So even this "plain text" site still transfers ~98 % bloat and just ~2 % content.
4,157 karma · joined August 26, 2016
So even this "plain text" site still transfers ~98 % bloat and just ~2 % content.
It was a relatively common method in precision electronics.
There could be other reasons as well, e.g. if these were ceramic capacitors they might do that to avoid cracking issues. (Because ceramic capacitors drift a lot by themselves, a drift/precision explanation does not make sense)
Sometimes there are still schematics, Agisight still does it e.g. for low-end power supplies.
Very clever...
>>> import gc
>>> 'foo'.lower()
>>> gc.get_referents(str.__dict__)[0]['lower'] = str.upper
>>> 'foo'.lower()
segmentation fault (core dumped) python
(That's the method lookup cache)A talk in this direction is https://www.youtube.com/watch?v=qCGofLIzX6g
Python is not a very dynamic language in the sense that you actually can't change a lot of stuff (and a number of the things you can change just segfault CPython). I think JS is more dynamic, for example. Or Ruby.
> That said, isn't it true that Windows drivers are userspace-based, anyway?
No.
Don't do that (the array part). Guaranteeing O(1) indexing isn't very useful but precludes efficient storage.
You also have to use GitLab.
For the sort of thing that you'd host on GHP, this is totally fine in my opinion. In fact, because CF is a pretty good CDN it likely accelerates page load times considerably for Non-Americans.
(I wish it'd be possible to do something similar for readthedocs, which only has one origin and it's located in North America, but alas this doesn't really work).
And there is/was a tiling window manager, but everyone I know who used it at some point migrated away because the configuration was Haskell as well.
My two 0.02 € on this:
Features have external and internal complexity. External complexity is documentation, usage, support while internal complexity is time spent on maintenance and development, increased complexity of the code, reduced stability etc.
External and internal complexity are unrelated, and when deciding whether to add some feature both must be considered against the assumed advantages of the feature.
When something wooden cracks or breaks and needs gluing, but you don't have any wood glue at home, you don't need to buy any. Just dissolve a sheet of gelatine (which you do have, no? :) in a bit of water and warm that in a water bath for five minutes. Use a brush to apply some of that to both glue sides and press it firmly together. As an added bonus, these glue joints become soft at ~50-60 °C, so can be fixed up later again. Structurally, it's pretty much as strong as any wood glue.
This is also the way it started with x86 and other second-sourceable processors. An Intel 8086 and an AMD 8086 are drop-in compatible. This is not the case with modern processors, they don't even fit the same sockets.
Note that the ECC on the processor's internal data buses and caches is completely independent of whatever ECC the memory controller may or may not use.
Needless to say, I didn't even bother to set these up lest build a rack for them (my normal rack is both full and too short). If packaging weren't so annoying with computer servers I would've sold them long ago.
[1] The good stuff. High static pressure, high flow fans from Delta with ball-bearings, and of course only speed monitoring, no regulation. These Sun servers make for excellent magnet boards for holding papers just without using any magnets.
With -O1 you can see that this part already happens. With optimization cranked further up it just inlines EraseAll, and EraseAll ceases to exist as a separate symbol. This is possible since Do is static and not aliased. Meanwhile NeverCalled continues to exist as a retq, since it is not static.
Replacement parts on these are also far more expensive. New turbo is a four-figure number just for the part. Injection pumps are bloody expensive, too.
Less optimized designs may also be a factor. CAD and FEM modelling has only become better with time, so back in the day engineers would rather make a more conservative design, nowadays that's not so necessary any more.
Python performance has been one step forward two steps back. For example, dict() has significantly evolved over time and some other things became faster as well, but most stuff just becomes slower as more stuff is added or previously native code is replaced by pure Python (see io in 3.0, which was reverted, or import).
Ditto for Python application performance. Python has exactly no zero-cost abstractions, so when something is refactored to use classes or big methods are split up, the result is almost always slower than before.
One point (100 °F ~ 37 °C) doesn't make a scale.