I looked this up recently and was surprised to see that the first of these was posted just few days ago.. (I was expecting this to be at least a few months old)
As per knowYourMeme,
the original instance of the series was uploaded by artist Garnet Hertz via his Facebook page on February 2nd, 2012.
I guess some of the reasons this became an internet rage:
- Realistic and Humorous (Most of the times): Focuses on mundane realities of life (most jobs are not as shiny as they seem to be). while allowing you to laugh at others and self: Self-deprecating humor is usually popular.
- Virality factor: Most people can either relate directly to the post. In other cases, after seeing one of these posts, people think of how this would fit in their jobs. In first case, people would share; in latter, they may go ahead and design one that describes their job (See next point). In both cases, their action contributes to virality.
- Easy to design: Unlike some other internet memes, you don't have to spend too much time drawing images or shooting videos. This even doesn't require too much thinking. Most of the time, you just have to do a think a bit about your job, and then do a quick Google image search for images that would fill the standard placeholders.
You don't need any API or a third party tool for this. All you need to
do is to go to this official Twitter account: "Verified" and see its
"Following" list. This account follows only Verified users.
A minor catch here (though it may not matter for any practical purposes): Since a twitter account can't be it's own follower and the "Verified" account is a verified one, you will need to add one to the number of accounts "Verified" follows to get the actual number of verified accounts.
As of today (Feb 28, 2012), "Verified" follows 18,589 accounts, so there are 18590 verified users.
An excellent resource to begin with is Hal Varian's book: Information Rules.
You can find this on Amazon here:
The book was published in 1998, and hence some chapters may be a bit dated. But although the software landscape may have changed, most basic rules still remain the same. The book discusses the key elements of software pricing: versioning, bundling, switching costs etc in detail.
You can also purchase individual chapters that you are interested in. These are available as HBR case studies. But considering that a used edition is available for less than $5, you may want to buy it from Amazon.
My schedule was about 1-2 hours in the morning and 1-2 hours in the evening during the weekdays and rigorous studies during the weekend. Also, I tried to write one Full-length test every week (Included AWA only in latter half).
One work-plan that has worked for many of my friends can be found at this link:
This isn't my prep-schedule. This includes fixes for the mistakes I made while preparing. I know many of my friends and followers of my blog, who followed this work-plan and got 750+ scores.
Recommendation-engine basically try to emulate what a human, who knows about your tastes will do. It makes recommendations based on what you have seen and liked. To understand why it "fails", let us emulate the recommendation engine itself. Here is a hypothetical conversation between you and a friend..
"I have some free time this weekend.. Recommend some good movie."
"Ok.. What sort of movies do you like?"
"Sci-fi and Fantasy"
"Well.. That's not very specific.. I have a long list of recommendations, but not really sure whether you'd like it. You may try "Back to the Future" to begin with.. An all time favorite for most of my sci-fi loving friends "
<Few days later>
"How did you like "Back to the Future"?
"I didn't like it.. I like to see my Sci-fi unadulterated.. So I don't like movies that try to mix it with comedy"
"Oops! You could have told this before.. Next, I was about to suggest "Men in black", but now I know that you won't like it.. Tell me some Sci-Fi movie that you like"
"And I loved "The Terminator" series! Even the later ones, which not many others like!"
"Now, we are talking some specific stuff! I think you like the Man Versus machine theme.. I would recommend "The Matrix" "
<Few days later>
"I loved "The Matrix" Any more suggestions?"
"Of course! I am sure you will like the sequels. Based on what I know about your tastes and my limited sci-fi movie knowledge, those are the only recommendations I have! "
"Well! I saw both the sequels right after the original one.. "
"Sorry! In that case I don't have any more suggestions.. I know a few Japanese movies on the topic, but I know you don't watch Japanese "
See what happened in the above conversation. Initially, more information you shared allowed your friend to make very specific suggestions.. But as the friend learned more & more about your tastes and distastes, the list gradually got smaller and smaller, and finally your friend was left with no suggestions.
A recommendation engine would not run out of suggestions so soon,and would have many more dimensions, but overall the model would remain the same.
Initially, it will show a long list based on their first guess for your tastes. With specific information, gradually it would be able to make very good recommendations. But gradually, the list will become smaller and smaller, finally to a point that you have seen almost all the movies that the engine would have suggested. So in my opinion, this may not be exactly a failure of the recommendation engine, it has simply run out of the good movies, as per your taste, that you haven't seen.
Adding a new dimension to your movie tastes (A new language maybe, as the example suggested) may perhaps expand the list.
BTW, instead of waiting to see whether a particular movie ends up in your recommended list, try the Netflix's estimated best guess rating for you for that movie. In my case, it has always been very accurate.
My personal take:
Use the retweet button if you want to retweet an unmodified version of the original tweet. This gives full credit to the original poster, as to your followers it will appear with his profile pic and Twitter handle.
However, in case you need to add anything, the old fashioned RT is your friend. This addition can be an endorsement, a factual addition, a correction or anything else. Another use of the old fashioned RT would be: if you want one or more of your followers to make a special note of the original tweet by including the @mentions of their Twitter handles.
As Sten answered above, putting lists is the one way to attain your objective…
There is one more way that can resolve your problem: I am assuming that for the users in question, you may be interested in their tweets on some specific topics, perhaps some common areas of interest. But you are not interested in other topics from the same users.
Solution: Use the followers of your target-users as filters. In other words, instead of following the particular user, selectively follow followers of the target-user, who retweet the content of your choice from the target user.
This may sound confusing, so here is an example to make things clearer.
User X tweets on two topics a lot: web-marketing and Basketball, and you may be interested only in web-marketing.
To solve the problem in the example case above, simply locate the followers of User X, who are passionate about web-marketing. One or more of these users will surely re-tweet good web-marketing content from User X. So you will see whatever X says about web-marketing, without ever having to see a running commentary of a basketball game.
You don't need any external tool.. Simply replace the place-holder in the below link with your actual twitter handle and your total tweet count and you will be able to download an XML of all your tweets. After this, open this in any XML viewer, and you will be able to see the list of all your tweets. You can even use MS Excel to view this in a tabular manner..
https: // twitter. com / statuses/user_timeline/ <Twitter-handle>/ xml?count=<Tweet-count>
/* Remove spaces before using the above link */
You can even use this to download tweets of any another user, whose tweets are public.