Can you give an example where theories and techniques from other fields are reinvented? I would be genuinely interested for concrete examples. Such "reinventions" happen quite often in science, so to some degree this would be expected.
The article mentions also that the computers are short-circuiting. I guess that you can lose a lot of energy when there is a short circuit. Maybe with an infrared camera, one can get an idea where all the energy is wasted.
Apparently the author did check that sentry was not enabled.
Sadly, several python projects do not use semantic versioning, for example xarray [0] and dask. Numpy can make backward incompatible changes after a warning for two releases[1]. In general, the python packaging docs do not really read as an endorsement of semantic versioning [2]:
> A majority of Python projects use a scheme that resembles semantic versioning. However, most projects, especially larger ones, do not strictly adhere to semantic versioning, since many changes are technically breaking changes but affect only a small fraction of users...
I was asking myself the same question. I would love to see a derivation from e.g. the Navier Stokes equation for this. I think, intuitively, when you draw the streamlines under the rectangular wings, the applied force should be related to the curvature of the streamlines (which is larger at the beginning of the wing).
I made some simple 2D Navier-Stokes solver here where you can use the mouse to draw a section of a wing:
This is true, but you can inspect the package (and its dependencies) once installed and before importing it. Now it is an all manual process (with default tools, as far as I know), which contributes to the current state that only very few people inspect their packages.
It can give people also a second change to notice, e.g. the typo in the package name.
I would be genuinely interested what are the inconsistency from a Python-perspective.
I am former Matlab/Octave user. To me the julia interface of matplotlib is actually quite nice to use, but unfortunately the installation is a bit brittle.
I once had an issue with my LG5 phone who entered in a boot-loop (also a common issue with LG4). I don't have a head gun, so I put it in the mainboard of the cell phone in oven for maybe 10 min at 150°C to reflow the soldering. At least I could reboot the cell phone and copy the important files. But after about an hour, it enter again the in boot loop and was broken for good.
It took me awhile to get used to the dot operator, but what convinced me that it was a good choice is that also your own function can be broadcasted with function_names.(args). For example is you have a function process_a_file(in_filename,out_filename,parameter_value), then you can easily process also a list of files with process_a_file.(in_filenames,out_filenames,parameter_value) (if parameter_value is a scalar it gets broadcasted). I think that it is similar to numpy ufunc's but every function have this possibility.
A useful macro is @. which allows you to write "@. A * B * C" instead of "A .* B .* C". Also the macro avoid the creation of an intermediate vector with A times B.
On land, I guess we would agree that an oil spill in a pristine rain-forest is worse than a pristine desert.
If you need a quantitative measure, one can use e.g. the number of difference species per area (biodiversity) to know the area where an accident has the least impact. Or maybe the number of endangered species.
Granted, computing these metrics are however quite difficult. However, what is easy to measure from space is the amount of chlorophyll in the surface water of the ocean. Besides coastal areas, the Arctic stands out as relatively chlorophyll rich.
I am wondering why the julia lox interpreter is so slow. Could it be that there is a lot of type unstable code or could it be an issue with julia's garbage collector? (I can relate to the comment that it is quite hard to find usable information from julia's profiler).
> This died a long time ago with the pervasive use of NPM and PIP and the likes.
When a malicious package is found on NPM or PIP, it will get removed. However, it is quite unlikely that a website will be taken town for a malicious script (or only after a long time).
I really doubt that most readers of HN would run a random script unless it comes from a source they trust (trusted enough to least to remove a malicious script in a timely fashion).
