227 karma · joined May 26, 2017
select count(*) from cars where condition;
If programmers are doing what you say that they are then the programmers are the problem not the library.Furthermore most ORMs (certainly any that I would consider using!) allow escaped SQL to be used - and if the query gets much more complicated than a couple of where clauses I consider using this feature.
A decent ORM used well allows programmers to program faster on the simple stuff but still write fast code for the complex stuff.
Is this sort of thing possible with Wayland? If so does Wayland already enforce the necessary process isolation or does something like Xephr for Wayland need to be developed first?
NB that being arrested once can hinder someone from getting Visas or jobs in the future and can result in social exclusion.
EDITED in response to a comment.
The effects of testosterone are far from as simple as you suggest. It tends to lead to higher competitiveness. This is not quite the same as recklessness.
Meanwhile during normal usage the driver (of whatever gender) is likely to be getting shot at. This will tend to have a larger affect than their gender.
Are you implying that being male means that they must be reckless? Or that if they were female they would automatically be careful and gentle? Careful throwing around those negative stereotypes.
EDIT: The driver is likely to be getting shot at. This is going to be an order of magnitude more important than slight gender differences.
I guess it gives some social pressure not to do superuser things?
Colorblindness also has very well established definitions. Note: learning what words mean should come before trying to redefine them.
Why would you use server instances in Azure to do ML? Something like Google CloudML (I'm sure that the other major cloud providers do managed Tensorflow as well I've just never tried it on their platforms) would be a better fit to a project with only two technical staff. Your two staff probably spent a combined total of one-person-month working on infrastructure.
Your issue with small data is very real. People need to stop trying to do ML on small datasets. The results will be sub-optimal.