Considering the relatively low crime rate among naturalized citizens, it’s entirely possibly this is the majority of people at risk of this mechanism being used against them.
It’s also another turn of the fascism ratchet.
Most people read that the government is revoking the citizenship of child predator immigrants and won’t bat an eye. Once it’s established, they’ll move to try it with less heinous crimes. Then they’ll skip the crime all together.
No it does work on drywall.
Water will spread until it reaches equilibrium. So if drywall is tested and shows 20% then the wood directly attached to it is likely at 20% moisture content as well.
Kind of a genius little device, honestly.
I think my balls are bigger than yours, cause that don’t work unless I do quite a bit of moving things about.
Spent a few minutes researching because I was almost sure it was just measuring resistance but that is different depending on materials.
They measure moisture by using a scale of wood equivalent moisture. Basically no matter what you stick it in, the pins measure resistance across the probes and give a number as an equivalent reading for sticking them in wood that is wet. Apparently wood is pretty consistent about resistance regardless ofof what type of wood it is. And since American houses are primarily made of wood, we only care about how wet the wood in a home could be.
I would think that pulling hardware ID values would be an administration level task, requiring root or kernel level access. So yes they are related or at least should be.
Per Mariam-Webster:
Mince: to cut or chop into very small pieces
And it is basic food safety knowledge that most bacterial growth on red meats is on the outside due to the muscle density compared to poultry. That’s why you can be served medium or rare steak and not medium chicken. Searing the outside of a steak kills the entirety of the bacteria that are on the surface. The meat must be fresh if you’re going to eat it raw, and you are supposed to carve away the outside layers all around when preparing it regardless, which is how I prepare my own tar-tar.
I have seen commercial kitchens and I have trust issues with undercooked food. I trust myself to make tar-tar at home because I understand how to handle the meat properly. I need to actually know the restaurant before I’ll risk eating tar-tsr there. I’ve had food poisoning that’s nearly killed me before, and it’s not an experience I’d like to repeat.
Most of the bacterial growth on a steak will be on the outside. As long as the outside is seared, the odds of food borne illness is very low as long as the meat is fairly fresh or has been aged properly.
My personal issue with tartar is that the meat is minced. Every surface is now a potential surface for bacteria to grow. The meat must be extremely fresh and the surfaces and knives used to prepare tartar must be very clean or contamination is a real risk.
Planes are absolutely not mostly fine with birds. It’s considered one of the most common hazards of aviation.
Where the fuck do you life where you’re heating your house with kerosene?
Or How’d you get a computer with modern internet in 1904?
I wonder what happens when a train traveling at 320mph hits deer.
Or a bird if it’s elevated tracks.
I can’t imagine this is good for the train.
LLM’s don’t know anything.
They have no capacity for thought or introspection. It receives an input and selects tokens that match an expected output. That’s it. Yes it has been fed the entirety of human knowledge to be able to do this. But that’s like a human memorizing the entire mandarin alphabet but never learning mandarin. An Al just makes a sentence that looks right, or generates an image that matches the tokens in a prompt. I could make a mandarin sentence with random symbols, and it might even say something. Doesn’t mean I speak mandarin.
Yes, we all know how your mother works on the weekends
If you have a functioning brain, LLM’s are just a really fancy text generator that gets facts correct about 50% of the time and can be useful for writing emails if you spend the time to proofread the email before slapping the send button. It’s also fairly good at generating functional boilerplate for programming projects. Really handy for small projects. However, LLM code, in my rather limited experience, quickly becomes a trap on more complex projects because the LLM has no capacity to grasp the scope of a project and frequently leads to spaghetti code that is utterly unmanageable. This may be operator error on my part, or just because I’m using local models that are limited by my machine.
The rest of society view a computer as a magic box where their favorite TikTok’s live, and thus are terrified that a machine can imitate human language well, and are too brain rotted to bother proofreading or fact checking a goddamn thing, so they assume LLM’s can accurately and plainly articulate the whole of human knowledge to you at a whim.
I am at a point where if I hear anyone saying how smart “AI” is, I just assume they have a room temperature IQ and lack any critical thinking skills whatsoever. It’s not an AI, it lacks any intelligence whatsoever. An LLM simply generates sentences based on what was fed to it. It has no capacity to understand the words it’s saying. It just knows that based on these tokens, the next set of tokens should be X, Y, and Z. It’s a Chinese room, It’s fucking autocorrect on crack. It’s a waste of time and resources for 90% of things it’s used for.
