notemaker
Joined 8 years ago
Joined 8 years ago
Like everything else, it's tied to your Apple account. You risk losing your browser history etc. You _can_ export it if it happens, but it's easier to not have the issue in the first place.
> 2. Is there an alternative to "Find My" that I can use with AirTag?
https://github.com/seemoo-lab/openhaystack maybe? I haven't tried it.
I was able to regain access to my phone and laptop eventually, but my airpods are still "someone else's" and my air tags were permanently broken (ringing a bell whenever they are moved).
As for derisking things from Apple:
- Whenever you buy apps on App Store, ask yourself whether you'd be OK buying it again if you get locked out
- Don't use icloud for email
- Don't use icloud for permanent storage (it's OK for transfer)
- Don't use safari
- Ideally don't use "Find My" - it's what locks your device to your Apple ID
- Use an external password manager like Bitwarden
[1]: https://skogsbrus.xyz/dont-put-all-your-apples-in-one-basket...
https://skogsbrus.xyz/dont-put-all-your-apples-in-one-basket...
Well done!
Seems more reasonable to me to have a much longer deprecation notice.
As an Apple noob at the time, I assumed that if my MDM-managed device prompted me to log in with my Apple ID, that it of course would be an allowed action.
With regards to data being shared, the only thing I noticed was wifi passwords and peripherals pairing (apple keyboard).
I hope legislation will force Apple to step up and be more transparent / helpful.
https://skogsbrus.xyz/dont-put-all-your-apples-in-one-basket...
See https://skogsbrus.xyz/dont-put-all-your-apples-in-one-basket... for more info.
See my previous posts on the matter for more info.
That book should set you up with the fundamentals. Pytorch is the defacto standard right now for training; during deployment you will use whatever your deployment setup allows (e.g. embedded devices typically have their own inference frameworks).
Python is the lingua franca for anything in machine learning, but other languages are used where necessary for performance or for ecosystem benefits.
[1]: https://www.manning.com/books/deep-learning-with-python
To do that you only need to understand the fundamentals of tensors, some basic knowledge on what the big no-nos are within ML development so you can course correct your peers if they break them, and either focus on the operations side of things or deployment. In both cases, having a knack for optimizing bottlenecks will be very helpful since they will be present during both training and inference.