grandma_tea

I'm not sure if I would consider tech demos like this state of the art. They look impressive and they do show promise but there's a huge gap between an impressive demo and a usable product. This goes for Google Gemini as well.

Have you ruled out the possibility that you have a bad stove?

My understanding is that battery degradation is not linear with time.

Oh wow, now I can finally have terminal that creates fire effects the faster I type! (if I ever get the time to make the plugin)

Is there anyway for plugins to interact with shaders?

I'm with you. I thought this was going to be an article about Numba.

Can you expand on that? Efficient in what way?

I mostly use tools like this for data exploration in Jupyter notebooks and PyVista was fine when I tried it out last year. However, I found I could get results much faster with Vedo[0].

No hate though, I'm so glad there are options in this space!

[0]https://vedo.embl.es/

Nice! I'm looking forward to trying it out. This seems very similar to https://github.com/cgarciae/pypeln/

Do you have any example prompts or suggestions for coming up with them?

Absolutely! I just picked up a 2023 for $17k. It's basically the perfect commuter car.

Your point aside, counting letters is not a good task for LLMs since they aren't trained on letters. They are trained on tokens which represent words or parts of words.

> Preferred by anyone who's actually using and modifying the work.

> ...fine tuning is preferred by everyone

How do you know this? Did you take a survey? When? What if preferences change or there is no consensus?

> The only people I've seen who've asserted otherwise are random commenters on the internet who don't really understand the tech.

There are lots of things that can be done with the training set that don't involve retraining the entire model from scratch. As a random example, I could perform a statistical analysis over a portion of the training set and find a series of vectors in token-space that could be used to steer the model. Something like this can be done without access to the training data, but does it work better? We don't know because it hasn't been tried yet.

But none of that really matters, because what we're discussing is the philosophy of open source. I think it's a really bad take to say that something is open source because it's in a "preferred" format.

Preferred by who? It sounds like these people have a strong say in what constitutes open source.

It's tough because proprietary software also has risks.

See the Unity license change fiasco. https://www.engadget.com/unity-apologizes-and-promises-to-ch...

I dunno, it seems pretty clear to me that's what they're trying to do.

I have a very different memory of my time on Facebook 10 or so years ago... It felt like every two weeks some update would change my settings to "public" in some way.

Language changes. Adapt or be cringe.

Oh, that's lame.

FUTO and Transcribro are open source.

That's a fair criticism of many open source projects, however this one does link to the Mamba paper at the bottom of the (short) readme.

Perhaps my question was worded poorly. I'm saying is: the premise of the article is wrong. It attributes the 29% figure to AI but the source attributes the 29% to new datacenters. It seems unlikely to me that 100% of new datacenter usage would go to AI.

I tried to find the source of the 29% figure, but it's defined as data center usage. I understand machine learning is resource intensive but how much is it really? Are the numbers for this available to the public?

Closed source and not relevant.

I wanted to like Logseq, but I couldn't stand how opinionated it is about the format of my notes. Also, at least on Ubuntu 22, there was a noticeable delay when interacting with any UI element.

> TORCS (The Open Racing Car Simulator)

The title should be changed to indicate that this is an acronym.

I think you mistake my meaning. I'm saying the HN audience is not a hive mind. Different articles attract different commenters which have different opinions.

It's not inconsistent. Different articles attract different commenters.

Certainly not a good one.

I didn't read the whole article because of the paywall, but I was taking it from the angle of "why can't we encourage this kind of development in America?" As opposed to "we should import Chinese cars."

I am frustrated by it because I feel like we could be doing better. I mean, the best thing would be to invest in public transport but I would also be happy with more cheap car offerings.

Which jobs have been killed?