Location-aware mobile web apps using Google Maps v3 + geolocation

When hiring Engineers, I always look for evidence of pet projects, so recently I thought it was fair to create one of my own: GeoMeme, the fun way to measure and share real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that ‘love’ beats ‘hate’ in Manhattan:

GeoMeme is a desktop web application and also a location-aware mobile web app for iPhone and Android phones.

Implementing the mobile version of GeoMeme as a web app has some advantages and disadvantages, compared to building native iPhone &/or Android applications.

Native apps are great because they currently offer the deepest integration to the full capability of the phone, for example using device APIs to access Contacts, the Camera Roll, an Accelerometer, or the GPS chip. For some applications, this deep device integration is essential and so a native application is beneficial.

On the other hand, emerging HTML5-based mobile browsers are aiming to standardise integration to such device APIs, starting with Geolocation APIs; meaning that location-aware mobile web apps are now becoming viable. Aligned with this development is the new version of the Google Maps API. v3 has been greatly simplified since v2, and is now optimized for use on mobile phones. Less is more.

The deciding factor for me choosing to build a mobile web app for GeoMeme rather than a native app was development speed. A mobile web app enjoys far greater code re-use from the desktop web version, and it is possible to push regular updates and improvements to users, without having to wait for appstore approval or for users to upgrade.

I believe this need for development speed is common among a good proportion of mobile apps that are still in ‘rapid iteration’ or ‘release early, release often’ mode, so this post is intended to share some of the techniques used in GeoMeme with developers wanting to build their own location-aware mobile web apps.

Let’s build an example location-aware mobile web app called ‘Here I Am!’, for the photographically challenged. The app will present some local photographs (from Panoramio) which can be shared with friends on Twitter or Facebook.

Where on earth is that mobile phone..?

The first job of a location-aware mobile app is to work out where on earth the mobile phone currently is. Unfortunately, at the time of writing, there is still no universally reliable and accurate solution for a mobile web app to detect the location of the mobile phone it is running on. However the following partial solutions can be combined to good effect:

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Scalable, fast, accurate geo apps using Google App Engine + geohash + faultline correction

GeoMeme is a web app (and also a mobile web app for iPhone and Android) that I recently developed as a pet project. It measures real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that :) beats :( in San Francisco:

A large amount of geo-data is generated by GeoMeme, and so arises a need shared by many geo apps: scalable, fast, and accurate spatial queries, used to select a subset of geo-data for display as markers on a map, or on Google Earth.

:)Google App Engine

Google App Engine is an obvious choice for hosting your geo app. The App Engine datastore is built on top of Google’s BigTable technology which scales very well, and is optimized for fast data retrieval. And it doesn’t cost the earth like some traditional GIS database solutions.

:( Inequality constraint

If you are coming from a background of relational databases, you might think the solution here would be to store the latitude and longitude of all your markers in a database table, and do a simple query to retrieve only those contained within the bounds of the map.

However, the flipside of being optimized for fast data retrieval is that BigTable only allows inequality filters on a single dimension, to avoid the burden of full table scans. For example, the following form of spatial query is not supported because it specifies inequality filters on both latitude and longitude dimensions:

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Fast map re-location using Google Static Maps v2 + geocoder

GeoMeme is a pet project of mine. It’s a web app, and also a mobile web app for iPhone and Android, that measures real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that ‘District 9′ beats ‘Inglorious Basterds’ in Manhattan.

As well as offering users the normal pan and zoom controls to move the map around, GeoMeme also introduces an innovative geo-autocomplete control which is powered by the geocoder service from Google Maps v3 API and the new Static Maps v2 API.

This blog post shares some details of how the geo-autocomplete control works, and offers some code so you can build your own geo-autocomplete controls.

1. Based on a partial location typed by the user, obtain a list of possible matching locations:

If the user has already typed ‘San’ into a form field, we can obtain a list of possible matching locations by passing this partial location to the geocoder service from Google Maps v3 API, as follows:

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Xumii acquired by Myriad Group

Exciting news: Xumii has been acquired by Myriad Group (SIX:MYRN). We are now part of Europe’s largest mobile technology business with software in more than 2 billion phones.

This is great for Xumii as it means we can take our mobile social networking platform to the next level. And it’s a great win for Australian mobile technology.

Here’s the press release and some of the blogosphere coverage: GigaOM and TechCrunch and VentureBeat.

