Cogito ergo sum » mobile

The Three Pillars of Social Reader Relevancy (I)

kenshin03 — Thu, 14 Jul 2011 15:49:47 +0000

In Web Search, the ranking of results is primarily determined by their freshness, relevancy (in regard to a search query) and content quality. Freshness is indisputable and needs little explanation, relevancy is an approximation of how much data a web site contains that have something to do with the user’s query and content quality is an indication of how “good” the site’s information is, given factors like PageRank, spam scores and so on.

Once crossed the line into the mobile word, however, these three factors lose their usefulness drastically. Text input on mobile devices is largely impractical and traditional web pages don’t render well, so media discovery and consumption on mobile devices is generally inferior compared to the same experience from printed mediums like newspapers and magazines. While big name players attempt to tackle the issue simply by snapping on extra features (Google Mobile Voice Search, Google Instant Preview for Mobile.etc.), the underlying problems remain resolved as its ranking algorithm is the same as its desktop counterpart. Flipboard is so great because, in my opinion, it has found and defined the new three pillars of relevancy for mobile content consumption and they are freshness, social, and readability* – and they work wonderfully.

With this in mind, I put some work to the server components of Cassius. From a simple script that turns a Tweet into a JSON feed, the pipeline now includes saving documents into a transitional store (MongoDB) and a series of quality measurement calculations. While the extra processing means we won’t be able to serve the feed in realtime, the cost should be worthwhile and I hope the results justify that.

How well does your article read?

In Zite or Flipboard, it’s not uncommon to run into articles with summary texts that resemble gibberish (see below). The issue is often a result of incorrect identification of raw HTML elements as meaningful content, and is very hard to avoid. I have seen attempts to solve this problem using NLP and machine learning classification methods, to varying degrees of success. Since those are beyond my capabilities, I opted to use some traditional methods to measure the quality of a piece of writing – by taking its readability metrics. From Wikipedia, readability evaluation refers to “the ease in which text can be read and understood“, and “…various factors to measure readability have been used, such as speed of perception, perceptibility at a distance, perceptibility in peripheral vision, visibility, the reflex blink technique, rate of work, eye movements, and fatigue in reading…“. Readability metrics measurement tools are widely available, and embedded in word processors and email clients.

source: corporategeek.info

results of bad scraping

In a nutshell, the tools apply different statistical formulas on a piece of English writing, and the resulting scores form an impression of its understandability. The formulas typically break text into syntactic components such as words and sentences and count their distribution or frequency in relation to the text being analyzed. The most common readability formulas and descriptions are given below:

I found it more pleasing to read blog posts and articles on Flipboard/Zite that are about a page in length. Contents that span multiple pages are too demanding for casual reads, while short tweets or one liners aren’t worth the two clicks effort to expand and shrink them from the page (yes really). For simplicity, let’s take my reading habits as standard, and use the following thresholds for computation:

Flesch – 50 (Times magazine has a score of about 52)
Flesch-Kincaid – 13 (pre-college level)
Gunning Fog – 12 (texts for wide audience have fog index of less than 12)
SMOG - 13 (pre-college level)
Coleman Liau - 13 (pre-college level)
ARI - 13 (pre-college level)

The gist here takes the readability scores, evaluate their distances from the threshold and combine the scores as a mean. Very straightforward.

In the next post we’ll continue to explore the three pillars, and look at some test results to see whether the additional aspect of readability would help us create a feed that is better optimized for the user’s final reading experience.

Knitting a page

kenshin03 — Wed, 22 Jun 2011 10:18:41 +0000

When set out to build the prototype, there’re many things in the design I considered fundamental, chief among them being a template system flexible enough so that no re-installs or updates are necessary if a new page layout combination is desired.

References on the topic is plentiful, but surprisingly the most useful one I came across was a paper published in 1977 titled “Computer Assisted Layout of Newspapers” by the MIT. You can find the full 184 pages here. The paper is a gem to read and goes into detail on even how ads and pictures layouts could be automatically assigned to a theoretical newspaper page. I shall definitely return to it for more inspiration, but so far I have based the design of the prototype on Chapter 6, A Symbolic Graphics Language For News Layout.

