At Fuse we like trying out new things and we always have very willing folks to sign up with. We started continuously monitoring each others locations 24×7 yesterday. As I came into work today it was great to see everyone converging into our workplace.

I have used other services like Glympse and many others which let me do this too but I never reached this type of critical mass so quickly so haven’t seen it across 10 people in real-time.

The cool thing is that this is integrated with Siri so I can just pick my phone and ask the question “Where is Flynn right now?” and the phone tries to track him down as best as it can and shows it to me:

In an ideal world this is awesome. But it’s a little involved to get off the grid when you want to and at least at this point it’s harder to remember that others could be seeing you.

Possible ways to make this better would be:
1. Notify me when a friend looks me up and views my current location – adds some level of symmetry.
2. Something at the level of a hardware button to easily turn off my location sharing
3. Get rid of the stitched leather

Other than that I think it’s designed pretty well and it’s clear they fought off many complicated considerations to come up with a solution that’s nuanced enough for something as sensitive as location sharing and still retains simplicity.

I had been planning to do this for a while and finally got around to visiting Jigsaw Renaissance which is a maker space in Seattle. They have recently moved into a really cool historical building and Lion gave us a fun tour of all the spaces and the very interesting basement. The building owners are giving out spaces to artists and creative types and we ran into many people who already call it home. Amidst brainstorming ways to drill holes to make grid beam sticks efficiently and an incentive system to promote community engagement, and learning we had a great time.

If you want to make something, or want to learn how to make something or just want to see what people in your city are interested in making you should visit Jigsaw Renaissance and connect with others who can teach, inspire and encourage you.

I visit the NYT webpage and read a couple of stories, say A and B. I then decide that story A is worth sharing and hit the “Recommend” button and it gets posted to my feed. My friend arrives on NYT and sees the headline for story B. He doesn’t know that I checked it out as well but he is interested in it and even clicks on it but he never shares it either. Then a third friend is now on NYT trying to decide what she should read. Given the old scenario, only story A would be recommended to her. The information about story B and two friends interacting with it has been lost.

Maybe it’s lost of for a good reason – it probably wasn’t worth sharing. One could argue it keeps the signal to noise ratio high. But the best way to deal with information overload is generating more information, not less. With enough training data, and meta information like time spent and other derived engagement metrics it won’t be too hard to use that lost information to come up with even better suggestions.

And this is what Facebook is beginning to do with the new Open graph. It wants you to stop thinking about sharing. No need to make a conscious effort to do anything. Just live your life, scrobble it to Facebook and let it decide what you like, what you dislike and what your friends might be interested in. There are more vectors to the signal now which means more information and an ever-learning ever-improving engine.

This is not a new model e.g. Last.fm executed very well on this in the music vertical. Facebook’s play is a broad swipe with a very wide reach. It will become the laboratory of rich human interaction data like none has existed before. Some might be upset by this. But Facebook is a pretty neutral player here. It just happens to be the agent that has successfully made it possible. It’s what the world would have eventually moved to anyway. Just like other evolutionary technologies it will push us out of our comfort zones and it might be a while before we fully comprehend and come to terms with our new realities. We need it as much as we don’t. At least some people have already begun to piece their life together : All of life has been utterly, profoundly changed thanks to Facebook’s new features, and nothing will ever be the same, and all I can do is sit here and weep at the beauty and magic that Mark Zuckerberg has brought to this world

Though I agree that saving an indefinite history of sensitive location data without explicit user notification is a terrible oversight at the least, I was also tempted to see what my own data held. So I went ahead and here’s what it looks like.

My iPhone faithfully recorded my road trip halfway across the country, my SXSW visit to Austin, Bay Area and LA trips and also my trip to Michigan and Ohio. I think it makes a very interesting sharing object at this level of zoom. Especially because I have been voluntarily giving that data to Foursquare anyway. Foursquare is a lot sparser than the iPhone data but it has more explicit knowledge of the exact business/venue I went to as opposed to the iPhone data that can only be used to make a reasonable guess. However, overall the data that the iPhone has been accumulating is obviously more exhaustive.

I am curious to run more detailed analysis on my own data, and possibly compare it with other people I know and other data sources I have to see what interesting stuff I can find. For example, it would be cool to see how much time my wife and I spend with each other and how it correlates to how many steps I took that day, what I ate, or what music I listened to.

