It was my absolute pleasure to lead such a massively talented group of Engineers to build our mobile social networking platform. What a ride we shared!

From stealth mode, through private beta to market launch, then the crucial pivot from a direct-to-consumer model to one aimed at mobile operators and handset manufacturers, resulting in our acquisition by Myriad Group and the scaling of our delivery capability, and then achieving customer validation by selling our technology to mobile operators in over a dozen countries, with revenues estimated at USD 80 – 100 million.

But all good things must come to an end. It’s been around 12 months since the acquisition, and I have decided to move on to The Next Challenge…

I’m co-founding a business to commercialize and export some exciting intellectual property coming out of NICTA (National Information and Communications Technology Australia) that we believe will solve a big problem in the global mobile industry.

My partners in this new venture are Max Ott and David McKeague at NICTA. It’s great to be doing this with NICTA, the largest organization in Australia dedicated to ICT research, and the birthplace of Open Kernel Labs, which recently announced the deployment of its software in over one billion mobile handsets worldwide.

We are in stealth mode, so I am unable to share many details at this time. However, for now, the tag cloud includes: mobile, geo, social, big data, psychology, and machine learning.

I’ll be leading Engineering and Product, and I expect to be back & forth between Sydney and the Bay Area for the next several months as we progress through the initial stages of customer development and product development.

Stay tuned for more details.

But what about the mobile browser on your phone?

Mobile Firefox is the only major mobile browser to officially support extensions, and that is currently only for Maemo and Windows Mobile.

I’ve decided that’s not enough!

According to this man below, the mobile browser that accounts for most of our browsing is the iPhone’s Mobile Safari, so let’s start with that.

The technique for extending Mobile Safari makes use of bookmarklets, which are small snippets of javascript stored as a browser Bookmark. This does not require you to jailbreak your iPhone, and this does not require the latest iPhone hardware or software.

What extensions might be useful for your iPhone? According to Comscore, social networking is the fastest growing mobile activity, so I’ve focused on five extensions to make your mobile browsing more social.

There’s a whopping 55 million people using the Facebook iPhone app, and I suspect there might be demand from those people for some social extensions while browsing the web on their iPhone. So included below are extensions that allow you to Like or Share *any* mobile web page, not just the sites which have so far implemented Facebook Social Plugins. And there’s another extension that shows personalized Recommendations powered by Facebook.

Also, my personal use case: I often find myself wanting to tweet about the mobile page I’m reading. But some sites do not include any way to do this, and other sites do not make it easy, or use third party tools which require too many clicks or take me too far away from the page I am reading.

Figuring all that out on a mobile browser is not fun, so one of the extensions below is an easy, quick, reliable, always-opens-in-a-new-window extension to simply pre-fill the status box on Twitter’s own mobile site with simply the title and URL of the page you are reading. Simple.

And last but not least, there’s an extension for Google Buzz users too. Enjoy…

Like!

Whenever you want to Like a web page, this extension will display a Like button powered by Facebook Social Plugins. No more searching high and low for the Like button, and no more waiting for developers to add Like buttons to your favorite sites.

Go to http://mbx.hitching.net/like on your iPhone to add this extension.

Tweet!

To tweet about any web page, use this extension to open Twitter in a new Safari window. Your status will be pre-filled with the title and URL of the page, ready for you to embellish before tweeting. Long URLs will be automatically shortened.

Go to http://mbx.hitching.net/tweet on your iPhone to add this extension.

Recommend!

This extension displays personalized recommendations of content, powered by Facebook Social Plugins.

Go to http://mbx.hitching.net/recommend on your iPhone to add this extension.

Share!

To share any web page on Facebook, this extension will open a share page in a new Safari window, showing the title and URL of the page. You can choose an image from the page, and add a comment, before sharing with your Facebook friends.

Go to http://mbx.hitching.net/share on your iPhone to add this extension.

Buzz!

To post on Google Buzz about any web page, this extension will open Google Buzz in a new Safari window, showing the title and content from the page. You can add a comment before submitting your post.