The diagram below lifted from P.84 of the paper tells it all. Pages on Flipboard largely employ a rows/columns layout combination, and the powerful template language described in the paper should be able to cover all variations effortlessly .

a simple yet powerful layout language

Note that I cheated a little and defined my version of the template language in JSON, mainly for easier parsing in Objective-C.

Therefore,
P1 || (S1 = S2) is represented with {"columns": [{"type":"P1"}, {"rows":[{"type":"S1"}, {"type":"S2"}]}]} in my app,

and

S3 || (S4=(S5 || S6 || S7)) becomes {"columns":[{"type":"S3"}, { "rows": [{"type":"S3"}, {"columns":[{"type":"S5"},{"type":"S5"}, {"type":"S7"}]}] }]}.

With a structure like this, we could simply parse the JSON into multi-dimensional arrays (e.g. {“P1″, “{S1, S2}”}), then write classes to traverse the array and return suitable UIViews or collections of UIViews. Only two-level nesting is supported in the code right now.

The UIView generation process itself is just as crude at present. While looping through the array, the type of value stored is examined, and if it’s a definition like “P1″ or “TIA”, a helper class would create the corresponding UIView, with arguments being the article itself and attributes like the size of the array passed in for presentation purposes. All these take place in the PageLayoutManager class. A whole lot more work will be put in around these classes.

I’m hoping that more help from the server-side will be used for both the templates definition and articles selection process. Analysis on word count, images in the article, source authority, social signals and other relevancy factors should already been taken into account by the time these articles and templates arrive at the client app.

Finally, here’s the template used for generating the pages shown in the first video. There are four pages altogether, with pages 1 and 4 being row-based and pages 2 and 3 column-based. These layout designs are quite similar to the ones used heavily on tweets-display pages on Flipboard.


{"pages":[

{"rows":[{"type":"TIA"},{"columns":[{"type":"TIA"},{"type":"TIA"}, {"type":"TIA"}]}]}, {"columns":[{"type":"TIA"},{"rows":[{"type":"TIA"},{"type":"TIA"}]}]},

{"columns":[{"type":"TIA"}]},

{"rows":[{"type":"TIA"}, {"type":"TIA"}]}

]}

The rendering:

page 1 - row 1 is article, row 2 three columns of articles.
page 2 - column 1 is article, column 2 is 2 rows of articles.
page 3 - 1 column, 1 article.
page 4 - 2 rows of articles

Remote or Local?

A colleague pointed out the template definitions must be defined and stored on the client app locally, as the app shouldn’t need to fetch a new template from the server when the device changes orientation. I haven’t thought about that yet. To me, it makes more sense to have the server picking templates that are more suited to the content being served. I’m totally not thinking about how to deal with landscape orientations yet.

Extended Reading:

COMPUTER-ASSISTED LAYOUT OF NEWSPAPERS. Reubtures et al.
Optimization of web newspaper layout in real time. J. Gonzalez et al. / Computer Networks 36

Cassius is on github

kenshin03 — Thu, 16 Jun 2011 10:43:16 +0000

Finally got round to putting together a decent enough client! Although the code is pretty rough now and the app would crash after a while, but at least it’s a start innit?!

Repos:

https://github.com/kenshin03/camus

https://github.com/kenshin03/cassius

The article pages were generated by a custom template that’s defined in JSON, and the image on the cover page is grabbed from Instagram’s API:

Cassius 20110616

View more videos from kenshin03

The iconic page flip effects were lifted straight out from AFKPageFlipper. Thanks again Marco!

Replicating Flipboard Part IV – Prelude

kenshin03 — Sat, 11 Jun 2011 17:13:41 +0000

It’s been forever since the last update. But amidst a trillion other things, this Flipboard study never strayed far from my mind (and the IDE).

