somehnreader

Thats also true for multiple choice tests as opposed to real-world programming.

Or if you are like me (and a bit stupid):

1000 = all possible combinations from 000 to 999.

less 10 combinations with all numbers identical is 990.

now there are three patterns for numbers to be identical left that need to be taken out: 00x 0x0 x00. The x can be replaced with all digits != 0 in this example, so thats 3 (patterns) x 9 ('x' digits) = 27. That times 10 digits that can have double patterns is 270.

1000 - 10 - 270 = 720.

I think he was wrong about the Apple watch market size, but I like his argument.

So many charts and such a boring conclusion.

Why is the male attractiveness axis flipped in that big blue box? Why is it not mentioned that the turquoise area stretches only on one axis and not the other? Does that mean that super attractive women are more likely to engage with less attractive men than the other way around? I struggle to understand the chart.

Their old data posts (pre-acquisition) were exciting and captivating like short novels with few, but very clear charts. This made me pity professors at uni that have to read uninspired papers and theses at the end of the semester. Sorry.

Thats how it should be I suppose, but can't be taken for granted everywhere.

Thanks, very helpful. I am just looking at it and will have a bit of a think about this later :)

How is a graph database built under the hood?

I know that a decent RDBMS (simplified) will consist of the following:

- data in blocks organised with a block-size that the underlying filesystem likes

- a cache for the most frequently used blocks

- every index is a B-Tree with pointers to the blocks containing the tuples

Then there are column stores as well as row stores, and for compression you might have some dictionary encoding going on.

Now, how does the Graph Database look under the hood and what are the complexities involved? How is the Graph persisted?

As a twilio shareholder I would like to please encourage everybody to implement 2FA via SMS!

> how selling TODO lists to businesses is like taking candy from a baby

What does this mean?

"productivity chat market" <-- whats that?

I had to CTRL+F through the article to learn that PWA == "Progressive Web App".

Am I missing a trend?

Are Barcelona and Madrid meant to be overvalued?

I live in London and am considering to buy a house in Spain in 5 years time or so and the prices look pretty good in the countryside. I had a look at city centre properties too and compared to the southeast UK they struck me as quite sensibly priced.

I do understand that I am privileged to be able to consider this.

Could you elaborate on news & NLP in regards to stocks?

We tried sentiment analysis in uni a few years ago and had no good results:

The idea was essentially: news says: 'stock A is great' -> it goes up shortly thereafter

We tested our algorithms against classifying Amazon reviews & Tweets by sentiment. Those are filled with sentiment and easy to detect if it is a 5 star review or a 1 star review. The news articles we parsed all had near neutral sentiment. We ended up building a classifier that could detect the news category of an article quite easily instead.

My initial idea was sparked by the Gulf spill and the subsequent dip in BP, I wanted to detect and capitalise big events like that, but the news sources we parsed always seemed to significantly lag behind the stock movement, too.