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GeoMeme: measure and share real-time local twitter trends

I am pleased to announce the launch of GeoMeme, the fun way to measure and share real-time local twitter trends.

I got thinking about this when a recent Los Angeles earthquake was being measured in tweets per second rather than using the Richter Scale.

Then came the Magnitwude Calculator as a standard way to measure the magnitude of Twitter trends.

[Then came twotspot.com but that domain name was just too damn rude, so it was quickly renamed to GeoMeme.]

What does GeoMeme do?

GeoMeme measures real-time local twitter trends.

Tweeps are located on the map using public data from a number of iPhone twitter apps. When twitter launches its geolocation API, that will be used to locate even more people on the map.

GeoMeme measures and compares how many people on the map are tweeting about each of your two search terms:

The ‘magnitude’ of each search term is equal to the number of unique people tweeting per hour per square kilometer, so it increases when more people are tweeting in a smaller area.

Example: if 100 different people in an area of 10km2 have tweeted about ‘love’ in the last 2 hours, the magnitude is 5.0 (100 divided by 10 divided by 2).

So you can search for ‘love’ and ‘hate’ and GeoMeme works out which one “beats” the other with the higher magnitude.

The default search terms are :) and :( smiley faces which provides a good measure of local happiness, as an example.

Can I use my iPhone?

Sure, or your iPod Touch. Here’s the screenshot:

Give me an example!

Thanks to some early coverage on The Register, Mashable, and Google Maps Mania, and winning Mashup of the Day on ProgrammableWeb, we’re off to a flying start. I’m glad GeoMeme is hosted on Google App Engine for scalability.

Here’s a selection of the most popular GeoMemes so far:

How does it all work?

I will leave the details of how it all works to another post, stay tuned for that.

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How to measure Twitter trending topics

2009 has already seen some big Twitter moments, including Michael Jackson’s death and memorial service, #iranelection, Oprah’s mainstreaming, and the race between @aplusk and @cnn to reach 1 million followers.

But how can we objectively measure and compare the scale of such things?

A little while ago I got thinking about this when a Los Angeles earthquake was being measured in tweets per second rather than using the Richter Scale.

And now here is my solution, the Magnitwude Calculator, which measures the current magnitude of tweets on any topic within any location.

Please have a fiddle. Type in a search term or select from the autocomplete list of currently trending topics, move the map around, and tell me what you think:

You can link directly to the Magnitwude Calculator at http://hitching.net/magnitwude

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Wave goodbye to spam

Google Wave combines the best of email, instant messaging and real-time collaborative editing into a new form of online communication.

The email paradigm of ‘send and receive’ is replaced with a model of hosted conversations, in which “people can communicate and work together with richly formatted text, photos, videos, maps, and more.”

Wave is refreshingly ambitious. In years to come, I hope we will be waving nostalgically about email as “something that my parents used to do.”

This blog post describes an idea built upon Google Wave that could also turn email *spam* into the stuff of nostalgia.

Spam sent by people you don’t know is a real pain in the inbox. But simply ignoring emails from people you don’t know is not the answer. (Otherwise I would never have learnt about my recent win on the Nigerian lottery. Just kidding.)

So how might Google Wave help us to finally wave goodbye to spam?

  • assume that developers will build robots to connect my wave account with the rest of my social graph (either that &/or Google plugs in Friend Connect)
  • if someone (or a spambot) outside of my social graph invites me to a wave, my wave server responds to that invite with a reCAPTCHA challenge (try one out below)
  • Continue reading

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Mobile Social Technology and Alternate Reality Gaming (ARG)

Today I spent an enjoyable couple of hours at the Australian Film Television and Radio School (AFTRS), learning about Multi Platform Content, and talking about Mobile Social Technology & Alternate Reality Gaming (ARG).

We examined some emerging mobile social technologies, and how they can enable new forms of story-telling. And we shared my personal journey into a Star Trek Alternate Reality Game which has so far involved me sending pictures of sheep to strangers in Paris, and which explains my recent cryptic Twitter and Facebook status updates. Well some of them anyway.

The slide deck is embedded below, and contains all the links for those of you who asked.

[Update 3 June 2009] OMG! I was chosen as one of the five finalists in the game. Here’s a video of Leonard Nimoy putting my name into the hat to pick the winner.

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10 cloud datasets that I’d like to mashup

Cloud computing is being sold as a hosting architecture to provide instantly scalable on-demand computing power, storage and bandwidth.