Go to http://mbx.hitching.net/buzz on your iPhone to add this extension.

If you’ve been using Google Buzz on a mobile phone recently, you would know that you can choose between two filters to the real-time stream of content:

• Social – choose ‘Following’ to filter the stream based on your social graph, or social ‘circle’ as Google prefers to call it. You will see posts from your friends, and also some public posts from friends-of-friends if the Buzz filtration algorithm thinks you want to flex your social circle.
• Geo – choose ‘Nearby’ to filter the stream based on your location, as detected by your mobile phone. You will see public posts from nearby Buzz users, as a chronological list, or located on a map. Most of the value here comes from the stream being updated in real-time.

Now, with the release of the new Google Buzz API from Google Labs, I have added the real-time stream of geo-located Google Buzz content to GeoMeme, my pet project.

GeoMeme detects real-time geo-located trends, now based on millions of daily posts from various Google Buzz and Twitter and MySpace mobile apps.

GeoMeme can detect, for example, that Justin Bieber beats Lady Gaga in New York City.

If you’re curious about Justin Bieber, or about the amount and contents of geo-located Buzz posts, compared to geo-located Twitter and MySpace posts, check it out and let me know what you think.

Murdoch had apparently failed to grasp the significance of the interwebs.

However, ten years later Time Warner has regained its mojo and is now trying to offload a spent and jaded AOL. Did Murdoch get it wrong ten years ago, or did it simply take a whole decade for him to be proven right?

In 2009, the mob is rushing once again to the conclusion that Murdoch is losing his marbles, planning to charge for his online content and blocking the Googlebot from stealing it.

Personally I believe that Murdoch should worry less about the Googlebot, and more about how social media is turning his industry on its head.

The problem is that all of those dotcom upstarts have brought us information overload. There has been an exponential increase in the amount of information and content available to us, way beyond the capacity of the human brain to process.

The solution is social media, which empowers us to easily share the content we care about with our friends and contacts, and adds valuable metadata to that shared content, such as Likes or Retweet counts. This metadata helps us filter the signal from the noise, so that we can focus on just the best quality content from our trusted circle of friends.

This works great for movie reviews. People have always listened to the advice of friends when it comes to choosing what movie to watch. Social media simply provides an efficient and scalable way to do this.

The best example of this social filter is currently FriendFeed, although we can expect to soon see something equally impressive on Facebook. Twitter Search could do this even better if only it were possible to search the entire tweet history of just your friends, or a chosen social distance into your social graph, rather then merely search 7 days of the public timeline. I am hoping that the Google Social Search Experiment will enable this sort of social filter when Google completes its Twitter integration.

Back to Mr. Murdoch… Social media also works for the filtering of news content, however it’s more tricky than movie reviews because there is a need for trustworthy fact rather than mere opinion. This is why Eric Schmidt believes that figuring out how to rank real-time social content, perhaps based on a reliable measure of reputation and authority, is “the great challenge of the age“. It also explains why Twitter’s Retweet feature does not allow the original tweet to be modified, because this makes the Retweet count a more reliable indicator of authority.

So my advice to Rupert Murdoch and other media companies struggling with this; worry less about the Googlebot and more about social media. Focus on improving the quality of your content, so that people share it with their friends.

And if your own social media strategy is not delivering any tangible benefits, try moving it from your Marketing department to your Customer Service department. Use social media to listen more carefully to the needs of your customers, so you can improve the quality of your content to the point where a paid online content model becomes viable.

If Marketing and Customer Service argue about who owns the customer relationship, remind them both that thanks to social media it’s actually the customer who owns and controls the relationship with your business. Not the other way around!

There are now some quite interesting public datasets available, and the developer community is hard at work turning this data into useful APIs, and building innovative applications to consume the data.

