And Julia!

Lemmy generally attracts the same kind of person that would also use Linux. Both of them are open source and community driven alternatives to software otherwise provided by large corporations and milked for every last cent. Both of them require just a bit more knowledge in order to comfortably use them. Linux with all the distro’s and desktop environments, Lemmy with all the instances and apps/front-ends. We’re very much a bubble here.

The “Forgetting How Your Own Code Works” is real. I’ve looked at code, thought to myself “what fcking idiot wrote this garbage”, only to see my own name next to it. It’s a very humbling experience

I was just about to post the same thing. I’ve been using Linux for almost 10 years. I never really understood the folder layout anyway into this detail. My reasoning always was that /lib was more system-wide and /usr/lib was for stuff installed for me only. That never made sense though, since there is only one /usr and not one for every user. But I never really thought further, I just let it be.

I try to steer as many people as I know to Signal, but I don’t want to be the type of person who accepts no compromise so I also use a bunch of others. Whatsapp is the most common, as pretty much everyone here in the Netherlands uses it. I used to use Telegram, but nowadays I trust it less than Whatsapp and all my Telegram chats have moved to Signal. SMS is only there for backup and older people who don’t use other apps. And Discord is there for people who want their messages to never be read, because that app is a dumpster fire that constantly makes me miss messages.

Maybe the phrase “white ones are better” triggered some moderation bot?

Sometimes I look at the memes around here and wonder wtf y’all are doing. Like, neither my code nor the code at the place I work at are perfect. But I don’t think I’ve ever seen a merge do this. Maybe some of the most diverged merges temporarily had a lot of errors because of some refactoring, but then it was just a few find + replaces away from being fixed again. But those were merges where multiple teams had been working on both the original and the fork for years and even then it was usually pretty okay.

No, people just don’t like crypto because it’s a huge waste of energy that has no use for the average person at the moment and is only used by rich people to get richer without much regulation. Don’t get me wrong, it might definitely be useful when used correctly in the future. Not wasting as much energy by ditching proof of work, becoming actually useful for normal transactions, etc. But right now it’s just an overhyped technology for obnoxious cryptobros.

From the train dataset that was frozen many years ago. It’s like you know something instead of looking it up. It doesn’t provide sources, it just makes shit up based on what was in the (old) dataset. That’s totally different than looking up the information based on what you know and then using the new information to create an informed answer backed up by sources

No. ChatGPT pulls information out of its ass and how I read it SearchGPT actually links to sources (while also summarizing it and pulling information out of it’s ass, presumably). ChatGPT “knows” things and SearchGPT should actually look stuff up and present it to you.

Oh I never knew, but it seems true. On his Wikipedia page both researches are mentioned. It’s so impressive how these researchers are behind so many different but interesting papers.

Machine learning and compression have always been closely tied together. It’s trying to learn the “rules” that describe the data rather than memorizing all the data.

I remember implementing a paper older than me in our “Information Theory” course at university that treated the creation of a decision tree as compression. Their algorithm considered sending the decisions tree and all the exceptions to the decision tree and the tree itself. If a node in the tree increased the overall message size, it would simply be pruned. This way they ensured that you wouldn’t make conclusions while having very little data and would only add the big patterns in the data.

Fundamentally it is just compression, it’s just a way better method of compression than all the models that we had before.

EDIT: The paper I’m talking about is “Inferring decision trees using the minimum description length principle” - L. Ross Quinlan & Ronald L. Rivest

I’m on Arch (actually a converted Antergos) and I have an NVIDIA card as well. My first attempt a few months ago was horrible, bricking my system and requiring a bootable USB an a whole evening to get Linux working again.

My second attempt was recently, and went a lot better. X11 no longer seems to work, so I’m kinda stuck with it, but it feels snappy as long as my second monitor is disconnected. I’ve yet to try some gaming. My main monitor is a VRR 144Hz panel with garbage-tier HDR. The HDR worked out of the box on KDE Plasma, with the same shitty quality as on Windows, so I immediately turned it off again. When my second monitor is connected I get terrible hitching. Every second or so the screen just freezes for hundreds of milliseconds. Something about it (1280x1024, 75Hz, DVI) must not make Wayland happy. No settings seem to change anything, only physically disconnecting the monitor seems to work.

My 10 year old laptop (which has been running Linux for 9.5 years now) has an SSD, so it’ll restart in a normal amount of time. Even old laptops no longer have HDDs only

I might misunderstand what you mean with “implementing” an LLM, but unless you have a good understanding of deep learning and math I wouldn’t recommend to implement one from scratch. There’s a lot of complex math involved in these kind of topics. If you mean implementing an application around an existing LLM, for example writing a chat website that interfaces with ChatGPT or a local LLM, then it’s doable (depending on you current skills).

I bought a ThinkPad new in 2014 for my study for like 1200 euro’s. She’s still happily purring today. Around 2019 I made the mistake of emptying a cup of tea into the ThinkPad accidentally and then holding it upside down to get the water out. I think I should’ve just let it leak out of the bottom since the laptop has holes for that, but I panicked. This broke the keyboard, but not the rest of the laptop. I got an official new keyboard for like 100 euro’s which came with a tool and the simple instructions, and since then everything has been working flawlessly.

So I recommend ThinkPads, although I can’t really say anything about compatibility of new models

Sounds cool. The types like Haskell’s data types or Rust’s enums compared with proper pattern matching are pretty much a requirement for a good language imo. And the process/message passing is interesting.

I wouldn’t be so sure if I were you. Everyone, and I mean everyone, uses WhatsApp here. Friends, family, work, doctors, landlords, etc. Not using WhatsApp will make you miss get togethers with friends, make it way harder to communicate with colleagues, take away a lot of convenience when talking to your doctor or landlord or something.

I have Signal groups with friends, but you’re never going to be able to fully lose WhatsApp here unless you’re prepared to be “that person” everywhere and miss a lot of convenience.

True. Guess I misread. I’ve already switched to 36 hours and I totally aim to go lower once I earn more. Work to live, not live to work

Disagree. I used to spend time on more energy-intensive hobbies like programming and music production. But I’ve had mono and COVID in 2020 and I’ve never felt the same thereafter. Also working 40 hours a week drains a lot of energy