“The cloud’s resources scale with user demands. Pay only for what you use” says RackSpace, the latest to join the cloud gang.

One problem for the cloud gang, however, is that hosting has always struggled as a low margin commodity business.

Rackspace has just hired Robert Scoble to help spread the message, so we should expect this space to soon get hotter than an Sun SPARC with a loose heatsink.

But where exactly can some value be added in cloud computing, to increase the margins and keep Scoble funded so he can continue to filter the signal from the noise on FriendFeed? Okay, that’s slightly selfish but it’s an interesting question.

The interesting answer IMHO is cloud datasets.

Having useful datasets available in the cloud will unlock value from the data by allowing a new generation of mashup. These aren’t mashups that simply use data from remote web services, like plotting Craigslist ads onto a Google Map. This involves the mashup (joining) of datasets in the cloud using the power and speed of a relational database.

This cloud database approach might also provide Twitter and other owners of valuable data with a revenue model that doesn’t depend on advertising.

Here’s 10 cloud datasets that I’d personally like to mashup, to help explain:

1. Wikipedia. Funnily enough Amazon Web Services has just announced that it now offers a 66Gb dataset of Wikipedia. “The wiki markup for each article is transformed into machine-readable XML, and common relational features such as templates, infoboxes, categories, article sections, and redirects are extracted in tabular form.” One example: imagine the opportunities for a start-up social travel site to mashup its content with the wealth of travel information now available on Wikipedia. Massive.

2. Geonames. It bugs me that everyone who wants to use the geonames database needs to duplicate 800Mb of data. Move it into the cloud! Example: the travel site can now analyse reams of user-generated content (or Wikipedia content) for up-to-date categorization and geo-coding onto a map. Another example: most websites need a simple (but updated-more-often-than-you-would-think) list of countries on the rego form. Wouldn’t it be good if everyone used the same (geonames) list?

3. MaxMind IP address lookup. Turn an IP address into an always accurate city location. Example: targeted ad serving and traffic analysis.

4. Google PageRank. For any URL, what’s the PageRank measure of quality? If this is relational data (rather than from a remote web service), it can be combined with other measures of quality at database speeds.

5. Real-time stock market data.

6. Real-time sports data.

7. Dodgy credit card numbers.

8. Dodgy email addresses.

9. Twitter. Some of the above might be considered proprietary rather than public data, which brings me to Twitter and a potential revenue model for them and the cloud gang. If you’ve got valuable proprietary data like Twitter has got (some would say that’s all they’ve got), then replicating it into a relational cloud database will unlock more value than could ever be extracted (or sold) via a remote web API.

Example: when visiting an e-commerce site, it would be nice to see only the product reviews submitted by people I am following on Twitter, sequenced by a measure of quality based on how often those people have been retweeted. Of course, the cloud gang already have the billing infrastructure and monitoring in place to work out exactly how much proprietary data you have used, and what to charge you for it. Did I mention yet that Jeff Bezos is an investor in Twitter?

The advertising pie is not big enough to fund the whole of the interweb, so perhaps paid data consumption is the revenue model for Twitter and others. Businesses are happy to pay hosting providers for commodity services like CPU cycles and disk space, so why not pay Twitter (via a hosting provider) for valuable information? Did I mention yet that Jeff Bezos is an investor in Twitter?

10. This one is further out there; private foreign keys. Imagine the Twitter dataset including the email address of users, joined using that email address to a Facebook or Digg dataset, but not revealing that email address in the result set. That’s number 10 on my list. It would need to work in a similar way to Facebook’s FQL or Yahoo’s YQL or Google’s GQL, to expose enough information to be useful but to not expose anything that would violate privacy concerns. I hope to write some more about this and the privacy implications in another post.

So, who’s in the cloud gang? Google is well placed with AppEngine and plenty of valuable datasets to get started with. Amazon has all the billing machinery in place to sell proprietary data from Twitter and others. Sun now has MySQL which already supports remote replication and column-level permissions to enforce private foreign keys. And now RackSpace has Robert Scoble. This will be an interesting one.

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10 ways to combine your blog with your micro-blogging

Your micro-blogging on Twitter or FriendFeed is topical.

Your blog is quality.

Both are valuable. How can you combine the two?

1. Display your FriendFeed content on your blog using an embeddable widget:

2. Display your latest Twitter updates on your blog with a customized widget:

3. Combine the display of RSS feeds from FriendFeed and Twitter and elsewhere using an RSS plugin on your WordPress, Blogger, Moveable Type or TypePad blog.