More detailed posts on the progress and designs of the prototype will come this week, but right now, I can’t wait to share the very first screenshots of ~~Cloneboard~~ Cassius.

What you see here is a cover page using a random image from Instagram transitioning into the first articles page showing stories from my Twitter feed. The layout was generated dynamically from a custom template defined in json.

I’ll be putting all the code up on Github for better back up and version control soon.

JQueryMobile & Prezi

kenshin03 — Sun, 27 Mar 2011 11:35:15 +0000

Amidst the crazily squeezed schedule in the last few weeks, a couple of things fascinated me so much I couldn’t resist from getting my hands dirty.

Putting the two together, this is a Prezi demo of a simple prototype I’ve built with JQuerymobile. The technical stuff will be added in this post later – for the moment, just sit back and be dazzled by pretty pictures flying across the screen!

jquery mobile prototype on Prezi

Replicating Flipboard Part III – How Flipboard lays out content

kenshin03 — Sun, 06 Mar 2011 11:47:18 +0000

Shifting the focus back to the iPad app, let’s take a look at how Flipboard processes and lays out Facebook and Twitter feeds. Does it really employ any social signal based ranking?

Here’s a sample of some Twitter feeds I receive, shown in a browser and in Flipboard:

Sample Twitter Feed

Same group of Twitter feeds viewed in Flipboard

Alright…what can be deduced from this? A few things became obvious when the same data is shown side-by-side:

Ranking of the Twitter articles in Flipboard more or less retains the original sort-by-time order! Don’t think there’re any clever hidden social ranking at play here.
Some Tweets were dropped by Flipboard, presumably because they do not contain links. Examples like (#3, #11, #15, #17, #18) are simply Twitter conversations.
Tweets that have links to sites with Images (e.g. #7, #9) seemed to be given higher display priority.

What about Facebook? The typical Facebook feed is a lot more complex, with mixed content ranging from Check-ins, uploaded lmages, to Likes and notifications from whatever apps/games a user has added. The prospect of having to analyze so many different types of content itself is daunting enough, let alone the additional efforts of re-ranking and laying them out in an app.

As above, this is a snapshot of my Facebook News feed (names, faces and updates blurred out – don’t worry people!) in a browser and in Flipboard:

facebook news feed

facebook in Flipboard

Immediately we are striked with much more intriguing findings:

Flipboard completely ditched the published time of the feed articles and laid them out entirely based on readability attributes (text length, image size.etc.).
Quite a few Facebook articles were dropped:
- Article #5 – a Facebook Places-Checkin: Due to lack of images?
- Article #6 – An Image uploaded from iPhone. Not sure why it wasn’t shown in Flipboard.
- Articles #7, #13 – These were “xxx started using xxx app” messages, which contain no links or images and frankly, nothing interesting.
- Articles #20, #21 – These were messages from Groupon. As article #14 was also a Groupon article, I guess #20 and #21 were dropped as Flipboard prevents showing too many articles from the same source all at once.

And while looking at Facebook articles, we might as well take a quick look again if Facebook-Likes contribute to the layout or ranking at all. The verdict – NOPE. If social signals play a major role in ranking, then #12 and #19 should have higher placements in Page 1 or 2 at least.

#1 – 1 reply
#2 – 1 Like
#3 – 0 Likes (but 82 youtube Likes)
#8 – 0 Likes
#4, #9 – 0 Likes
#11 – 4 Likes, 7 Comments
#12 – 1 Reply, 92 Likes
#14 – 0 Likes
#16 – 3 Comments
#19 – 34 Likes
#17 – 4 Likes
#18 – 2 Likes
#22 – 2 Likes

From such observations, I suspect Flipboard conducts workflows that roughly compose of the steps below:

Likely processing workflow for Facebook feeds

Likely processing workflow for Twitter feeds

Initially, I found it hard to accept this interpretation of templates-over-content. It is basically saying there’s no magic, that Flipboard merely puts together a random collection of page templates then proceeds to filling those templates with the next most suitable article from a content feed.

