Building a Bicycle Barometer

A networked dial makes it easier to choose whether to commute by bike or train

4 min read
Building a Bicycle Barometer
Photo: Jonathan Ford

It takes me 25 minutes to get to work via London’s Tube, and about the same time to cycle to work. I prefer to cycle—the Tube is crowded and prone to delays—but there are times when it’s too cold or wet to avoid the train. Last year, I realized that making the decision before my daily commute to cycle or take the Tube had become a surprisingly complex affair. Transport for London, which runs the city’s transit network, opened up its application programming interfaces (APIs) in 2010 to developers as part of the London Datastore, which aggregates data from a number of public sector organizations serving the city ( This led to the creation of dozens of status and travel planning apps for commuters. Consequently, I’d check the weather forecast the evening before and the status of the Tube on my phone in the morning. I’d hooked up an instant messenger alert (via the service) for rain. None of this was particularly draining, but it was an extra bunch of things that made Monday mornings a bit more Monday morning-ey. I wanted something that would handle all that data and help me spend less time choosing and more time drinking my tea. So I created the Bicycle Barometer, with a needle that swings between a bicycle and a Tube icon depending on the conditions.

In my day job in the United Kingdom’s Government Digital Service, I had got used to ambient information from screens that dot the office. These provide real-time updates about the progress of various projects. I wanted a bit of that for home—something I didn’t need to tap or click to check, something that was just there.

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From WinZips to Cat GIFs, Jacob Ziv’s Algorithms Have Powered Decades of Compression

The lossless-compression pioneer received the 2021 IEEE Medal of Honor

11 min read
Photo of Jacob Ziv
Photo: Rami Shlush

Lossless data compression seems a bit like a magic trick. Its cousin, lossy compression, is easier to comprehend. Lossy algorithms are used to get music into the popular MP3 format and turn a digital image into a standard JPEG file. They do this by selectively removing bits, taking what scientists know about the way we see and hear to determine which bits we'd least miss. But no one can make the case that the resulting file is a perfect replica of the original.

Not so with lossless data compression. Bits do disappear, making the data file dramatically smaller and thus easier to store and transmit. The important difference is that the bits reappear on command. It's as if the bits are rabbits in a magician's act, disappearing and then reappearing from inside a hat at the wave of a wand.

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