4. Install Fresh From FriendFeed and Twitter – a WordPress plugin that keeps your blog always fresh by regularly adding your best recent content from FriendFeed or Twitter. Unlike the above solutions that only display content, Fresh From allows your visitors to search your micro-blogging content, and allows you to easily edit, tag and turn it into regular blog posts. Disclosure: I wrote this plugin, it got me thinking about this post.

5. Going the other way, FriendFeed makes it easy to import your blog’s RSS feed into FriendFeed.

6. Make sure your blog’s feed is using Media RSS extensions if you can, so FriendFeed picks up any media attachments. There are a couple of WordPress plugins available that achieve this.

7. You can import your blog’s RSS feed into Twitter using services such as twitterfeed and RSS To Twitter:

8. Alex King’s Twitter Tools is a WordPress plugin that creates a tweet on Twitter whenever you post in your blog, with a link to the blog post. It can also create a daily or weekly digest post of your tweets on your blog.

9. Glenn Slaven’s FriendFeed Comments WordPress plugin will take the comments & ‘likes’ on your posts from FriendFeed and place them on the post that they’re related to on your blog.

10. If you are using the Disqus comment system on your WordPress, Blogger, Moveable Type or TypePad blog, comments can now be synchronised between your blog and FriendFeed.

What have I missed out? Comments please!

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What’s the difference between user generated content and user generated rubbish? Comments please…

Some user generated content (UGC) is genuine, honest, credible, reputable, trustworthy, valuable, quality information. But some is rubbish (let’s call that UGR), including deliberately misleading propaganda, biased blog comments, bogus product reviews, spam, veiled advertising, and bad poetry (or is it just my blog that attracts poetry bots?)

Google’s PageRank algorithm does a good job of measuring the quality of a simple web page, based on the number of incoming links to that page, and recursively weighted on the quality of those linking pages. However, web2.0 has given us blogs, wikis, forums, media sharing, customer product reviews and ratings, social bookmarking, and more recently aggregation of all of the above; resulting in web pages that contain an increasingly complex array of UGC and UGR, making it increasingly difficult for algorithms, and site visitors and site owners to filter the signal from the noise, the UGC from the UGR.

So I wanted to write a post about some of the emerging technology innovations attempting to solve this problem. Readers are kindly asked to add a comment at the bottom of the post. All comments will be shown, even bad poetry, for purposes of research and experimentation.

Measuring quality is relatively easy for eBay. Its Feedback Ratings provide an excellent indicator of trustworthiness, because online auctions involve measurable user actions such as ‘Was the product description accurate?’ and ‘Did the buyer pay up?’ Such actions speak louder than the mere words of a blog comment or product review.

Amazon now owns a valuable database of customer product reviews to help people through their purchasing decisions. Innovation by Amazon in this area has included the ability to provide feedback on the usefulness of other users’ comments, and a Reviewer Rank algorithm which provides a measure of reviewer quality (interestingly, this algorithm was recently improved to include some PageRank-like recursiveness).

In a past life I had the pleasure of working for Lonely Planet, a travel publisher whose credibility and quality has been built upon the independence of its authors and their unbiased travel reviews. Lonely Planet and its peers have long struggled with the opportunity to harvest UGC from loyal and passionate travelers, because it is just so difficult to measure the independence and quality of contributing users.

TripAdvisor was allowed to emerge as a disruptive force in the market for travel advice, allowing anybody to review any hotel or restaurant. That created a lot of quality content for a while, but ever since hotel owners found out about TripAdvisor and began to review their own hotels, it’s been difficult to tell the UGC and UGR apart. TripAdvisor still desperately needs a reliable measure of user generated quality to restore its credibility.

Perhaps social networking can help TripAdvisor; being able to filter your travel advice to that written only by your friends would eliminate biased reviews (unless you are friends with a bunch of hotel owners, in which case you’re probably going to stay in their hotel anyway). But until the internet settles on a standard for social data portability, not many of us will have enough online friends who have traveled enough and generated enough online travel content for such a social filter to work reliably, even allowing for recursive algorithms.

If it’s just travel advice and inspiration you’re looking for, you could wait for Lonely Planet’s upcoming blog syndication feature, which promises a novel solution to the problem.

