Region affects pricing, what payments methods are availanle or accepted and also what apps are available. Some banks, government service apps and few commercial companies limit apps to certain regions.
Unlike Android there are way more hassle in installing iOS apps under different accounts with different regions. On Android I just have 5 diffetent accounts with different billing countries and it just work.
Also AFAIK iCloud family features dont work cross-region.
This got me curious as-well. It cannot be bound to phone number or carrier, since that's not a requirement to create an apple account. It could be bound to phones sold in the UK, but that is easily avoided. It could check the GPS, but then you can just take a trip over to France to activate your account.
But apple also cannot just stop selling or supporting their product in the UK either. So that card is dropped from both sides. As long as apple is beholden to a few million costumers and subscribers to their services in the UK; UK law is able to pressure them, fine them and restrict them because of those users.
We don't want it the other way around; companies placed outside the borders doing and selling whatever they want without a care. So we end up in a negotiation.
Apple choose to maliciously comply; subtly revealing the TCN, giving all UK users notices about what their government demanded of them to encourage public outrage, and push on the correct parts of the UK government that has their interest in mind.
It may actually be illegal in US law for Apple to leave the UK market for ideological reasons. Public companies are beholden to shareholders and can be sued for not acting in their interest (fiduciary duty, Delaware General Corporation Law). (I loathe these laws, but that's a separate discussion entirely.)
So sure they _can_, but at a cost that strikes the option from the cards entirely.
I got a job by applying to an old listing, for the wrong city.
Small companies may look to fill a certain set of skill-sets, and if that is filled by one person or three doesn't matter much; can puzzle around to make the workload fit what is found. large companies may hire 10 people generically, but may stop at 5 if they don't like the candidates, or hire 16 after a good set of resumes.
Forcing companies to be overly specific in the roles is just making it harder to apply.
Its in a modern and easily to deploy package. The hype is a bit wierd. "0 cost output tolkens" is such a silly phrasing.
I would have never considered importing pytorch for filtering through log files before even knowing my way around it. But if i can type a filtering condition by text and hit enter; i may actually use that to save some time.
Id want something local though, but thats hardly a difficult demand for what it is.
Scripts and debugging, one-off log parsing or filtering.
I saw an article about 2+ years ago of a researcher using a small local AI strapped into excel to evaluate the abstract and intro of 10000 papers for "papers that research X in domain of Y", and let it loose.
jev is probably more capable avd faster than that workflow was, but saved one dude a few very grindy weeks for a litteratur review.
It's amusing how long it took, and much hype it gets for someone releasing the least revolutionary ML architecture in a new package. But i can see a fair few uses.
Depends what you man by "more specialised". You wont train very good language understanding without alot of data. It probably uses the core tranformer stack from an LLM.
Classic classifiers are regularly just tuned general models; Training a CCN on ImageNet and tune it for cats and dogs gives better results than just training it on cats and dogs.
There is likley a small network used to tranform model output vector to probabilities, but that wouldn't be massive. Retraining that small network for specific task may beat jev; but that's bairly considered training by modern standards.
> Ergonomics, cost profile, and ease of use are new.
Following AI from the academic papers side; jev really feels silly. They one-pass the LLM tranformer stack and tune the output network for a probability value.
(some clever pararellization optimisations to make it viable to offer as an api, since the normal kv cashing no longer works if you oneshot the tranformer)
The largest change is the packaging; An api with a tolken based pricing, and a schema to define the output structure for quick setup.
Previous projects would probably involve installing pytorch, running a converter script on Qwen, and write a fair bit of matrix math to change the output shape.
We are increasingly seeing "Sorry out <Black box> made a mistake, the developers are fixing it" rather than any one person taking responsibility for the decision. This is a growing problem that is made worse by AI. Over-inflated trust in AI output is also documented issue; "The perfect billion dollar targeting system with a 99.9% success-rate (on its verification dataset)" just made a "fluke"; "Nothing could have stopped this tragedy"
When it was just called a "database query" or "algorithmic estimation", it was more implied that the actual decision is left to be made and that no analysis had actually been done yet. But when AI preemptively says "I've analyzed every source of data and come to the conclusion that this ship is carrying nuclear bomb material"; it really sounds urgent doesn't it? It probably doubled down when asked about it.
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