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How we started? A part of our team loved the original old reader so much, she never missed a chance to whine about how good it was. Another part of our team was really patient to listen to all of that whining and was ready to start doing something right after another 1/3 of our team realized it was a really cool challenge and started coding.

We are nearly ready for a private beta (Share With Note and comments are being deployed right now). We missed the old reader and were tired of waiting for someone to do it so much, we created The Old Reader from scratch in five weeks.

P.S. Our cats, waiting for The Old Reader:

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Today  The Old Reader’s beta version became public and our team is happy with all the support and feedback so far, please keep ‘em coming.

I want to thank my friends for inspiring, helping and motivating me, so I never gave up on something I liked. I want to thank Dmitriy Krasnoukhov & Anton Tolchanov for making it happen and being incredibly patient while doing so.

I want to thank everyone who responded to my numerous letters and helped us along these eight weeks and personally Igor Safonov for logo lettering and UI improvements, Danil Mekhanoshin and Sergey Tomilov for UI analysis and fixes, Dasha Mingalieva for icon, Kseniya Ryzhkova for priceless guidelines and advices on communication, Natasha Rostovtseva for insightful questions and comments, Anna Gorodetskaya for a professional view on social media issues, Anton Outkine for agreeing to take care of some future layouts, Nata Pokrovskaya for being an attentive OCD beta-tester, Anna Zabolotnaya for encouragement and my sharebros for incredibly detailed useful feedback and kind words. 

I could go on and on with the list, you guys are awesome, thank you for every new user, mention, tweet, feature request and bug report. We already have a new milestone and some serious improvements to deliver.

Thank you all very much,

Elena Bulygina (The Old Reader’s team)

The comments are working. Yay hooray!

Thearn released a free/open program for detecting and monitoring your pulse using your webcam. The code is on github for you to download, play with and modify. If this stuff takes your fancy, be sure and read Eulerian Video Magnification for Revealing Subtle Changes in the World, an inspiring paper describing the techniques Thearn uses in his code:

This application uses openCV (http://opencv.org/) to find the location of the user's face, then isolate the forehead region. Data is collected from this location over time to estimate the user's heartbeat frequency. This is done by measuring average optical intensity in the forehead location, in the subimage's green channel alone. Physiological data can be estimated this way thanks to the optical absorbtion characteristics of oxygenated hemoglobin.

With good lighting and minimal noise due to motion, a stable heartbeat should be isolated in about 15 seconds. Other physiological waveforms, such as Mayer waves (http://en.wikipedia.org/wiki/Mayer_waves), should also be visible in the raw data stream.

Once the user's pulse signal has been isolated, temporal phase variation associated with the detected hearbeat frequency is also computed. This allows for the heartbeat frequency to be exaggerated in the post-process frame rendering; causing the highlighted forhead location to pulse in sync with the user's own heartbeat (in real time).

Support for pulse-detection on multiple simultaneous people in an camera's image stream is definitely possible, but at the moment only the information from one face is extracted for cardiac analysis

thearn / webcam-pulse-detector (via O'Reilly Radar)