Putting an end to this suspicion, I switched on Flipboard after a few more articles emerged in my Facebook feed. Given a largely unchanged set of data, if Flipboard employs a content-centric ranking criteria, then the layout of the pages should remain more or less the same? From the screenshots below, this was clearly not the case. Notice how the same data was laid out much more differently:

Original render in Flipboard

Second render of the same Facebook feed

So that concludes the brief study on Flipboard’s layout algorithm. From this point on I shall start firing up the IDEs and getting my hands dirty on building that prototype. Pretty thrilled by the amount of attention and support this little project seems to be garnering – thanks and please stay tuned!

Previous Post

Related:

how are the username and password used/stored for Google Reader?

Replicating Flipboard Part II – Social Signals

kenshin03 — Mon, 28 Feb 2011 10:32:22 +0000

Image: http://www.flickr.com/photos/19marksdesign/

A quick update on the project.

Most people never stray far from Flipboard’s default sections (e.g. Tech, Gaming, Fashion) which feature collections of articles from sources hand-picked by the Flipboard editorial team. Pay closer attention though, and you would notice that the laying out of those articles in the Flipboard grids were not simply based on published time or source. Length of the headline, size of the embedded image, similarity of the topics – all these factors seem to come into consideration. Then of course the social strength (tweet count, retweet count, Facebook likes.etc.) of a particular article is one major relevancy factor too. In my opinion, the adoption of a brand new ranking paradigm – social strength, was the single most ground-breaking thinking that was in Flipboard’s design, not unlike how Google invented PageRank and changed the game on search.

This post is not about Flipboard’s layout algorithm (I hope I could do one someday), but rather a quick detour into social signals scraping. The more social signal information we could gather about any article, the easier it would be for us to rank them in terms of interestingness.

After a few hours of fiddling (wasted countless moments not realizing twitter has removed basic authentication support), the two pieces of information I was looking for became accessible.

Twitter Shares

This couldn’t be simpler. Just do a curl to the Count API:

curl "http://urls.api.twitter.com/1/urls/count.json?url=web_site_url"

e.g. curl "http://urls.api.twitter.com/1/urls/count.json?url=http://edition.cnn.com/2011/WORLD/europe/02/10/index.html"

which returns

{"count":4,"url":"http://edition.cnn.com/2011/WORLD/europe/02/10/egypt.protests.london/index.html/"}

Although there’re some limitations. Most notably is how it doesn’t follow redirects and treats different addresses of the same web page as totally separate:

/* all have different results */
curl "http://urls.api.twitter.com/1/urls/count.json?url=http://www.facebook.com/BufSabres/posts/10150099820062954"

curl "http://urls.api.twitter.com/1/urls/count.json?url=http://fb.me/R97GMpug"

curl "http://www.facebook.com/BufSabres/posts/10150099820062954?somefakeparam"

We could definitely use some pre-processing before calling the Count API.

Facebook Likes

Then there’s the beast of Facebook-Likes. Poised as an innocent function for simply declaring your interest on any entity (a web page, a person, an event.etc.), Likes is a tour de force from Facebook’s arsenal for pushing forward their Social Graph vision. When a Like button is added to any web page, that page automatically becomes a living entity in Facebook’s Open Graph repository. In other words, if there’s a Like button added on your web site, Facebook would be able to analyze what type of entity your site represents (a blog, a movie, a sport team.etc.) and whom expressed interest on it. Now whether that ambition of categorizing the entire web and building social graphs around them is a move forward towards a semantic web or a potentially huge exploit of privacy is for another discussion.

As of now, Facebook’s API does not have a convenient way of returning the Likes count, so as usual, one has to do it via scrapping. The official documentation points to the “Facebook URL Linter” for obtaining the Social Graph info stored by Facebook on any web site. On the URL Linter page, the unique Social Graph ID for a web site would be shown. We could grab that ID and then invoke the Graph API to obtain the Likes count.

e.g. The Matrix page on RottenTomatoes has a Social Graph ID of 119655798047100, and from “http://graph.facebook.com/?id=119655798047100″ we know it’s been liked on Facebook 571 times.

