Yes, creative jobs near universally provide licenses to creative cloud. Aside from companies not hiring people without that experience, the amount of saved assets and templates, along with the deep integration between apps makes the prospect of a full “migration” a ridiculously expensive prospect.

The value in these assets is not just in video files or pictures you can easily migrate to another app. It’s the complex scripts and templates that allow creatives to make custom branded content on the fly. Like a lower third that adjusts styling depending on the name you put in, and auto resizes to fit the text, etc.

That’s what they are saying though. These shouldn’t be thought of as “rules”, they are suggestions near universally designed to point you to the most relevant content. Ignoring them isn’t “stealing something not meant to be captured”, it’s wasting time and resources of your own infra on something very likely to be useless to you.

The law that passed congress, as written, explicitly gives the sitting president the ability to delay the ban by executive order.

Server hardware isn’t free. At the end of the day, SOMEONE has to pay the bills. Either you are the customer, or the product. If you insist on being the product, you don’t get to be surprised when platforms focus on the actual customers that actually pay the bills, by enshittifying the platform.

Zigbee devices are a pretty small minority of the larger IoT landscape. Most consumers are likely to have more wifi or bluetooth only devices than zigbee (at this point in time). The notable exception being Hue bulbs.

Apple is pretty much the only company in the smart home space right now that not only allows, but requires that devices be able to function locally, without having to call home. They CAN call home, but they continue to work just fine locally if say, the internet is down. It’s a central tenant of their homekit standard.

There are multiple other browser startups in development that are not Chromium based. Like LadyBird (which is completely independant), and Zen browser (which started as a FF fork)

Well it has to go somewhere, you can’t just take in water forever with nowhere for it to go. So either it’s non-potable water being returned to its source, or it’s closed loop. In either case, it’s not really a problem.

It doesn’t use water in the sense that it is consuming it. It “uses” water in the sense that it is temporarily in a datacenter, gets a little hot, and then leaves the datacenter. I don’t even think a lot of datacenters use actual drinking water, instead taking water directly from a river, warming it slightly, and putting it back in said river.

Not to say I like AI, or think it’s a good thing. But this phrase that’s been going around just bugs me, because it’s really misleading. We should be focused on the ridiculous amount of energy it consumes, not the water it temporarily uses.

The answer to this question is quite simple, because Google (excluding the Pixel line) isn’t making the actual phones, just the software. The actual manufacturers (Samsung, Motorola, Huawei, etc) are taking Google’s OS and putting it on their phones. This case mostly hinges on Googles behavior being monopolistic to them, not to the end consumer.

On the other hand, Apple make both the OS and the Hardware, there’s no manufacturer they’re forcing the app store on, so the same rules don’t apply here.

As far as I’m aware, all donors are public, and have been for quite some time. Anyone can get exact numbers down to the dollar of each contributor a major politician took money from.

Sure, but the difference here was that all those companies were offering something different. Some had better results than others, a better ui, more accuracy in certain niches, etc. But 99% of AI companies now are all effectively reselling the OpenAI API. They aren’t making an effort to differentiate themselves at all. It’s as if Google was the only shop in town, and everyone bought all their search data an algorithms to slap their logo on. That’s just simply not sustainable at anywhere near the scale it is now. This won’t be a 3-5 year decline, it’ll be a 2 month crash.

I van totally believe that it detects AI generated content 99% of the time, that’s trivial. What I really wanna know is the false positive rate. If I write a program that flags everything, it’d have a 100% hit rate. It’d also however have a crazy high false positive rate.

Curved screens are often significantly harder (and more expensive, even at independent shops) to replace than standard flat screens.

It’s not just random jitter, it also likely adds context, including the device you’re using, other recent queries, and your relative location (like what state you’re in).

I don’t work for Google, but I am somewhat close to a major AI product, and it’s pretty much the industry standard to give some contextual info to the model in addition to your query. It’s also generally not “one model”, but a set of models run in sequence— with the LLM (think chatGPT) only employed at the end to generate a paragraph from a conclusion and evidence found by a previous model.

Relaying a key signal 20 ft when you know the key is there isn’t too tricky, like when you’re home. But I would propose that trying to relay a signal across hundreds of feet, like a busy mall or store, when you’re not even sure the owner is there is quite another thing. You can also require that the IR blaster is in the car before starting. There’s also a technology Google has been using for a while now where the device (car) would emit a constant ultrasonic signal for the other device (key) to pick up on to determine if they are close to each other. Something that could be done through clothing, but not easily relayed.

Potentially better idea, add a gyroscope to the key fob, and stop broadcasting after the fob is perfectly still for some threshold. That way when you set it down inside it can’t be relayed, but if it’s in your pocket, it won’t remain perfectly still, and will start transmitting. Could also add an IR blaster to detect if you set it down in the car. Battery life would start to become a bigger issue, but I think solutions to these problems could be engineered.

Indexing and lookups on datasets as big as companies like Google and Amazon are running also take trillions of operations to complete, especially when you take into account the constant reindexing that needs to be done. In some cases, encoding data into a neural network is actually cheaper than storing the data itself. You can see this in practice with gaussian splatting point cloud capture, where they are training networks to guide points in the cloud at runtime, rather than storing the position of trillions of points over time.

I firmly believe it will slow down significantly. My prediction for the future is that there will be a much bigger focus on a few “base” models that will be tweaked slightly for different roles, rather than “from the ground up” retraining like we see now. The industry is already starting to move in that direction.

While I agree in principle, one thing I’d like to clarify is that TRAINING is super energy intensive, once the network is trained, it’s more or less static. Actually using the network isn’t dramatically more energy than any other indexed database lookup.