Are we really as unique and different as we like to believe or are we just predictable dots on the map? At a higher aggregate level, data from cellphone carriers has already been used to find that we actually are quite predictable!

Monday
Spec writing. Formulate a development plan for a new project including goals, scenarios, implementation details, evaluation plan and schedule.

Tuesday
Demo a project at a public event. This one, specifically, is where I demo Project Montage at the Hacks/Hackers event in Seattle.

Wednesday
I get down and dirty with HTML5 and Jquery as I code away on the project most of this day. The project would benefit from some additional development resources.

Thursday
Conduct discount usability study to identify usability issues in a project under development. I am formally trained as a User Experience Researcher and I am always looking for ways to get useful and timely evaluation in the cheapest way possible.

Friday
I receive a “Ship it” badge as a recognition for another project that was completed and brought to market in the past.

So in summary, my week = spec + demo + coding + usability study + recognition. This, actually, might be different for a different PM depending on their background, passion and project. In general, folks at FUSE are very versatile and multi-talented which makes the workplace a lot of fun!

Conductor: www.mta.me from Alexander Chen on Vimeo.

You can read more on his blog here. [via Gizmodo]

I was unaware of this song until 1996 when it was used in a Coca Cola commercial during the Cricket World Cup. It was an interesting ad and the sound went really well with it.

The Indian Cricket focused Coca Cola ad (1:04)

After I saw the ad, I tried to find out what this song was called (no SoundHounds or Shazams back then). Eventually I found the song through a friend who let me “borrow” the MP3. The file name on the song I got said ‘Nusrat Fateh Ali Khan – Mustt Mustt (Massive Attack Remix).mp3″. I had no idea that Massive Attack was actually a band and assumed it was some fancy name given to the remix. But I enjoyed the song and … that’s all that mattered.

Over the years I lost the song and was recently trying to find it again. I tried searching on iTunes and was happy to find it in the appropriate album. $0.99 later I had the song on my iPhone and was playing it. It sounded like that song but at the same time there was something different about it. It seemed like the percussion lacked the punch, there was less reverb and overall the song felt flatter compared to how I remembered it. After a week of occasionally hearing it and not being able to put my finger on it I went back to see the album listing.

There were two songs that had a similar name. One was called “Mustt Mustt (Lost In His Work)” while the other was called “Mustt Mustt (Massive Attack Remix)”. I confirmed that I had downloaded the latter. However, then I noticed that both the songs had the exact same duration 5:18 minutes! I found that a little odd so I decided to hear the 30 second song preview for the first song. And I was shocked – it sounded exactly the same as the second song. They don’t have the Massive Attack version so they simply copied the original and renamed it, offering it as a separate download? Here’s the song that iTunes was selling as two different songs.

The non-remix (5:20)

I felt cheated and decided to find the song I was looking for, so I went to Amazon MP3 and looked for the same album. Guess what, they had the exact same song listing with the two versions of the song with the same duration.

So finally I decided to look at Walmart MP3 and found the same album. But this time, the two songs had different durations. I heard the preview and it sounded different so I went ahead and downloaded it.

It felt great to finally hear the song as I had heard it a long time ago. It’s funny that I found Walmart to be the most trustworthy source for finding the right song. And, of course, it was $0.05 cheaper than anywhere else. Here’s what that song sounds like in this Youtube video.

The real remix (4:29)

By some weird coincidence, a week later I heard the same song on the radio on KEXP. And it actually sounded yet different, not quite like the first one nor like the second one. Was it my mind playing games or is there really a third version out there? I hope to find out

Web Seer is another visualization tool built specifically for comparing and contrasting google search suggestions for two different search terms. I decided to augment the venn diagram above with the web seer visualization – it’s just another way to present the same information.

As you can see from the visualization, “stupid” is the most frequent word used on Google to complete the sentence “Why are muslims so …” that is also used to complete the sentence “Why are christians so …”. Other common words for both religions are “intolerant” and “hateful”. On the extremes,the words associated with Muslims are “angry” and “violent” while those associated with Christians are “judgmental” and “mean”.

Contrasted with the unique words associated with Muslims, those associated with Jews are “cheap”, “successful” and “smart”. However, the interesting common word for both Muslims and Jews implies “Why are Muslims and Jews so hated?”

According to this visualization, there is nothing in common between words associated with Jews and Christians, however, the original Venn diagram above found the word “annoying” to be common enough.

