78 karma · joined August 12, 2014
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.
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.
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?
What does this mean?
Am I missing a trend?
I attended a few years back and learnt an awful lot, this is the course page:
https://db.in.tum.de/teaching/ws1516/imlab/index.shtml?lang=...
Haven't found the accompanying slides yet, but they must be somewhere on their website.
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.
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.
I am amazed what is done in large organisations sometimes. No one would ever sign off on me doing something like this. How do you convince the number crunchers that its a good thing to build things like this if there is no measurable outcome?
Not trying to be rude or devalue OPs work, but I haven't worked in an organisation that would allow for this in a long while.
I work with legacy systems all the time, lately mostly CRUD web apps, but I have debugged myself through pages of Excel macros, database drivers in enterprise systems and some code that was almost as old as me, and 100% agree with what you have written.
Bookmarking this post for future reference.
If I find repeating if statements throughout my code I put them in a method, end of story. Pseudopython:
def logging(logger, message):
if null == logger:
return
if null == message:
return
logger.write(message)
I don't understand why the author would even bother with more than this, given his example. Also note that I used two return (disguised goto's) as guard statements to check the conditions before I do anything.Absolutely not. That will never ever happen. F1, DTM, MotoGP, MonsterTrucks, NASCAR , Rallye Dakar are all well too amazing to allow for that.
I agree, your commute form SF to Palo Alto and similar boring activities will be automated away, but there are way too many enthusiasts to allow hobbyist driving to ever disappear completely. Maybe, but only maybe, have them confined to racetracks or leisure roads, but I doubt it.
Source: I love cars and bikes and would go berserk if someone would tell me I am not allowed to drive anymore and I know hundreds of people that would feel the same.
Try to over exaggerate the way natives speak your target language, almost as if you were mocking them (Do it by yourself first, not in a conversation as that might make them punch you).
Watch QI and try to speak like Stephen Fry does, or if you want a US accent do the same with American shows. After a while you get the hang of it and you speak more natural.
I am in no way able to trick English people into thinking that I am one of them, but other foreigners always assume I am English, to Americans I just sound British.
I only just started organising some of my non-technical writings in a repository and can already see the benefits.
When I did my very first project when I was 18 I spent money on a lawyer and incorporation before we made any sales, of course the project tanked after we made a couple of hundred in revenue and then split ways.
Took me ages to realise the twist though, because I was somehow set on making them oscillate further on every iteration and couldn't understand how I was to remember how far I went without a stack.
Basically what I wanted to do was: x left, if its not there, return. Next time x * 2 left, return to base if its not there, ...
And yes of course you will want to take a step back and change almost everything about it if you want it to scale to more orders of magnitude.
What I in _no_ way could do is rewrite fb or twitter at the scale these systems operate today with a billion users and even more posts and data points. I don't agree with the sentiment of the previous poster, but fb & twitter were initially basic CRUD apps.
Just general curiosity as I am technical and I have no idea how their models work. The three things that I am familiar with are:
1) I give you a small discount of my dayrate in exchange for a slice of equity (low single digit percentages)
2) You are an employee and take a paycut to participate in their vesting schedule with CAP table (the same as 1) really)
3) You are a cofounder and get a solid stake in the company but instead of providing capital as a technical person you are more likely to build an MVP for not much money in exchange for solid double digit percantages or equals pequals in the company.
Whereas their model is completely different and more comparable to real estate agents or similar.
That means these companies are willing to try, but that only gives you a 50/50 chance for the H1B, as everywhere else, right?
What are the exceptions for Azores etc? I didn't implement them, better check what the implications are.
I see the problem probably as an easier thing because I was able to roll it out bit by bit and only switch the tax codes over on Jan 1st. If one were to start from scratch now, that would be more annoying.