I've had a similar idea on my backburner for two years, as a fun project to dabble in Elixir, but this is just so much more _fun_ than my version which looks like an admin page that just happens to have a Vim canvas on it.
In hindsight, yes that was a bad move (especially considering that my work laptop is still locked to my banned ID…)
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).
With risk of being spammy, this is probably the most relevant discussion I've seen so far on HN w.r.t my experience of being locked out from my Apple ID.
I hope legislation will force Apple to step up and be more transparent / helpful.
True, but Apple specifically in my case was abhorrently indifferent to the consequences and their support structure is entirely unable to help you. If you have a dedicated storage provider, you lose their single service only. With Apple, you lose everything you have attached to your account which carries a much higher risk.
Read Deep Learning with Python [1]. It's ok not to understand everything, but doing the labs will be invaluable.
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.
I'd recommend moving to development that interfaces with ML instead of moving to being an ML practitioner. You have much faster feedback cycles, your work is predictable (engineering, not science), and you don't feel the pressure of never reading enough papers.
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.
I've never found this style readable unless with pipes (bash / elixir), where I love it. With any other syntax, I find it just adds mental overhead. Maybe because you have to read it backwards?
Automated account suspension is anti-consumerist for the % of false positives. And since that % likely is low, companies get away with ridiculous processes for recovering your account.
As an aside, even if all else fails and I need to factory reset my devices - I'm not sure if that's straight forward if you can't log in to the currently enrolled Apple ID...
Honestly don't know where I'd even start to initiate such a process (not based in the US). I'm still in a phase where I'm angry enough to keep bugging support / going to Genius bar with the issue.
Pretty happy with my existing workflow. It started out with a photo organization script a couple of years ago that I ran manually and has now developed into a fully automatic system.
1. syncthing uploads photos from my phone to my NAS (receive only)
2. scheduled systemd service (hourly) on my NAS reads newly received photos, renames them by date, moves it to my photos catalog
3. photoprism starts a scan of the photo library (triggered by systemd when 2. finishes)
4. weekly systemd service syncs the library to backblaze
I am so far merely an enthused observer, so I can't give any insights into the experience of delivering business software with it. Here's my observations though
- Engineers that work with this stack love it. The two most common issues I've seen are lack of static typing and difficulty in hiring
- Great for IO bound tasks due to collaborative scheduling. For CPU bound tasks performance is better offloaded to e.g Rust (see rustler)
- Exception handling is done by processes having supervisors. My mental model for this is similar to pods & deployments à la K8s, but at any abstraction level you want inside your component.
- Elixir pleases many crowds: ruby, FP, performance, resilience
- Phoenix (an elixir library) is seen as a next step from ruby on rails.
- Concurrency model is considered to be simple & powerful
I'm running NixOS on https://teklager.se/en/products/routers/APU2E0-open-source-r.... Working great. And interestingly when I upgraded to NixOS 22.11, its power consumption went down from ~12W to ~7W. In the NixOS matrix channel it was suggested that it might be because of new power management work in the kernel.
https://skogsbrus.xyz/dont-put-all-your-apples-in-one-basket...