cracker_jacks

"College became default"

Oh, you're talking about using the forward gradient method. This still does not make much sense because if the projection of the gradient in that direction is zero or near zero, that is a very weak step in reducing the loss.

I believe what you are describing is grid search, not gradient descent. Gradient descent is not an exhaustive search.

I agree, in expectation, the orthogonal directions cancel out. But the variance does not cancel out. Are you using some sort of momentum-like optimizer?

> The more dimensions, the simpler the surface. With enough dimensionality, everything is a straight line.

Even assuming this statement were true (which is a big if). That does not explain how it would outperform gradient descent.

That does not make sense, can you expand? The steepest descent direction is far more useful than a random direction in high dimensions because that random direction has a greater chance of being nearly orthogonal to the steepest descent direction.

Yes, I am certain the background this particular website provides will be super objective and unbiased.....

Can a viable business be made around targets/victims sending their phishing attacks to a reverse engineering lab? The more valuable the target, the more likely an unknown and valuable exploit might be surfaced. As a consequence, this increases the expected cost of attacking these targets/victims.

lol, 'even', I find HN in particular loves pitchforks.

"Air tag" like trackers on decoy packages mixed in might be a cheap solution to this issue.

Agree that the reddit is more relevant to me because it has multiple perspectives in a discussion format, but I don't see any particularly bad links with the first search. The first one just has more conventional review sites like GoodHousekeeping and Forbes.