filterfiber

> still run on a device like the iPhone, which weights about 170g [1]? The human brain is 8 times heavier.

Why are you comparing the weight? I'm sorry but this is a bizarre comparison. This isn't even apples to oranges, this is apples to a telephone pole.

I'll also throw in that a single Nvidia H100 is 1200 grams. Unless you have a "Bracket with screws" which will add 20 grams (who wouldn't want an extra 20grams of intelligence?).

Like 70%+ of the human brain is water. The human brain needs a massive network of systems to transport nutrients/oxygen which is irrelevant to logical processing.

Similarly the majority of the iphone weight is the battery and frame. The weight of the processing chip is _grams_.

Besides the conflicting variables with the weight, the way that ML works on a physical level is completely different from the human brain.

In their second sentence they have the most honest response I've seen so far at least: " averaged across 4 diverse customer tasks, fine-tunes based on our new model are _slightly_ stronger than GPT-4, as measured by GPT-4 itself."

Does anyone know if mmWave could differentiate between my cat and I?

Does anyone know where I should look if I want to detect specific sounds? Like a smoke alarm, food bowl dispenser (its very distinct), cat meowing, 3d printer collision, that sort of thing?

Fun monetization strategy for federated apps - federate with your own instance dedicated to ads.

But more seriously what is the monetization strategy for federated apps? Up front pay or subscription for using the app?

Does anyone have any input on how this compares outside of benchmarks?

They don't appear to have any info on how they made it.

> Realistically, even with Turbo+LCM, you're still going to 4+ steps (often 8+), with CFG, for reasonable one-generation quality anywhere close to the images people generated at 50+ steps without Turbo/LCM.

For sure the only reason I considered comparing it that way was because the linked repo appears to also be going for a similar approach with 1 step/image on the pi.

From my own experience I've had a hard ever getting a decent image below 6~8steps, but this repo seems more focused on getting it to run in a reasonable amount of time at all, which understandably requires the minimal "maybe passable" settings.

> since you have stocks of the billion dollar message

I have no idea what you mean by this?

Are you saying I'm defending apple somehow?

Because my point is nearly every phone/laptop could pull off this attack, not just a single "special hacking device". Which I think is worse for them.

I don't know why the title even mentions the flipper.

The attack can be done from any device that can send crafted BLE packets including laptops/android phones, etc.

Apple just fixed a BLE DOS attack.

I mean, if you need a human in the loop to verify the image quality then you HAVE to pre-compute the images.

> 100 images/s is likey too much volume

You can always generate less

I was just using that as a reference. Stable diffusion will run well with almost any relatively modern gpu.

You don't have to use a 4090, you'll still get double digit performance with a 3060 or whatnot.

> for people who can only otherwise afford Raspberry Pis ;)

You can rent a 4090 for 0.7USD/1hr, or get an A100 for 1.1USD/hr. And if your project is a display + raspberry pi then those costs will dwarf the rental cost.

> But that's not the point, obviously.

If you want to say the zero2-w is what's making it then sure.

> Besides, a 4090 costs more than a car.

They only cost ~0.70USD for 1 hr. In fact you could put this on an A100 for 1$/hr. Renting would make the most sense for this type of thing.