Where they forgot to remove there are co-author tags.

There are plenty of people who are interested in using tools that are available and also recognize that it is underhanded to hide that fact for people who feel strongly about it.

Doesn’t matter, that human is still the responsible party putting it there.

The whole premise here is weird anyway; unless you’re using a RAG designed to quote code, the chances that you end up in a perfectly identical environment needing exactly the same operation, AND it’s proprietary somehow, AND you’re the one working on it and wouldn’t know there was a proprietary feature (whatever that even means here) is astronomically low.

If you were writing a book with a handful of other people and you used an LLM which PERFECTLY reproduced a chapter of another book on the topic without attribution, whose fault would that be? It’s not like there are software 2 liners that are just off limits for use, so besides the fact that it would be crazy unlikely to reproduce a perfect copy of a huge block of code, the chances of you working in the Linux kernel for example, getting your commit added to the official version, and not knowing it’s proprietary by looking at it is, IMO, so unlikely it’s not an argument against culpability.

Maybe if you’re vibe coding an app from scratch on your personal GitHub or something (even then the odds of perfect recapture are stupid low), but it’s not really possible or a good argument here.

Edit: I guess the obvious exception here is if some LLM provider DID train a model to use whole pieces of code and strip attributions and licensing. In that case, I don’t see how they survive getting sued into the ground.

It’s the same as when you use any generative tool: if your name is on it, it’s your problem, tools aren’t accountable.

How do you verify that no human has given a proprietary snippet? 🤔

Idk probably about $250

I think we have different definitions of “easily”.

Who is having breaking update issues anymore in 2026? I’ve been running vanilla Arch for 10 years and the only times that has happened (there have been a handful I guess) the archwiki says “hey there’s a breaking change run these 2 commands” and it’s fixed. As a beginner on Linux I actually switched to Arch because every Ubuntu issue I googled was 6 to 10 lines to fix while arch was 1 to 3 lines. The only problem is that the OS expects that you be able to read, which is sometimes tough.

I can’t imagine being on a system that is multiple major releases behind on basic things like nvim and python. I guess if you’re content not to use anything remotely current it makes sense.

Ah yes, review the PKGBUILD for every AUR update. Luckily I do this while I’m rereading the ToS every time those get changed for all my software as well.

When I finish that I intend to read the changelog in git for each of the commits since the last update.

A man of culture

This is just a regular OS. Gui is bloat because DE is bloat.

Window manager is all you need, the terminal is the file explorer. Free your mind!

Unless that’s how people are designing front ends for models, it literally DOESN’T work like that. It works like that until you finish training an embedding model with masking related tasks, but that’s the tip of the iceberg. The input vector, after being tokenized, is ingested wholesale. Now there’s sometimes funny business to manage the size of a context window effectively but this isn’t that unless you’re home-rolling and you’re caching your own inputs or something before you give it to the model.

The joke is that stopping discussion is healthy (which was obviously wrong). So I said I was stopping you–and thus the discussion–and then showed it was healthy with a salad.

I get it. You can’t get by “Ai iS slOp” at top level comments anymore. I get that kind of ending because I would add it… but then I also don’t mind collecting downvotes so ymmv I guess.

I feel like there needs to be a dedicated post (and I don’t want to write it, but maybe I eventually will) that outlines what a model really is. It is not just a statistical text prediction machine unless you are being so loose with the definition of “statistical” that it doesn’t even mean anything anymore.

A decent example of a statistical text prediction machine is the middle word suggested by your phone when you’re using the keyboard. An LLM is not that.

In the most general terms, this kind of language model tokenizes a corpus of text based on a vocabulary (which is probably more than just the words in the dictionary), uses an embedding model to translate these tokens into a vector of semantic “meaning” which minimized loss in a bidirectional encoding (probably), that is then trained against a rubric for one or more topic area questions, retrained for instruction and explainability, retrained with reinforcement learning and human feedback to provide guardrails, and retrained again to make use of supplemental materials not part of the original training corpus (resource augmented generation), then distilled, then probably scaled and fine tuned against topic areas of choice (like coding or Korean or whatever) and maybe THEN made available to people to use. There are generally more parts to curriculum learning even than that but it’s a representative-ish start.

My point being that, yes, it would be nuts to pose ANY question to a predictor that says “with 84% probability, the word that is most likely follows ‘I really like’ is ‘gooning’ on reddit”, but even Grok is wildly more sophisticated than that and Grok is terrible.

Edit: And also I really like your take at the start of this thread: user error is a pretty huge problem in this space.

A little personal flourish doesn’t invalidate the rest IMO. Humans get aggravated and humans are aggravating.

20 line commit: 5 issues and suggestions.

500 line commit: “looks good to me!”

I’m just gonna stop you right there.

🥗

It sounds like you are already doing it! Fixing annoying stuff as it comes up feels painful and tedious, but that’s the good instruction you’ll remember. Hopefully you aren’t hitting too many work stoppage type issues. Definitely reach out for help for those, but be prepared for uptight people to tell you “RtFm noOB”. You’ll get it done.

My only advice is to give a little bit of time to learning at least basic bash commands (search and print a cheat sheet) and basic Vi / Vim. Vim is what got me to Linux in the first place to be honest. Those 2 things will give you the latitude to move around and work on any issue you might come across as any user even if you “break” something.

Good luck! You’re already doing the hard stuff.

Happy cake day. I was asking, before I dive into the hunt, if you saw any docs for doing the same sort of firmware update / swap for plain old irobot vacuums.

Idk how motivated to be to go do this. Just started reading about valetudo.

COA => course of action

Is this a COA for irobot Roombas?