No worries! You’re probably right that it’s better not to assume, and it’s good of you to provide some different options.

If by more learning you mean learning

ollama run deepseek-r1:7b

Then yeah, it’s a pretty steep curve!

If you’re a developer then you can also search “$MyFavDevEnv use local ai ollama” to find guides on setting up. I’m using Continue extension for VS Codium (or Code) but there’s easy to use modules for Vim and Emacs and probably everything else as well.

The main problem is leveling your expectations. The full Deepseek is a 671b (that’s billions of parameters) and the model weights (the thing you download when you pull an AI) are 404GB in size. You need so much RAM available to run one of those.

They make distilled models though, which are much smaller but still useful. The 14b is 9GB and runs fine with only 16GB of ram. They obviously aren’t as impressive as the cloud hosted big versions though.

I’m an USAian and that was my first thought too! “Put some branston on that and it’d be an alright lunch.” Seriously though, at least put some mayo on there or something, my mouth is dry just looking at it.

These are fine examples, but all over 20 years old now! (Also I’m still salty about Origin Systems in 1992!)

But hey, EA has never stopped sucking! We can also lament recent purchases like legendary UK studio Codemasters (2021) or Respawn (2017). Respawn is especially funny since it was made by ex-Infinity Ward founders, who got bought again! I wonder what their next future-EA-acquisition project will be?

I’ve been hoping to save this one next time I saw it. So… thanks!

I’m really wondering what it means myself! Is it like superstitious programming? Or “cookie cutter” perhaps, using templates and copy-paste from QA sites to duct tape it all together?

I can’t browse lemmy or play phone games while I read docs, but I can while I wait for the program to compile, load itself into a docker container, deploy to the test server, load my browser, and then fail to have fixed that bug I was looking at. Oh well, let me change one character and try again, it only takes about 15 minutes per attempt.

I’m noticing a trend of scientific-sounding announcements about physics results that turn out to be theoretical explorations of simulations. The whole “false vacuum” idea isn’t really even a hypothesis, just a what-if. We have no indication that the ground vacuum state isn’t the lowest energy configuration. I think people just find a non-zero minimum unintuitive.

Anyway, the key figure in all these theoretical simulation articles is the multi-billion dollar quantum super computers running these simulations. Wouldn’t it be funny if tech investors with a lot of money staked on quantum devices pushed for low-quality science that required their machines to be done, thus expanding the market and value of their otherwise pointless supercomputers? This article ends on a very optimistic “these computers have so many uses in cryptography and science” which seems a little out of place when discussing physics results.

Nothing is more homosexual than being a loving, present father for the children you’ve created with a woman.

!egg_irl@lemmy.blahaj.zone will ultimately be looking for this.

In a rich man’s house, there’s no where to spit except for his face.

LOOK WHAT THEY NEED TO MIMIC A FRACTION OF OUR APOLOGIES

Seems like the cross post isn’t displaying quoted content (for me on Voyager mobile anyway) so I just wanted to add that in the original post, there is a long discussion I wrote highlighting some interesting aspects of this output. Please click through if you’d like to know more!

It’s already happening. This article takes a long look at many of the rising threats to nvidia. Some highlights:

• Google has been running on their own homemade TPUs (tensor processing units) for years, and say they on the 6th generation of those.

• Some AI researchers are building an entirely AMD based stack from scratch, essentially writing their own drivers and utilities to make it happen.

• Cerebras.ai is creating their own AI chips using a unique whole-die system. They make an AI chip the size of entire silicon wafer (30cm square) with 900,000 micro-cores.

So yeah, it’s not just “China AI bad” but that the entire market is catching up and innovating around nvidia’s monopoly.

I do All - Scaled. I’ll switch to New if I feel like I’m seeing a lot of repeat content. I’ve had to block a few bots (the reddit reposters are particularly egregious) and communities I don’t really care for.