But more generally, I think we all need a universal reputation system, one which aggregates lots of measures of quality from lots of different sites. Imagine if you could easily see a summary of my quality metrics from eBay and Amazon and Yahoo Answers and LinkedIn Answers and GetSatisfaction, perhaps even my Bugzilla and Basecamp metrics too; would that be enough for you to trust my travel advice and any other content that I generate?

Site visitors would benefit from increased visibility of users who generate content. Genuine contributors would be encouraged by being able to build a universal reputation for quality UGC, and discouraged from the risk of creating UGR. And site owners would benefit from data to filter out the UGC from the UGR.

A universal reputation system could also help to eliminate online vote rigging, astro-turfing (all those reviews of iPhone apps posted by the developers themselves), and space-faking (setting up false identities on social networking sites).

Who are the players?

SezWho SezWho provides a plugin for blog commentary which presents a useful summary of UGC history for each contributor, and allows customizable 5-point rating scales for site owners.
Intense Debate Intense Debate has a great interface design. It’s recently been acquired by Automattic, the owners of the WordPress blogging platform, which will provide some valuable distribution, perhaps critical mass. But will the other blogging platforms want to adopt or integrate with a standard controlled by a competitor?
Google Friend Connect Google Friend Connect allows any site to embed a comments or ratings gadget onto any page. The universal view of previous UGC is not there yet, however this will become powerful when integrated fully with Google’s other stuff; Blogger and SearchWiki and the Social Graph API and YouTube (arguably the site most in need of a UGR filter!)
Disqus Disqus is getting lots of press for its prompt Facebook Connect integration which takes the hassle out of commenting. Video comments can by posted, powered by Seesmic. Readers can nudge comments up and down the list by voting on them. Try it out below.

If you have a view on who will win the race to become the universal reputation system, please comment below. Are there any other players that I have missed out? (Yes I know that is exposing me to some comments on the quality of this post!)

Also here’s some further questions to inspire some commentary:

  • Should we settle on a word for what is being measured here? Quality, importance, value, trust, reputation, credibility, honesty, transparency? Or will the winner of the race provide a web2.0 brand name to describe this concept of a universal measure of user generated content?
  • Is it even possible to determine an objective universal score? The success of PageRank would suggest yes. Or is quality in the eye of the beholder? Is one person’s signal another person’s noise?
  • Would a universal metric destroy the democratic level playing field that is UGC / UGR?
  • What are the consequences of such a universal reputation system being gamed?
  • How likely are eBay and Amazon to open up their reputation data? What are the privacy implications?

Thoughts please. Don’t be shy!

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Social data portability: who benefits?

In 2006, a certain old-media tycoon reportedly asked Mark Zuckerberg, the 20-something founder of Facebook, “how can I build a social network like Facebook?”

Zuckerberg replied “You can’t!”

What Zuckerberg meant was that Facebook hadn’t set out to ‘build’ a social network. His billion dollar insight was that Facebook would instead provide online social tools to help existing friends and existing social groups to communicate easily, share photos, stalk, and poke each other.

Then in 2007, Facebook opened its app platform for third party developers to add additional social stuff to keep users on the site. Soon we were all happily throwing sheep at each other and spamming our friends with app invites.

App fatigue arrived in 2008. A redesign of the Facebook site removed some of the weeds, but the metrics spoke loudly, or rather their unit of measurement did; popular apps began to be listed according to ‘monthly active users’ rather than ‘daily active users’.

Slide, RockYou and iLike had been quick enough to make some money, however there was a long tail of apps without enough active users to generate a decent return on investment. The app gold rush was over.

It become apparent that there was less value in creating new social activities inside of a social site such as Facebook, and more value in socializing, or adding social data and context to, the existing sites that people are already using out there in the big wide web.

In other words, social data portability has arrived, and extends Zuckerberg’s earlier “You can’t!” insight; you can’t ‘build’ the platform because the web is the platform.

We are told that data portability is for people who want more control over their data and do not want to be locked in to any particular social network. In 2008, Facebook Connect and Google Friend Connect and MySpaceID have emerged as the big solutions from those wanting to port your social data, and profitably.

Facebook makes money from people viewing and clicking on ads on their website. Facebook Connect therefore allows you to export your Facebook profile and friend list to external sites, but really is intended to increase activity back on the Facebook website, by importing social information from those connected external sites back into your Facebook Feed for your friends to see. MySpaceID ditto.

Google however makes money from people clicking on ads anywhere, so Google Friend Connect can afford to remain socially agnostic, allowing users to identify themselves and their friends according to any network they belong to, and feed their external site activity into the social sites of their choice.

