std_throwaway

This is different from the package that I've tested (PyInstaller or py2exe).

Is Nuikta compatible with numpy, pickle, etc? I remember that numpy was very problematic with compilers like pypy for a long time.

People who are importing numpy usually are also not inverting massive matrices. These libraries provide tons of functionality that you would like to use in a short script. For people accustomed to these libraries the code is quick to write in a reliable fashion, easy to read but not quick to run. Everything is great except the startup time (and speed when you have more data).

I imagine this could be handled by some kind of "fork". Where you instantly duplicate the whole process with copy-on-write.

Do we have numpy, matplotlib, scipy, pickle etc. for Go?

This is truly a problem. Even more so if you host your application on a network directory. Loading all the small files takes ages. I really wish there would be a good way to compile the whole application with all the modules into one package once you're ready to release. I really wish the creators of Python would have given such use-cases more consideration.

Edit: I'm aware that there are solutions that put everything a program touches into a kind of executable archive. A single file several hundred Megabytes in size. I've tested it. It doesn't really pre-compile the modules. The startup time was exactly the same.

But what else do you switch to?

Imagine you import tons of modules which often are only available in Python. This gets you going really quickly with your project and it runs very smoothly. Transferring this to C++ would probably take so long you won't even finish to find out before you run out of funding.

I have hopes that Rust or some descendant of Rust will get us there in maybe 10 years but in the meantime it would be better to get Python up to speed as good as possible.

The whole line stops if a single process has problems.

Given a 99% chance of working for every single process for hundreds of processes you end up with... well... not much output.

It's not that other auto-makers were sleeping all this time. They build new factories every now and then and increase automation where appropriate.

"ultra-high resolution" doesn't mean much if you can't see the problem.

"advanced machine learning" sounds dangerous once the machine damaged itself or killed a bunch of people trying to figure out things.

Some things are surprisingly hard to automate. When your parts are hard to grip and have high tolerances and some are broken, good luck in getting a robot working reliably.

Humans are in many conditions surprisingly fast, accurate, flexible, space saving and cheap when compared to a special robot that does the same thing.

You have to pay most up-front for solar. If you don't have the money at hand, forget about getting a credit when recession strikes. It's a good investment if you expect money devaluation without high interest rates but in a recession you expect deflation first and cash is king.

...and references the good old Reiser file system.

For transmission of data yes, but you still need "lots" of energy to drive the loudspeakers.

Coming from C++ it feels really weird that you can simply assign instance.new_name = value from anywhere without properly declaring it beforehand. You also never really know what you get or if somebody modified your instance members from the outside.

I don't actually see this as a problem; as long as you're only running one application like that.

They are supposedly not directly x86 compatible and thus will need to emulate it at a performance penalty. Not something you'd want in a "pro" machine.

> There are now architectures other than CPUs that matter.

You would make a killing with a CPU twice or ten times as fast. Many algorithms are only suited for single-core operation. I don't know if this will ever change. The focus has shifted to other architectures mostly because we've reached a ceiling for single-core CPU.

This is a tautology.

Similar to the conservation of energy in physics you can ask yourself "is there enough space for the information?". If somebody tries pulls two signals out of one signal, they better don't have too much entropy each. For a given bandwidth (number of independent samples), a given signal energy, and a given noise energy, there is a hard limit to the amount of information you can pull out.

If your signals are not "full entropy" or "white noise", you can do all sorts of funny tricks to increase the SNR to some extent. These tricks sure are nice but they veil the true issue at hand which is about information and capacity.

Is there an Add-On for Edge to open the page in another browser?

Quaternions are used to describe a rotation in 3D-space. Three numbers give the rotation axis and the magnitude of all four is the rotation angle. They are used instead of euler angles because they don't have a singularity problem (gimbal lock). I don't know if this plays a role here. Could these neural networks represent 3D transformations really well?

The sugar replacements might change your gut bacteria in the long term. You probably will not notice this for a long time until there are seemingly unrelated symptoms showing up.

At first it may seem alien to think about negative brightness but it really is necessary when dealing with different color spaces.

The area inside the "RGB" color triangle is represented by positive (non-negative) values. The sRGB color triangle only covers some part of the colors that physically can exist. If you have colors outside the sRGB color triangle at least one component will turn negative.

Not all colors you could represent by RGB-values do actually exist. You can even define a bigger color triangle covering all colors that exist with positive values but also some colors that don't exist.

It doesn't physically make sense but it is a very useful tool when doing calculations. By using floating point of sufficient precision you have to worry less about how you calculate your things. It's not needed but useful. If you know your calculations well you probably don't need float.

Here is an image of some color triangles and what area they cover: https://en.wikipedia.org/wiki/Color_space#/media/File:CIE193...

They did so for quite a while but somehow didn't really take off.

As I understood intel has the best FABs. About half the advantage over AMD lies purely in the production technology. ARM is said to be more energy efficient than x86. Yet, when intel makes ARM chips you don't hear them outperforming everything else. I wonder why.

I bet it didn't randomly lack fluid due to some stupid miscalculation. It's probably more like: "Due to unforeseen circumstances it needed more fluid than expected. But we adjust our numbers and next time we expect more (in the hopes that it will be enough, which it will probably be)."

This post shows how the "weak" are weeded out from the sciences. If your frustration level is as low as shown in the article, you won't become happy doing science. If you instead like getting hit in the face repeatedly for years, welcome onboard until you fall off!

They are measuring different things and use different transistor designs.

More efficient in what terms?

> I feel in my gut that some engineer, somewhere, MUST have asked this question at one of the big chip manufacturers.

Sure, they were about to commit career suicide but then they learned to love the bomb and went on with their day. Maybe they even tried to explain the problem to management but somehow it got lost in translation.

Except we're living inside the joke like goldfish in a glass.

Somehow this doesn't seem to be up for discussion.