It's actually a small but very important victory of Facebook, Google and Twitter. Initially they refused to follow this new law. Then Russia decided not only to postpone the law for it until January 2016 at least but also build this special data center.
And since there are no official comments about it from Facebook, Google and Twitter, they are still fighting. This fight is very important not only for them but for small companies, which will be the main victims of this law in the future.
Still better than in China but worser than before.
Normally if you get an access to the market, to be really successful and legal there, you will open a local office, pay taxes, bring your own money and 3rd party investments, create jobs and so on.
In Russia it will work like this: "Our forthcoming policy brief on the new Russian amendment estimates the losses from this amendment to -0.27% of GDP, equivalent to a loss of 286 billion roubles (US$ 5.7 billion). Russia’s economy is already in severe recession, and the Russian economy is likely to contract by 2-3% this year. Investments in the Russian economy would drop by -1.4% or 187 billion roubles, with considerable effects on employment. The manufacturing sectors are hardest hit, as they must also absorb cost increases from their suppliers in the service industry."[1]
Also when I mentioned "small companies", it was not only about foreign companies, but about Russian companies as well. Many of them use foreign hosting providers and cloud services because the quality is higher.
Imagine that every county in the World will require the same thing. For monsters like Google and Facebook it will be an issue but still solvable issue. For small and middle size business it will cost a lot.
And if we take a look from the Russian Government point of view, if you have a choice to check that Google follows the law or to check that 100 small companies follow the same law. Of course, they will check 100 companies, because it's easier to find something there than losing a lot of money, time and other resources fighting against Google.
I think most of the people always forget most of Math in a few years after getting degree if they're not actually using it on daily basis. That's why, yes, it's not mandatory to be good at math to learn to code.
But what they will never forget, are the skills which they got while learned math: logic, analysis, passion to improve the solution, patience and so on. Because in general the mathematical problems are the same as development problems and to be able to find an optimal solution you need to use the same ways. That's how Math helps.
Some questions:
- What are the benefits of this tool in comparision with Code Sniffer + pre-commit hook?
- Is it possible to convert custom code sniffer standard to .styleci.yml to be able to use both?
- The documentation looks a bit poor https://styleci.readme.io/docs. It would be nice to have more examples of failed tests, auto fixes, etc.
- Is there're any other features apart from coding standards review and auto-fix?
- Does it make a separate commit to repository, when it fixes the code? If yes, how to keep the history of commits clean?
General impression about it is like a silver bullet for the companies which are ready to pay for fixing coding standards instead of teachning developers how to do it.
Sorry, I'm not a person to whom this question was addressed. But in our company we had 3 developers coming from PHP and Python world. They learned basics in 1-2 days and started to commit in production repository. Then in 1-3 weeks they learned it much deeper. But in our case it was easier because we already had high traffic and there were a lot of production examples to learn.
Do they ever have to use an Erlang lib and find the documentation harder because they're Elixir programmers?
I love the Elixir syntax but worry it'd be like Scala: a better way for people who already know Java, rather than a way to use BEAM / OTP etc for people used to modern languages.
Most of my time I write Python, but I'm trying to spend more time with Elixir, and I haven't found using Erlang libs that hard.
There are a handful of rules to remember, if that, and it's reasonably straight-forward. module:func becomes :module.func, the erlang func probably wants char lists rather than strings, atoms are lower-case and should be changed to :atom, vars are upper case and should probably be lowered.
Learning the Elixir language is easy, thinking functionally less so (for me at least).
I don't remember that it was harder actually. We were using Mochiweb (was replaced by Cowboy at some point in time), Chicago Boss and some other additional Erlang libs and people switched from it to Elixir and other way around.
Agree with rossj, there're some rules to remember and then it's not that hard.
Great idea, Erlang is very good for such tasks. Native communication between nodes, very high availability and fault tolerance (native as well) using "actor model"[1] and good performance. As an example, in one of my previous companies we built a message broker, which was able to keep 35K keep-alive connections on Amazon Medium EC2 instance and there still were reserves in CPU and Memory.
Plus it's easier to learn it than it seems first time when you see it. Most of our Erlang-developers came from other languages, but they learned basics very quick: 1-2 days until the first commit on production.
The only one thing, it's not so easy to debug it in the beginning. But it's a question of practice, I think.
Interesting game, but I think, practically it's not as useful for memory training as, for example, chess. Because in case of chess, you're not only storing fixed combinations (debuts especially), but training your brain using and improving it.
According to moonwalking with Einstein, no, it doesn't. And the author makes a pretty good argument about how chess players can easily remember the placement of multiple chess pieces on a board - as long as they are all in legal positions.
Show a chess player a board full of randomly placed pieces and they'll have great trouble remembering the placements. So if their memory doesn't even transfer to chess pieces, how can we expect it to transfer to other domains?
I think there're no "yes" or "no". It depends on many factors. You're talking about "Moonwalking with Einstein", but from other point of view there's a book "How life imitates chess" by Garry Kasparov, which I recommended a couple of comments below. For me, this source is reliable.
"Show a chess player a board full of randomly placed pieces and they'll have great trouble remembering the placements" - how about "Chess960"? It uses almost random positions in the beginning, which of course doesn't allow you to use already known debuts. But if we check the list of world champions, there're the same Top grossmeisters: Aronyan, Svidler, Nakamura.
I think we will have a very good practical answer in a few years. Because in 2011 the Ministry of Education of Armenia started an educational program about teaching chess in schools. This sounds very interesting and soon we will know if it helps to improve something or not.
Of course. It improves not only memory, but also logic, prudence, attention, etc. And you can easily use the same skills in real life. Let's take a situation when you're trying to convince an investor to invest in your project. And now imagine that you're just playing chess against him. At the beginning he has more power, this means he's playing white. And you're playing black. To be able to win (to get money in this case) you should think at least 3-4 steps forward, trying to take an initiative and wait for his mistake. Perhaps at some point you will sacrifice or exchange something to get benefit in the future. And so on. And chess can teach you how to do it.
But another very good example is a book of Garry Kasparov "How life imitates chess", where he explains how it can be transparently connected with each other.
In my opinion chess can be used as a model of real life and if someone wants to learn something useful from it, he can get it.
I read, just recently, that they gave novices and expert players chess positions to remember. The experts remembered real positions better. There was no difference in recall between the experts and the novices when they weren't real positions. This suggests that the ability for recall is highly contextual.