Some of the notable apps to emerge from OpenAustralia include:

• It’s Buggered, Mate – from the Canberra Hackfest, a geo app to crowdsource the reporting of broken public infrastructure.
• Suburb Matchmaker – the winner of the Sydney Hackfest, a tool to help you find your ideal suburb to live in.
• FridgeMate – currently winning the MashupAustralia contest and only a couple of days away from the $10,000 prize. FridgeMate lets you assemble a map of local public amenities to stick on your fridge door. My advice to the Creative Possums behind FridgeMate would be to look at using the Zazzle API so people could buy the actual fridge magnet.

My own presentation focussed on some mobile, geo and social technologies to create location-aware mobile mashups to share OpenGov content with friends on Twitter, friends on Facebook, and *real* friends on a t-shirt. Here’s the deck:

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:

• beats in New York City
• Mega Shark beats Giant Octopus in LA
• Snow Leopard beats Windows 7 in Cupertino
• bridge beats swimming in San Francisco Bay
• wtf beats ftw in Washington

How does it all work?

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

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

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)

• a spambot will fail to solve the reCAPTCHA so i am spared the distraction of spam waves
• a genuine person however will be able to solve the reCAPTCHA challenge, so i can enjoy an invite for a genuine non-spam wave
• alternatively the genuine person can befriend me on Facebook, Twitter, etc. to avoid the need for a reCAPTCHA
• and later the genuine person can befriend me on Google Wave to avoid the need for futher reCAPTCHAs

Here’s the translation for my parents: “if I am sent an email by someone I don’t know yet, make sure it’s from a real person before bothering me about more /iagra.”

This solution could be built as an optional extension to a wave server. But that would not be optimal for genuine people waving to others they don’t yet know, because multiple reCAPTCHAs might be required for a single wave. So an improvement would be:

• when a reCAPTCHA is solved, my wave server issues a “Turing token” (proof of humanity) that is also valid for other invitees connected to my social graph
• this “Turing token” can be securely federated between wave servers so that others in my social graph know that the wave originated from a genuine person

That’s it; an idea for combating spam using Google Wave. Thoughts please!

[Update 16 September 09] Google Acquires reCAPTCHA

—
This post originated as a tweet that was imported here by Fresh From, and then I thought about it some more.

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.

Massive thanks to everyone involved in this ARG, it was a fantastic journey. LL&P!

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 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 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 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 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!

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?

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.

I’ve already set up my LinkedIn network and my Facebook friends, so why should I have to do it all again on every other site that has decided to go social on me?

When I heard the OpenSocial announcement last year, while I was F5ing the API URL, waiting to see the campfire video, I was imagining that the problem had been solved, by allowing any social networking site to share its social data with any other.

But the first incarnation of OpenSocial, actually the 0.7th as I write this, is more aimed at developers re-using code to make applications more portable, rather than data portability.

Then Plaxo released a LinkedIn sync feature which looked promising, but that was just two social sites, what about all the others?

Now it looks like Google has provided the solution, not as part of OpenSocial but with its new Social Graph API. Social data becomes portable simply by adding some XFN tags to the hyperlinks between your pages and your friends’ pages and your other pages (view the source of this page and search for rel=”me” to get the idea), then letting the Googlebot spider those links to work out the connections. Very simple and powerful. The internet is the platform.

This must upset Facebook, because the social data representing all those friend connections is a big part of their crown jewels. If Facebook changes profile pages to become publically available (or less revealing profile summaries, as LinkedIn has done), and adds some XFN tags, then that social data and the ad revenue extracted from it will start to trickle out onto the wider web. How long can Facebook resist?

Anyone could start to work out who knows who by using the Social Graph API. Reputable sites will put the decision of how to use that social data in the hands of the user. But there’s also a privacy risk here. Perhaps the answer to that is something along the lines of OpenId which puts the user firmly in control of how portable their identity data is.

My mind spins with the opportunities and challenges created by this great innovation. My favourite: combine social graph data portability with always-connected location-aware mobile devices (“phone”), and you can mashup the social landscape with the physical landscape you’re walking through.

Maybe that’s what Judge Dread’s helmet did.