Somehow there’s never time to do it right, but there’s always time to do it again

I did not hit her! Oh hi, Mark, you piece of shit

My favorite variant: Your secret is safe with my indifference.

Fun fact: The topic of the community is memes, which means discussions of memes are an appropriate post for the community.

Additionally, posting discussion had elsewhere is a valid way to generate further discussion on something.

Ergo, yo u are complaining about a non-issue.

Because the point of the post is the comments you donkey

DSP recently got localized small distribution drones, you can convert any storage box into a tiny logistics station now. It’s pretty sweet, really reduces the spaghetti early on in recent playthroughs

I switch between this and Shutter on a regular basis depending on what I feel like using that day.

I dunno what you were using but I recommend virt-viewer.

The main thing for this one is that you’ll want to get a PCIe USB controller card and pass that through directly to the VM so that unplugs/replugs/device resets don’t connect the device to the host machine briefly while if determines if it should pass through.

In i3wm you can set a key bind to float a window above the tiling and it’ll do just that; You can even automate it with some custom for_window rules if desired.

I’m sorry, my goal wasn’t to be a bother. My initial comment was intended to be friendly and funny - I’m not trying to patronize or be antagonistic. I learned a couple of years ago that I have autism, so I should have learned my lesson by now and stopped trying to be funny; It never pans out the way I mean for it to.

Hope I wasn’t too much of a drag on your day, and I hope it gets better for you.

With that said, a genuine question with no jokes: Can you help me understand how 2016 counts as recent, given the context? It was almost a decade ago, and I’m having trouble comprehending how it counts at all as recent since in tech “recent” usually means “in the last 2-3 years” unless you’re comparing to something from a much longer time ago like the 90s.

It was a lighthearted jab at calling 8 years ago recent; Not a political statement about Apple or operating systems.

8 years is a ton of time in tech, CPUs from 2016 are ancient. Single-core CPU performance has doubled in Intel’s laptop chips since then, and modern laptop CPUs from Intel are often 12-core, versus the top end 2016 MacBook Pro having 4 cores.

Not trying to start any fights, was just poking fun at the choice to call 2016 recent

I can read, and a 2016 MacBook pro is not even a bit recent; It’s from 8 years ago :-)

Just a bit of light-hearted leg pulling, nothing to get worked up over

I hate to break it to you friendo, but 8 year old hardware isn’t recent. It may still be usable, but that doesn’t make it recent. It’s ok though grandpa, let’s get you back to bed

The dual GPU problem has actually for the most part also been solved; Optimus rarely poses a problem these days

I’m gonna stop responding to this asanine thread now before you continue to demean us both with your nonsense.

Simpler language is fine when it’s accurate.

Your simplification is inaccurate and could mislead people into thinking GPTs are just advanced regex matching engines.

They are not. They are closer to autocorrect on steroids.

Analysis. It uses it, but not by “matching it”. The training data is not included in the final model. No GPT can access its training data at runtime.

Training analyzes the contents of the training data and creates a statistical model representing the likelihoods of various tokens based on a complex series of mathematical transformations that encode various attributes of the tokens making up the training data.

3Blue1Brown has a great series on the actual math behind it, I would highly recommend educating yourself on what GPTs actually do. It’s way more interesting than simple matching.

You said it matches text to its training data, which it does not do.

Your single-phrase statement only works for very short, non-repetitive phrases. As soon as your phrase repeats a token more than a few times, the statistics for the tokens change and could result in nonsensical output that repeats through subsections of the training data.

And even then for that single non-repetitive phrases, the reason you would get that single phrase back is not because it would be “matching on” the phrase. It is because the token weights would effectively encode that the statistical likelihood of the “next token” in the generated output is 100% for a given token when the evaluated token precedes it in the training phrase. Or in other words: Your training data being a single phrase maniplates the statistics so that the most likely output is that single phrase.

However, that is a far cry from simple “matching” against the training data. Which is what you said it does.

They do not store anything verbatim; They instead store the directions in which various words and related concepts relate to one another in some gigantic multidimensional space.

I highly suggest you go learn what they actually do before you continue talking out of your ass about them

That’s not how GPTs work