Recent submissions

Comment · 18 days ago
Road safety. Personal safety. A consistent experience.
Comment · 25 days ago
Comment · 28 days ago
Comment · 10 months ago
Comment · 2 years ago
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.

Well done!

Comment · 2 years ago
Elixir is really fun. It hits the same spot for me as Ruby does for many others, bringing joy when writing it.
Comment · 2 years ago
Can someone explain why this issue deserves a 24h notice?

Seems more reasonable to me to have a much longer deprecation notice.

Comment · 2 years ago
Really impressed with how thoughtful _and_ fast they are delivering on their type system.
Comment · 2 years ago
FWIW, it's not self hosted. I use Fastmail. Thanks for the link about .xyz though, I was not aware it is associated with spam.
Comment · 2 years ago
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).

Comment · 2 years ago
Comment · 2 years ago
Comment · 2 years ago
It seems to be extremely rare, but do bear in mind that if Apple bans your account (Apple Id) you will lose access to your icloud files irrevocably.

See my previous posts on the matter for more info.

Comment · 2 years ago
Comment · 2 years ago
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.

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