security knowledge. Some of these people came to my attention partly because of all the rage happening at the moment, so I get some rage storm clouds have silver linings. Watch out for some credits for some great new rsync developers in the next release.
The project is being taken over by vibe coders, yay.
In my perception¹, ML differs from a brain by operating on words in form of tokens, while the human brain works by associating a concrete piece of information or thing with another, with the path in between being formed at some points, but crucially, being editable more or less easily and flexibly by retraining. And that’s the points, humans learn on a fundamental level. Dropping the prod DB means that my brain will form a hard association between the action of writing ‘drop database’ and fear, which in turn triggers deeper thoughts about wth I’m doing. LLMs see “conflict at line 1, 12”, and for some reason one possible path of tokens to generate can be a drop command. And as the underlying model data does not change, they don’t learn.
On how living being’s speech centres work, idk.
¹The perception of an acidhead. So don’t trust me.
The differences between a human brain and any kind of model we can currently train are too great to be listed. They are incomparable. It turns out that no matter how many perceptrons you put together, you don’t get a brain.
Heck, we don’t even know how brains work, and you got people talking about how they’re making AI clones of themselves with LLMs lol.
I think he pretty much nails it. Makes a lot of the same points I get downvoted to hell for making here.
If you’re not using LLMs now you’re a dinosaur.
“But but LLM’s can’t think, they are just glorified autocomplete” /s
That was a fair response. But I get the feeling that a lot of “intelligence” is given in this tool. Feels like they are seeing something that I’m not.
I didn’t get that feeling at all. They didn’t make any such claims or used such wordings which I often see elsewhere.
Well I can always point to English isn’t my native tongue, so I can always infer stuff that isn’t there :D
Still, the way it explain give the idea of something that I can’t see it. And this is what is concerning me for the last week at least.
Trust. For me that fits your description, the thing I don’t “see” but some out there do. I try to keep an open mind, but the way this stuff is being sold hard bothers me.
Interesting. I’ve been waiting for some context to this. Btw Brodie Robertson made a Youtube video yesterday, scrolling through the issue tracker and untangling some of the drama. Here’s the link for people who like to consume their Linux news in video form: https://youtube.com/watch?v=FLCfRs6nKW8
There’s a bit of opinionated context here, in Danish. Get your LLM to translate it for you.
Thanks. Yeah, I’ve never looked into code quality of many tools I use on a regular basis. So far, rsync has served me well. I’ve been using it at work, at home, for larger amounts of data… Without major hiccups. And we kinda need something like this. It’s a bit of a shame how many essential software projects at the foundation of many things struggle being maintained. My distro has openrsync in the repository. Seems just that that software project is also a one-man-show.
(Btw, Firefox Translate for the win, I don’t really need a big LLM to translate stuff.)
