But their gaze can also create bias. If you go to a window and look out of it, your dog will as well. If you mime looking at something your dog cannot see, it will come over and try to find what you're looking at.
Defaulting what is called "player view" on this page is a very odd choice. All learning material is displayed with the low string at the bottom (or "tab view" on the page). All guitar emulators I have ever used have that view as default.
This is also how it looks when you hold a guitar, so I have no idea where this "player view" comes from. If I take my guitar and lay it on the table in front of me, the low strings are closest to me not farthest.
Thanks for the feedback. I chose the default orientation to make a downward mouse movement produce a downstroke. In tab view that relationship is reversed. But I understand your point that tab view is more familiar to guitar players and matches learning materials. I will reconsider the default and how the view options are presented
This would make sense if you're going with keywords, like "downward". But this logic only holds in language. If you compare it with human experience, then it no longer holds.
For example, did you notice that if you look at your own body, your legs are pointing "up"?
If the tool is meant to help people learn the real instrument, the view from player's perspective (low strings closer to the user, down on screen and lower frets on the left) would support the.process of learning. And if you'd like to be really inclusive I'd suggest also including a view for left handed players.
The hardware miniaturization gains have finally dried up, though.
I am pretty certain that the current state of the art silicon feature size won't shrink again for at least another decade or two.
It normally takes about a decade to mature a tech which can create a smaller feature size into something commercially viable for mass production scale, and no further improvements have been in the pipeline for that long now.
So it's like the "next piece" indicator while playing Tetris is just blank.
The 'job hopping advice' is always so one-dimensonal as if salary maxing is the only metric that counts. You should always take everything into account
- work climate and coworkers
- work life balance
- legal rewards for staying (This might not apply to the US but in most of Europe you get progressively harder to fire the longer you stay at one company)
It is still good advice at early to mid stages of career. You can get into senior level much faster, and potentially learn much more, since you're less pigeonholed into current role, and you see more of how other organizations work.
This. I hate interviewing. I hate having to "learn" new people and corporate processes. I like being able to rely on intuition I've built up over time, both about my colleagues and the products.
Bouncing from place to place could have earned me more money over time, but I valued the stability of sticking around for longer stints.
because when doing whacky hardware you need to control the entire UI stack. Android can never deliver this because of the fragmentation and you get apps that don't work right and bug out constantly.
Apple can force developers to use APIs that make the apps work correctly on the foldable even if they are not directly optimized.
The most important part of a foldable is the screen and they are designed and made by Samsung, so hard to come up with something Samsung couldn't have done in their own phone.
As someone that mainly switched to e-readers because of bad eyes, this is way too small. Even the bigger x4 seems too small for the font size I feel comfortable with.
My professor thought that there could be paper in my thesis. Thankfully they did not insist. Work was pretty crap and useless(mostly due to happening in between versions of project/idea they were working on). But well I did graduate so that is fine for me.
I can't even imagine the horrors what papers on some Master thesis would look like. Often as there is so many graduates they are not that novel topics anyway.
And if you play by the spirit and not by the rules, you're fucked. Because, with masters, it takes about a year to get to the first publishable result, and about a year to publish it in a respected journal. And that's the happy pass. You might get an extra rejection, or fall into the back of the publishing queue. This could cost you three or six month easily.
They have no fab for CPUs, they are manufactured by Samsung and TSMC. The bottleneck is in manufacturing RAM not CPUs so there is nothing Apple can do here.
Recently Micron pointed that finger and threw that shade in Apple’s direction saying that their priority for years has been squeezing margins in their favour, so there wasn’t the slack in the system to build the fab capacity that is now desperately required given the sudden need for RAM now that there’s a cause to use it in AI Inference.
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