What is the plan when VS Code introduces all your features?
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And it is worth noting that prices are vastly different in China. Labor is much cheaper and licenses aren't respected. The cost of living is cheaper to a smaller profit margin goes a much longer way. As tfa mentions, there are identical cameras sold by different manufacturers. It is unclear if this is typical reskinning or designs being taken. Both significantly reduce the cost of things. I have no idea how much hosting costs in China.
Bandwidth to and from China is not that cheap, and you could be running this stream 24x7. The streaming service still works 4 years later even though the company whose name is on the camera has vanished.
So, who is paying the server/bandwidth bill? The camera is too cheap to afford indefinitely providing this service, so you can only presume that you're paying in another way. Probably there is some third party in China that the camera manufacturer makes a deal with. The camera manufacturer may even be getting paid to pick a particular provider.
> “We were really disappointed that Alphabet decided to change directions,” Komar said. “We have really had a great partnership with the Mineral team and from our vantage point they were just getting takeoff altitude. And then all of a sudden, you know, plans changed.”
Google is beyond parody with how they still continue to kill off promising products.
I can't picture any way to use a RAG to do that.
I can picture a way to do that that doesn't involve any model fine-tuning, but it'd be pretty ridiculous, and the results would probably not be very good either. (Load a static image2text LoRA tuned to describe the subjects of photos; run that once over each photo as it's imported/taken, and save the resulting descriptions. Later, whenever a photo is classified as a particular subject, load up a static LLM fine-tune that summarizes down all the descriptions of photos classified as subject X so far, into a single description of the platonic ideal of subject X's appearance. Finally, when asked for a "memoji", load up a static "memoji" diffusion LoRA, and prompt it with the that subject-platonic-appearance description.)
But really, isn't it easier to just fine-tune a regular diffusion base-model — one that's been pre-trained on photos of people — by feeding it your photos and their corresponding metadata (incl. the names of subjects in each photo); and then load up that LoRA and the (static) memoji-style LoRA, and prompt the model with those same people's names plus the "memoji" DreamBooth-keyword?
(Okay, admittedly, you don't need to do this with a locally-trained LoRA. You could also do it by activating the static memoji-style LoRA, and then training to produce a textual-inversion embedding that locates the subject in the memoji LoRA's latent space. But the "hard part" of that is still the training, and it's just as costly!)
Or how it is possible that in countries which do have this to some degree (EU for example), every time we try to look at something the archives have mysteriously been lost..