Win 11 IOT enterprise LTSC already exists and doesn't even require a TPM.
Unless this is a legitimate channel for regular users to get IOT enterprise LTSC, this is a non-story. The thing we want already exists, they just refuse to sell it.
Pop-in is pretty distracting for me too. That is generally the last thing I'll change for framerate. Hell, I'll take a resolution downgrade before I sacrifice framerate or pop-in
See this is the part I don't understand. I will turn literally every graphic setting down until I hit 120+ fps. Playing at anything less feels choppy and distracting, regardless of visual quality.
Their implementation doesn't seem like a completely shit way to do it:
Apps don’t get your birthday. Microsoft says an age signal doesn’t directly expose the user’s age or date of birth. Instead, GetUserAgeRangeAsync returns one of five ranges.
Windows signal Range
Under 10 0-9
10-12 10-12
13-15 13-15
16-17 16-17
18+ 18 and above
At best they could figure out a kid's birthday every couple of years, not an adult's
You can absolutely train a nontrivial task model on a gaming gpu (image classifier, sound classifier, text model with a very structured input and output, etc). You can also post-train addon layers on top of existing open-weight models (LORA).
This "feature" exists as a sort of government-backed guarantee to the lendor that the investment will eventually be paid back. It's a large part of why interest rates for student loans are able to be reasonably low. Say what you will about the system as a whole, but I don't think this particular narrow slice is the broken part. One only has to look at credit card interest to see the otherwise going rate for unsecured debt against the poor.
I would argue that Linux kernel scale basically doesn't work. It is a huge barrier to would-be developers to get into. You might argue that's a feature, but it's pretty hard to argue that it doesn't add a lot of friction to the process
You always keep training data because you never know what you need for the next generation. Ironically they might be preserved (privately though unfortunately) pretty well
I have no knowledge about the economics of live poultry, but rotisseries are generally loss leaders at grocery stores. They're so commonly loss leaders that they're often cited as one of the primary examples to explain the concept of a loss leader.
I'm not aware of any public frontier LLM provider that uses a static seed for inference. Meaning, even with an identical prompt and identical model you will not get the same output. Seeds should absolutely come back with the streaming metadata on requests imho, but they don't in any api/harness I'm aware of.
I am rich was the og. It was one of the first ios app store apps
Win 11 IOT enterprise LTSC already exists and doesn't even require a TPM.
Unless this is a legitimate channel for regular users to get IOT enterprise LTSC, this is a non-story. The thing we want already exists, they just refuse to sell it.
Pop-in is pretty distracting for me too. That is generally the last thing I'll change for framerate. Hell, I'll take a resolution downgrade before I sacrifice framerate or pop-in
Weirdly: no
Right now I'm playing through kcd:2 and I've lowered the settings a bit to hit a smooth 120hz
See this is the part I don't understand. I will turn literally every graphic setting down until I hit 120+ fps. Playing at anything less feels choppy and distracting, regardless of visual quality.
Am I really that far in the minority here?
Their implementation doesn't seem like a completely shit way to do it:
Apps don’t get your birthday. Microsoft says an age signal doesn’t directly expose the user’s age or date of birth. Instead, GetUserAgeRangeAsync returns one of five ranges.
Windows signal Range
At best they could figure out a kid's birthday every couple of years, not an adult's
I don't understand what you're proposing that nagios/chrck_mk doesn't already do
Copyparty is what I've been using. Seems very similar in philosophy.
+1 though for anti-discouragement. Competition breeds competence!
Only if you pay for premium. Regular users it refuses to play in the background.
You can absolutely train a nontrivial task model on a gaming gpu (image classifier, sound classifier, text model with a very structured input and output, etc). You can also post-train addon layers on top of existing open-weight models (LORA).
You'd be surprised how far it goes when you aren't doing training. I'm serving an entire university on 1/2 of a 6000B
This is the only drama in your list I'm not familiar with already. What/when did they last break?
This "feature" exists as a sort of government-backed guarantee to the lendor that the investment will eventually be paid back. It's a large part of why interest rates for student loans are able to be reasonably low. Say what you will about the system as a whole, but I don't think this particular narrow slice is the broken part. One only has to look at credit card interest to see the otherwise going rate for unsecured debt against the poor.
I would argue that Linux kernel scale basically doesn't work. It is a huge barrier to would-be developers to get into. You might argue that's a feature, but it's pretty hard to argue that it doesn't add a lot of friction to the process
I'm glad I'm not the only one who is confused
Genuinely, pull requests being off platform without good integrated diff and merge conflict resolution tooling just doesn't work at a certain scale.
You always keep training data because you never know what you need for the next generation. Ironically they might be preserved (privately though unfortunately) pretty well
I have no knowledge about the economics of live poultry, but rotisseries are generally loss leaders at grocery stores. They're so commonly loss leaders that they're often cited as one of the primary examples to explain the concept of a loss leader.
https://thehustle.co/the-economics-of-costco-rotisserie-chicken
https://en.wikipedia.org/wiki/Loss_leader
Is this hopium or did they release a statement suggesting an 3.8 35b a3b was in development for release?
I'm not aware of any public frontier LLM provider that uses a static seed for inference. Meaning, even with an identical prompt and identical model you will not get the same output. Seeds should absolutely come back with the streaming metadata on requests imho, but they don't in any api/harness I'm aware of.