nobullet
Joined 14 years ago
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74
Submissions
69
Member since
Jan 6
Recent submissions
Comment · 10 years ago
Comment · 10 years ago
It is all a lot simpler: power, money and sphere of influence. No one is going to give up power and money in a poor country. That's why Mr. Putin and Company build tons of bullshit about external enemies, preventing folks from blaming the government for existing economical and social problems.
Disclaimer: I'm from a country that is in Eastern Europe and borders with Russia. It also has very close relations with it. Russia affects internal politics and mass media. 80% are brain-washed.
Comment · 10 years ago
By the way, cool usage of markdown.
Take a look at the page source. Amazing!
Comment · 10 years ago
You are right about JAX-RS, WebSockets, JSON-P, JPA, JTA, etc: there are no replacements for these Java EE technologies in client-side technologies.
But I have a feeling that author is judging about whole set of EE technologies by JSRs that loose their popularity at the current time. I've provided explanation for that.
As for Chrome: the lifecycle of the UI in Chrome is different than UI lifecyclein JSF/Portlets. JSF/Portlet builds the DOM in memory, sends the output, GC cleans up after. This happens per request. But I've seen GC utilization in JSF under a big load. It wasn't nice. Also, JSF/Portlets' APIs are mostly synchronous.
Also, browsers are evolving. Client-side technologies are easier to learn. Server-side is harder. That's why I don't believe in major shift back to server-side. Twitter is relatively simple application. This is an exception, I think. There will be cases when server-side rendering is more efficient. But UI will be much simpler (no templating support, for example).
Comment · 10 years ago
As for these JSR's: I have a strong feeling that Enterprise Edition has become a little detached from the real world.
Java SE is fine but the audience and industry is becoming not interested in Java Server Faces and Portlets as society is shifting to client-side technologies to do same stuff with JavaScript, TypeScript, etc. Java SE is enough to work on server providing RESTful interfaces to the outside world.
Regarding JMS: may be it is a variety of Java asynchronous processing/storage tools that don't need EE at all (like Actors, Hazelcast, Hadoop, Redis, etc).
Comment · 10 years ago
The article is still true if you `sed 's/Venezuella/Belarus/'`.
Comment · 10 years ago
Try sending a link... :)
Comment · 10 years ago
That's not a true statement about modern C# and Java.
Comment · 10 years ago
I have a strong feeling that there is a marketing campaign going to promote Go. It is promoted so hard that it became annoying. :(
Comment · 10 years ago
Guys, make /v2/ with namespaces. Global is good, but your clients have to think about uniqueness. Make namespaces for clients, this will help a lot.
Comment · 10 years ago
I would expect something like Go On Rails (no pun intended). That's where history repeats.
Comment · 10 years ago
Is this post Go biased? Why not C++ or Erlang? :) You can do the same with Python
Comment · 10 years ago
Lots of apps in this list are there because they use Sparkle Framework.
It doesn't automatically mean that if an app uses Sparkle it is vulnerable.
Each must be checked for insecure HTTP requests. Sparkle is not the reason of the vulnerabilities. Sparkle clearly states: use HTTPS.
Comment · 11 years ago
'removal branch' is a great approach. Module Flag DD is how I call our approach :). There is no widespread notion for that .
Below is my favorite response by vaibkv:
vaibkv 15 days ago
Here's a tentative plan- 1. Do fully AndrewNg's course from Coursera 2. Do a course called AnalyticsEdge by MIT folks from edx.org. I can't recommend this course highly enough. It's a gem. You will learn practical stuff like RoC curves, and what not. Note that for a few things you will need to google and read on your own as the course might just give you an overview. 3. Keep the book "Elements of Statistical Learning" by Trevor Hastie handy. You will need to refer this book a lot. 4. There is also a course that Professor Hastie runs but I don't know the link for it. I highly recommend it as it gives a very good grounding on things like GBM, which are used a lot in practical scenarios. 5. Pick up twitter/enron emails/product reviews datasets and do sentiment analysis on it. 6. Pick up a lot of documents on some topic and make a program for automatically producing a summary of those documents - first read some papers on it. 7. Don't do Kaggle. It's something you do when you have considerable expertise with ML/AI. 8. Pick up flights data and do prediction for flight delays. Use different algorithms, compare them. 9. Make a recommendation system to recommend books/music/movies (or all). 10. Make a Neural Network to predict moves in a tic-tac-toe game. These are a few things that can get you started. This is vast field but once you've done the above in earnest I think you have a good grounding. Pick a topic that interests you and write a paper on it - it's not such a big deal.