asher_

Why is this scummy exactly? If a salesperson was to try to sell to you in a store, they would take into account how you appear and act to tailor the sale. There's nothing wrong with that. Why is it suddenly bad if a machine does it?

Excellent! One of GCPs many advantages (outside of it's technical ones) is it's simpler pricing model.

Can you explain? The analogy seems like a terrible one to me.

We are not talking about charging different amounts of money depending on the brand of device you are consuming the data on. NN is about not differentiating the cost or quality of bits based on their source. In the US, can you not opt to pay a slightly higher rate for renewable energy? (This happens in Australia).

I'm all for NN, but the analogy Sam used doesn't hold in my opinion.

As an additional thought from someone outside the US: NN doesn't exist in places like Australia, and has actually overall led to better services, especially in the early days of the internet, because overseas data is significantly more costly to provide than local data. The difference is that we have more robust competition and we can more easily switch providers, where is seems in the US (purely based on things I've read on the internet) that the near-duopoly cuts consumer choice, so if NN was not in place, people would have little ability to switch providers, and they would be stuck with it.

Is the lack of competition the real issue here? If people in the US had a choice of many providers and it was easy to switch, then people would likely switch to services that are Net Neutral.

If that's true about this tech, then it will be awesome. The focus issue is the only thing that kills my enjoyment in 3D movies.

These types of articles seem to always get the same mixture of responses. The biggest problem that I see is that everyone starts with completely different sets of assumptions and they are almost never up front about them.

The lack of cited sources in articles like these leads people to bolster or criticize particular studies that they have read or heard about, usually without referencing those. Many of these studies are either flawed or contain assumptions that some people don't agree with, so this ends up going nowhere also.

Are there any really good studies on this topic that we may discuss as a common point of reference? Once that take into account all the facts, and don't start with assumptions like the following:

1. There should be equal numbers of men and women in tech (or there is some other ratio that is preferred or correct). 2. Women and men in tech should - on average - be paid the same.

Some people have these assumptions as part of their personal belief systems, but they entail a whole bunch of other assumptions that are not prima facie true.

One other huge weakness in these kinds of studies is that they measure the things that are easy to measure; things like education and experience. If companies are hiring compensating employees rationally, they would use these only as heuristics, and have some measure of how much an individual employee would contribute to the company as the determining factor.

Measuring job skill, as well as all the other skills that go into being a good employee is really hard, but until a study tries to actually do this, they are coming up with conclusions that aren't at all useful in the real world.

This is spot on. What makes the above probabilities look incorrect is that people are assuming that the algorithm understands the relationship between tiger and animal the same way that humans do. Clearly they are evaluating each independently.

This seems like a PR-based defensive move to me, rather that one rooted in principle.

This practice has been known for some time (the recent news is not new at all) and has been used to prevent drivers from being caught up in the regulation battles by taking fake fares. This move makes drivers' experience worse.

It seems inconsistent for Uber to maintain their position when it comes to undermining/circumventing taxi monopoly laws and also make this move.

Is there a broader context or principle that can explain this in a way consistent with Uber's values?

A cache seems like a particularly odd example to choose for this. If you are changing the way a cache functions, then presumably you either have fairly short expiration times (problem will fix itself) or you would have some form of cache invalidation as part of the deployment process.

Additionally, it would have been nice to see some mention of patterns that solve this issue more completely, like CQRS, where state is disposable.

Does anyone know why GCP isn't one of the supported cloud providers for EE? This is surprising to me since they had docker-related offerings a long time before AWS and Azure.

Can someone explain this to me?

If I own something, I want to sell it for as much as the market will bear. I don't want to sell it and have someone flip it for a huge margin almost immediately.

How is a large first-day bump not considered a failure for an IPO? What is it about the situation that reverses a common sense understanding?

> companies normally try to price it so it goes up about 20 percent on the first day

Can someone explain this to me? Why would a company try to sell its shares for less than they could get? When I see something like this - 44% up on the first day - I immediately think that this was a complete failure as an IPO, because the company left so much money on the table.

Am I crazy? Or can someone set me straight.

Did Uber fire the driver? Or are they ok with passengers being recorded and those recordings being made public?

#23: Scrolljacking is still annoying as hell.