{
   "id": "119655798047100",
   "name": "The Matrix",
   "picture": "http://profile.ak.fbcdn.net/hprofile-ak-snc4/162036_119655798047100_2294421_s.jpg",
   "link": "http://www.rottentomatoes.com/m/matrix/",
   "category": "Movie",
   "website": "http://www.rottentomatoes.com/m/matrix/",
   "description": "Average Rating: 7.4/10 \t\t\tReviews Counted: 126 \t\t\tFresh: 109 | Rotten: 17",
   "likes": 571
}

Except that for reasons unknown, the Linter does not always return the Social Graph ID, like “http://developers.facebook.com/tools/lint/?url=http%3A%2F%2Fwww.oscars.com”, “http://developers.facebook.com/tools/lint/?url=http%3A%2F%2Fwww.apple.com%2F” and many others.

For that matter, perhaps it is perhaps easier for us to bypass the Graph API altogether and directly scrape the iframe that site owners use to add the Like buttons.

All that’s required is a curl followed by some ugly regex filtering code on the returned markup from line 22:









Like




 
You like The Matrix.
Unlike

Admin Page
Insights

Error
You and 611 others like this.
611 likes.
Sign Up to see what your friends like.

Unlike

Admin Page
Insights

Error

So here we are, able to extract Twitter Shares and Facebook Likes for any web site. To complete the test, I rounded up a handful of random web sites from Delicious.com and re-ranked them based on social strength. The result was quite clear in terms of showing what kind of web sites might be more appealing to the masses.

Reranking with Social Strengths

Random sites from Delicious, with their social strength scores extracted and normaled

Before and After

A bunch of latest sites from Delicious

Same group of sites, ranked by their Social Strength

Previous:
Part 1: Site Scraping

Ideas scrap pad: Social Empowered Mobile Search

kenshin03 — Fri, 29 Oct 2010 04:24:48 +0000

A piece of writing from earlier this year (1st April). Still believe there’s a lot of room for innovation in this area and want to keep it at the top of the pile.

screenshot from an early prototype combining Facebook images with Search. Some months later, the Image Search team really did launch such a feature.

Background

As consumer awareness and device capabilities have come of age, the trend in Mobile apps have converged towards hybrids of location based services and social networking. The rise of FourSquare? and Gowalla in the mainstream market only added more fuel to the fire.

Though not obvious at first, this trend has the potential to take Mobile Search to a higher stage of evolution. During the past few years, the uptake of search on mobile devices haven’t been as significant as some might have hailed. While new smartphones come pre-loaded with Search appliances and apps, usage rates compare pale to their desktop offerings.

There’s no denying that in general, user experience of mobile search services leave a lot to be desired, especially when the majority of them were designed to be straight ports of the web search experience. The problem is that the user interface of web search, with typically a text box and a listing of textual results, geared towards offering the user an immersive experience, where he or she is given the illusion of scanning through a long list of related web sites. Due to constraints imposed by hardware, network connectivity, and physical convenience, mobile users rarely find themselves needing to engage in such an intense and immersive search session. It is therefore hardly surprising that keyword based mobile web search has failed to catch on – there just isn’t such a great need.

That’s not to say mobile users lack the intent of searching and discovering information. On the contrary, people on the move are perpetually at the forefront of receiving new data (e.g. running into new people, going to new places, new shops being opened in the neighborhood), and very often find it immensely useful to have additional information at their disposal (say, a guidebook) to help them make sense of the new data. While most people would keep a copy of a local businesses directory in their cars, few consider it an appropriate source for dining advices. When it comes to asking for personal suggestions, nothing beats calling a knowledgeable friend.