Being socially agnostic is more useful to more users in theory, but not yet in practice for Google Friend Connect. Even though it would be technically simple for Google to access your profile and friend lists using the Facebook Platform, what happened when Google submitted its Friend Connect app to Facebook for approval earlier in 2008?

Zuckerberg replied “You can’t!”, then added some fud about privacy.

This week however Google was able to make some progress on the theory of Friend Connect by launching an integration with Twitter. It’s now possible for you to use your Twitter identity and friends list on external sites powered by Friend Connect, which significantly increases the chances of spotting someone you know on those sites.

What’s interesting about this recent development to me is the apparent haste, including Google asking for my Twitter username and password directly, rather than waiting for Twitter to complete its long-awaited OAuth implementation. I’ve also seen more than the usual number of server errors and teething problems in this latest build of Friend Connect.

Maybe this is an indication that OAuth will be coming soon from Twitter, which would be fantastic.

Or maybe this is an indication that Twitter will be coming soon from Google; some visibility into Twitter data would be useful for Google in working out an acquisition price.

Or maybe this haste reveals how social data is such a hugely valuable chunk of information for Google to organize, and monetize, if ways can be found to use external social data to improve ad targetting without abusing the privacy of users and the privacy policies of their social networks.

In any event, there are interesting times ahead for social data portability. Users stand to benefit from a richer, more social, internet experience, as long as their privacy is not abused. And stay tuned on the social data portability battle between Facebook and Google and MySpace: who will work out how to best monetize external social data in 2009?

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Thanks to everyone who sponsored my Mo

Movember was a big success this year, raising awareness and $20 million for men’s health issues including prostate cancer.

Thanks to everyone who sponsored my Mo, and to anyone who smiled knowingly or looked aghast or otherwise encouraged me along the way.

Here’s the video evidence:

The Mo will return in 2009…

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SearchWiki + OpenSocial = mainstream social search?

Google today launched a rather massive change to its core search product.

SearchWiki adds some innocuous buttons to your search results page, enabling Digg -style voting and Friendfeed -style commenting on each result.


I think this feature might prove valuable for some users, at least the bad spellers among us and those who prefer to repeatedly type the same search term into Google rather than use bookmarks or their memory.

However this feature becomes massively valuable for Google if enough people bother to vote for their favourite sites and add comments. Harnessing the collective wisdom of all those users is a great way for Google to improve upon its not-so-secret-anymore search algorithm.

Currently your own SearchWiki wisdom impacts only your own search results, nobody else’s. But the words chosen to explain SearchWiki do leave the door open for Google to evolve into a social search engine; “Customize your search results with your rankings, deletions, and notes — plus, see how other people using Google have tailored their searches.”

Personally, I’m not sure how much I want strangers (or bots) to influence (or game) my search results.

But I might want my friends and social networks to influence some of my search results.

If only Google could somehow identify all my friends in all my social networks, and keep track of their searching activity. Wait a minute…

SearchWiki + OpenSocial = mainstream social search.

The web is the their platform.

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Speaking in HTTP/1.1

I’m a big fan of Hypertext Transfer Protocol. I am particularly fond of HTTP status codes and the meanings they convey with such concise and precise brevity. I just don’t GET why they are not used more often in natural language, and so this POST contains some examples of how we can start to use HTTP status codes in everyday dialogue.

Rita, trying to wrestle Bob’s attention away from his laptop: “Hey! Bob…?”

Bob, calmly, with Keanu Reeves curling fingers gesture: “100…”

Rita: “… will you put the bins out please?”

Bob, with a shrug of the shoulders: “202?”

Rita, with a roll of the eyes: “406!”

Bob, putting on shoes: “200 200 200 … It’s raining. Where’s my hoody?”

Rita, matter-of-fact: “302. Charity shop.”

Bob, slowly, to himself: “4 … 0 … 9″

Rita: “… and while you’re up, can you pop down to Woolies and pick up some bread and milk …”

Bob: “503″

Rita: “… and a pack of cheese sticks for Jack’s packed lunch. And nappies. And some of those stuffed jalapeño peppers ….”

Bob: “408″

Teapot: “418″

Bob, to the teapot: “Oh don’t YOU start.” (ref)

Rita: “Seriously; I NEED some jalapeños!

Bob, smiling: “402″

Rita, blushing: “403 …”

Bob, tasting victory: “…”

Rita, faking defeat: “… 200″

Bob: “200″

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