It's not the name registrar it's the dns server.

You just use a wildcard dns record, and then the webserver uses the host header to determine which subdomain was used (for webservers at least).

To my knowledge planes don't use the gps for very much, and AFAIK they use the barometric altimeter for maintaining their flight level. A controllable, plane that can maintain altitude is difficult to crash. The GPS stuff is mostly supplemental and is relatively new.

And pilots can land with a visual approach and still have ILS.

You would still have ADSB with your altitude, and I beleive heading and airspeed.

Because of the existing requirement to be spotting traffic and being over 1000ft away from other planes, they should be fine.

ATC should still have radar vectoring for their airspace.

I assume planes use more then just GPS, and use galileo and the other networks like consumer GPS do.

GPS is used a lot to figure where planes are but I think they are still not allowed to rely in it as it's not considered reliable enough by the regulators. At least not for things like altitude settings and instrument landings.

For light aircraft getting lost in the old days one hack used to be to fly low till you came to a motorway and read the signs. I imagine most such pilots these days would pull out their phone and open the maps app. Commercial flights of course have more high tech stuff like radio beacons.

My understanding is that would be covered under medical insurance?

Yes, acute care for eye injuries (or potential injuries like to verify the eye is fine after it was hit) is covered under health insurance (in the US) and not typically covered by vision insurance. Vision insurance mostly covers your routine exams, glasses, and contacts. If you have a real health issue (glaucoma, macular degeneration, etc.) with your eyes and not a vision issue (myopia, presbyopia) then that's also covered under your health insurance and not your vision insurance.

From my understanding many of the best players immediately look down to tell what "generation streetview car" they're using, and seem to know what continents/times they're from.

From my understanding they're basically an extension of FIDO?

You can just use a yubikey unless I'm mistaken?

Something like that, they're Webauthn alternative outside the web and allows multi-device syncing (Webauthn doesn't support syncing)

You could use any FIDO2 software or hardware key with Passkey.

> You can just use a yubikey unless I'm mistaken?

Yes, USB passkeys work!

This is simplified a bit - It's just a "machine" that maps [set of inputs] -> [set of probabilities of the next output]

First you define a list of tokens - lets say 24 letters because that's easier.

They are a machine that takes an input sequence of tokens, does a deterministic series of matrix operations, and outputs what is a list of the probability of every token.

"learning" is just the process of setting some of the numbers inside of a matrix(s) used for some of the operations.

Notice that there's only a single "if" statement in their final code, and it's for evaluating the result's accuracy. All of the "logic" is from the result of these matrix operations.

I've always wondered what percentage of "users" are "lurks" who never login, occasionally check a profile or click on a tweet on another website.

One of the previous uses of twitter was "announcements". Government, corporate, emergency, community, software, etc.

The previous move to require logging in was already a massive blow to that, requiring a payment would decimate it was a use-case.

I don't see company's/governments putting announcements on a paid platform. You can't expect people to pay for twitter for announcements.

> Because there's no way to control the seed, a direct comparison (using a before/after slider, for example) probably wouldn't make sense.

Even if it was the same seed, from my understanding Dalle3 would have to be just a further trained version of the same checkpoint to even resemble Dalle2's image. Like stable diffusion 1.4 vs 1.5 and 2.0 and 2.1 will make identifiably similar images, but 1.5 vs 2.1 vs SDXL won't look remotely similar.

Even more so because I'd wager they changed their encoder and/or decoder too.

* I think that if they generated something like a controlnet for guidance the same way in both models then they might be comparable but from my understanding Dalle2 doesn't work that way at all.

Comparisons would still be interesting though!

I think you're right — I thought about it a little more after I replied.

I guess it'll just have to be comparisons of the general concepts. It'll be good to see the change in understanding of the prompt and the change in image detail.

If anyone at OpenAI wants to give me early access to give me a head-start… smiles

Yeah I'll be interested to see how much you have to change the prompts to get similar styles

I'm not sure what you mean - the last security update fixed massive 0-day which was an arbitrary code execution caused by the image decoder. CVE-2023-41064

AFAIK every app that uses the ImageI/O api is effected by it, which includes every app you mentioned. You often don't need to even open the message for the image to be decoded.

From my understanding most vulnerabilities are from either the image decoder, text decoder, or webkit which again, effects nearly all apps. All apps can only use the webkit view, which affects nearly all of them to some degree.

I think you might be confusing the attack vector - messages is the easiest to attack since you just sent a regular text. Even if you don't normally use messages, it'll parse the image and you'll be hit by the 0 day. In theory this will work with most messaging apps.

I was referring to the opposite side of things— not the security patches but the actual new features of iOS 17. Rather than being OS-level capabilities that feel like they would impact the whole ecosystem, they're more like app features, and a lot of the app features don't apply to me as I use alternatives to those specific apps.

I've been struggling with figuring out a good dataset for fine-tuning. Most of the ones that exist were purpose made for finetuning/training a model already.

Does anyone have any tips for creating sufficient datasets for finetuning specific workloads?