Apple Maps does not update their map as frequently though.
I drive a lot for work and frequently have to go into neighborhoods that are currently being built or were built 1 to 2 years ago. Even the ones that finished construction over a year ago either do not have the addresses in the correct place, or one just flat out shows up as an empty field with no roads despite being fully finished. This is very fun at midnight on a Saturday when the builder decided to put all the house numbers as engraved numbers on grey bricks built into the garage corner.
Google Maps on the other hand will take me to where I need to go nearly all the time.
That is exactly the reason LLMs are in a bubble that is practically doomed.
The horse to vehicle transition is actually a good analogy. When society moved away from horses, people bought cars. The companies that sold cars, if they weren’t completely incompetent, made instant profit off the sale of the vehicle. The oil companies made profit off the sale of oil as a lubricant and oil byproducts as the fuel. Companies and private individuals that used to use horses for transportation of goods and for production had real benefits they could see instantly. A horse requires large amounts of food that must be stored or purchased regulars. It is an animal that has physical limits to what it can do. A horse can only travel so far in a day before needing food, water, and rest. The amount of goods you can transport via horse team is limited by this. A car or truck needs fuel like a horse needs food, but refueling takes minutes not hours, and a single engine can replace entire teams of horses, making the transport of bulk goods far more efficient. The car can use the exiting roads for horses, less efficiently overall but still more so and faster than a horse. This efficiency and speed of transport had real benefits seen on paper practically overnight and led to massive infrastructure investment to take advantage of the vehicles advantages.
An LLM can initiate human writing patterns. That’s it. That’s all they are good for. It requires massive infrastructure advancement to even begin to work, and can only really replace communication and other largely writing tasks like programming. Although with any skilled task, even customer service, there still needs to be a human making decisions and ensuring the output matches the goals. So where a single engine can replaced 10 horses that you can then sell or dispose of because you bought an engine, an AI can replace a few people, but you still need so many experts to verify its doing the job. Imagine if every truck had to have a horse walk next to it to makes sure it was doing the horse’s job correctly.
By the time investment in roads and fuel infrastructure were made, vehicles had already been shown to outperform horses in every way. Yet LLM companies are putting the cart before the horse. They’ve invested heavily in the infrastructure for their products, and haven’t yet found any role in which they are more profitable or better than a human. So they’ve partially reduced the cost of their services and are operating at a loss. Can they sustain that until every programmer or customer service representative has died? Highly unlikely. If LLM companies fail tomorrow, 99% of the staff that was downsized can be rehired fairly rapidly. It will lead to a massive economic shift that will almost certainly trigger a depression, but the total number of humans that can still do the job the old way is practically unchanged. Whereas after over a hundred years of vehicle dominance, you’d be hard pressed to find enough horses on the entire planet to replace every vehicle in a country the size of America.
Utterly useless.
Can’t message people, the primary communication system for the target audience, without using cumbersome voice to text.
Can’t browse the internet, the primary use case for a modern phone.
Can’t really answer phone calls without a separate headset.
Can tell time, which is good for a watch. Has a battery life measured in hours, which is laughable for any modern watch.
Anything the Apple Watch can do is better served by the phone that you need to pair with the watch anyway, or a seperate mechanical or digital watch. Or just the phone. It takes 3 seconds to pull out a phone and look at the time.
And if you want heart rate monitoring or pulse oxygen readings: Fitbit did it first years before the Apple Watch. All Apple did was let you struggle to read text messages on a tiny screen that also tells you that your heart rate is elevated.
Actually ripped from the German playbook circa 1940.
The SS were given a state sanctioned wife for the purpose of producing as many “Aryan” children as possible.
I see you’ve never lived in a rural area
Yeah, whoever voted for that hunter guy must have been an idiot
In one of the SG-1 episodes, the gang is stuck in some kind of underground jail where the captors just throw people and food through the gate. There’s no DHD on the jail side. So some of the prisoners try to escape by going into the wormhole but it’s the wrong way. Some also let themselves get swallowed by the woosh.
Gang escapes by using some mcguffin electric tree roots and manually dialing the gate.