Feature Ideas

Delivery of information streams relevant to a user’s social network AND context (time, location.etc.). For example:
- Photos uploaded to Facebook by a users’ contact list.
- New contents and links posted by a users’ contact list in Twitter and Facebook.
- Search/Filtering functionality provided.
Supplementary data extracted from social network streams. For example:
- Which one of my friends have come to this restaurant and when? What were their comments?
- “Find me a drinking place nearby where Stephanie and Becks would hang out, but not Vicky?”
- Any special unlocked Foursquare venues nearby?
Forms of input
- De-emphasize the classic search box and listings UI. Use location, direction, time, data mining.etc. to direct the search experience.

Resources

Yahoo’s Facebook Album Search:

Explore Interesting, Personal Photos on Yahoo! Search

A little goes a long way: NSOperations

kenshin03 — Tue, 23 Mar 2010 06:28:02 +0000

While putting together a prototype that pulls a feed of interesting pics from flickr and have them displayed as a carousel on a UIScrollView, I was baffled by the unusual length of time it took for the scrollview to render, even in the simulator. The delay, especially when combined with the use of a UINavigationController, was downright unacceptable.

Check out this vid to get a taste:

It took only one look from a veteran objective-c developer colleague to diagnose the problem – UINavigationController was waiting for a single thread to finish rendering all 60 images before sliding in the ScrollView. “NSInvocationOperation is your answer“, he said.

And indeed it was, the perceived performance sky rocketed ten folds after the process of initializing UIImages was wrapped in an NSOperation. Here’s the difference:

And the code change was as follows:

Basically, all that was required was to move the necessary rendering logic to a separate method, create instances of NSOperations with selectors set to the method, and add those NSOperations into the NSOperationQueue. The Queue would schedule the firing of these operations and there wouldn’t be any hint of sluggishness at the UI. Thread programming 101. Duh.

Extended Reading:

http://developer.apple.com/cocoa/managingconcurrency.html

My thoughts on EFF’s “All Your Apps Are Belong To Apple” rant

kenshin03 — Tue, 16 Mar 2010 12:14:17 +0000

WTEFF?

So I was recently asked of my opinion on EFF’s “All Your Apps Are Belong To Apple” posting which has sparked some discussions for a day or two.

My reactions? Hardly surprised, really. In fact, although the arguments presented appear reasonable, the article has a serious air of deja-vu to it that provokes more doldrum rather than rage upon Apple followers. Every few months, the old statement of “Apple treating its customers unfairly” would get recycled time and again. A few such litigation came to mind immediately:

“Apple should open up the iPod for sync-ing with other music formats.”
“Apple should allow songs purchased from iTunes store to be playable on other mp3 players.”
“Apple should allow Mac OS X to be installed on regular PCs.”
“Apple should allow consumers to jailbreak the iPhone because they paid for the device and could do anything to it as they pleased.”
“Apple should allow people to replace the batteries on their Macbooks/iPhones.”

Funny thing is, these never amount to anything substantial (like a court order for Apple to change its policies). Pundits would complain as much as they could, but every argument raised against the company would almost certainly be silenced by a larger group of supporters in the end. Some dismiss this as mere fanboyism, fused by leagues of blindly loyal geeks prepared to accept any offerings or services that came out of 1st Infinite Loop without ever questioning the fairness and sanity of it all.

When you think about it, this explanation doesn’t really hold. With more than 220 million iPods, 57m iPhones, 12m Macs (2009) sold, the installation base is huge and reaches much further than tech-heads and nerds. Is the majority of people in this large user base simply oblivious to the various restrictions imposed by Cupertino then? Why are people willing to pay a premium in exchange of Apple services or products that are apparently ridded with limitations? Flashy marketing and peer pressure alone doing the magic? Obviously not.

At the end of the day, it’s really down to the outstanding user experience offered by Apple products and services in general. Ever since 1977, when Steve Jobs decided he would hand-polish the plastic cases of Apple ][ machines with sand paper to "make the corners smoother to the touch", the company has been embarking on a never-ending mission of striving to produce "unparalleled user experiences" in everything they do or make. Even when that meant going against the entire computer industry and alienating their loyal user base by imposing strict EULAs or removing features that were popular amongst users.