Extra

India is a democratic country with separation of church and state. However, though it’s dominantly Hindu it still hosts one of the largest Muslim populations in the world in terms of absolute numbers. I was curious to see how the two religions compare on Google. There were zero search suggestions for the term “Why are Hindus so …” so for this case I shortened the search terms to “Why are Hindu” and “Why are Muslim”. Unfortunately, there were no words commonly associated with the two religions but it was interesting to see Hindus’ discontent with the movie Avatar and the color of the depictions of their gods.

I found it interesting to compare this data with the temperature heatmap of that day. We have had an awfully cold spring but this heatmap really drives in the point. That week, the Pacific Northwest was the coldest region in the country! Makes one wonder, if people really are moving to Seattle in hordes, do they really know what they are getting into?

What exactly happens when we are unconscious? You can always go to a sleep lab and have all sorts of machines monitoring your brain frequencies and other vitals so that you can learn more about how you sleep. But if you want the convenience of doing it yourself, you can try this $0.99 app for the iPhone called Sleep Cycle.

The setup

You start Sleep Cycle, leave it running, and place the iPhone face down on the mattress, somewhere near your head area. SleepCycle uses the accelerometer in an iPhone to detect motion as you sleep. This motion is interpreted as a proxy for how active your brain is at that time. When you wake up the next day, you can see a graph of your sleep activity the past night.

Certain levels of activity are associated with different stages of sleep. Lowest activity corresponds to deep slumber when we have no dreams. Some activity corresponds to the stage of Rapid Eye Movement when we have all of our dreams. And high activity corresponds to light sleep – the kind that leaves you tired when you wake up.

Sleep graphs

I have been monitoring my sleep this way for a couple of months now. I have noticed that on average I seem to have 4 instances of REM like activity levels. Does that mean I have as many distinct dreams each night? Most research points out that this is in fact close to the average number of dreams humans have. However, I rarely remember my dreams; so this is insightful for me to understand that my brain is being creative while I am unconscious

And then there are some days where I have interesting variations. For example, the two graphs below are quit extreme when compared to each other. According to the first one I plunged straight into deep sleep as soon as I lied down. But within an hour I was almost awake. I stayed in the high activity area dipping twice to catch 2-3 quick dreams but was pretty much sleeping lightly for the rest of the night. I am guessing it’s one of those nights when your dreams take input from what’s actually happening outside and distorts it into a fantasy dream world experience. Unfortunately, I don’t remember what I dreamt that night so I have no way of validating this.

The one on the right, however, suggests that I stayed in deep sleep for most of the night. I only had a dream around 5:00 am before going back to deep sleep. Is this one of the nights where I actually remember what I dreamt since there was only one? I am not sure because I really couldn’t remember what I dreamt.

Waking up at the “right” time

If you look closely at all the graphs there is one thing in common to almost all of them. They all end on an uptick in my sleep graph – did you notice that? This is no co-incidence. This is the second feature in this app which makes it an awesome alarm clock. You set the time you want to wake up and then use the app as described earlier. Around 30 minutes before your wake up time the app starts looking for increase in activity. As soon as it notices your body’s activity level going up, it triggers the alarm even if it’s 10s of minutes before your desired wake up time. The theory is that if you are woken up at a time when your body is naturally trending towards being active then you wake up feeling more active and less groggy.

I am not sure I can say for certain that this helped me – it’s really hard to notice difference in grogginess – and I am never super happy to wake up And that’s the other problem with the app – there’s no snooze button. So if I don’t want to wake up, I have to quit the app to shut the alarm. Then it stops recording consequent activity, though, so the graphs are not true to the actual time you wake up in case you snoozed.

Augmented mindfulness

At SXSW this year, Robert Fabricant of Frogg Design described Augmented Mindfulness. It’s the idea of collecting information about yourself, processing it, reporting it and then reflecting on it to effect change in yourself.



Sleep Cycle is a great app for the curious and I highly recommend trying it out. However, this data can’t be exported, nor can it be analyzed or visualized in other ways. So it’s limited in how much it can help you in making deeper insights into yourself. It would be great if I could correlate this data with other data about me. For example, I could look at my tweets or social updates of the preceding day to analyze my sentiment and derive my mood and then find correlations with my sleep patterns and how I was feeling that day. Or I could compare it with the food I ate that day that I capture in my food journal.

My Fitbit finally got delivered today. It also lets you monitor your sleep and helps you reflect on your data. It will be interesting to compare how the data differs across the two systems. I will find out soon!