Cases in point - getting rid of floppy drives in iMacs and 1394 ports on iPods/Macs, killing the iPod mini, purging more than 5000 mild-sex oriented apps from AppStore overnight, suing third-party accessories makers, enforcing non-removable batteries on Macbooks and iPhones, forgoing physical keyboards on iPhones…in other words, imposing a water-tight system and employing draconian measures that fiercely go against anyone or anything daring to stand in its path of evolution.

Openness comes with a price.

Each of these steps created uproars and distress upon introduction, but when the dust has settled, favorable opinions would come sifting through. The controversial change brought about by Apple would become a de-facto standard, and pretty soon, competitors who ridiculed the change initially would begin to jump on the bandwagon and roll out inferior copycat products/services to cash in.

So…back to the point. Does this 30 year old track record render the points EFF highlighted in the iPhone Development Program License Agreement justifiable? That remains to be seen, but knowing that control-freak motif behind the company might make those restrictions more reasonable:

App Store Only: this is the only way Apple can make sure that apps available for the iPhone are reviewed and up to a certain standard, and could be revoked anytime if need be.
Ban on Reverse Engineering: duh. This is common sense right? Apple develops and owns the iPhone OS and its core apps, and need to protect its investment one way or another.
No Tinkering with Any Apple Products: same as the App-Store-only sentiment.
Kill Your App Any Time: this is the only way to ensure crap apps like I’m Rich and porn/malicious apps can be removed at anytime.

Consumers associate everything they see on the iPhone with the Apple brand, so it is of no surprise that the company does everything to protect its decades-old reputation.

Having said that, there always follow the question of whether Apple Inc is “right”, because many believe that the companies in the industry should play a part in “fostering innovation” by “promoting openness and interoperability”. After all, M$ got her share of negative opinions by being a monopolistic goliath that stifle innovation in return for cash. Shouldn’t Apple Inc be subject to the same level of scrutiny?

No matter how rosy you paint a picture, it's no use if we'll still stuck on the other side of the wall.

In my opinion, that can’t be answered by anyone until the legacy of Apple Inc, Microsoft, Google, Yahoo and all other “monopolistic” companies are history and the market has entered a new phase. Consider J2ME (arguably the predecessor of Android) – a standard that was backed by all major software/hardware companies, was totally open but yet was also totally chaotic and totally failed to be grasped by consumers at all. Yes, the openness of J2ME was great for the tech crowd and manufacturers. But the side effect was fragmentation and incoherence. At the end of the day, openness became the very thing that killed the market it wanted to create in the first place, and when the market is gone, so are your promises for innovation. All efforts became futile.

I think the day we should start worrying is the day when it comes to a point Apple Inc became the only choice available, much like how M$ Office and IE stipulated the markets back then. Until then, as a consumer and developer, I’d still be thrilled by what the iPhone platform has to offer.

p.s. Come to think of it, I should have answered this question simply with “Hey, the success of the AppStore meant that I could now throw Hadokens against Blanka on the bus!” Now that’s an argument.

Extended Reading:

http://www.businessweek.com/the_thread/techbeat/archives/2008/03/apples_design_p.html – Apple’s design process
http://www.businessinsider.com/the-case-against-apple-2009-8
http://techcrunch.com/2009/07/27/apple-yanks-the-cord-on-gv-mobile-is-it-trying-to-kill-google-voice-on-the-iphone/
http://www.wired.com/gadgets/wireless/magazine/16-02/ff_iphone?currentPage=all – “The Untold Story: How the iPhone Blew Up the Wireless Industry”
http://www.rollingstone.com/news/story/5939600/steve_jobs_the_rolling_stone_interview
http://thenextweb.com/mobile/2010/03/15/android-market-breaks-250-million-downloads-989-free-apps/