Four short links: 26 November 2015

Mozilla Search, Web Dependencies, Systems and Power, and Alphabet Structure

  1. Firefox Leaves Google’s Money Behind (CNET) — regional deals with other search engine companies, notably Yahoo in the United States, Baidu in China and Yandex in Russia.
  2. Managing Performance of Third-Party Scripts — in the words of Tammy Everts, A typical web page contains 75+ 3rd-party calls, which means 75+ potential webperf SPOFs.
  3. How Change Happens — draft of a book with a “systems and power” approach. Consultation period ends December 10, so get in fast if you’re interested. (via Duncan Green)
  4. More on Alphabet (NY Times blog) — G charging its Alphabet siblings for services like HR, mapping tech, compute, etc. Paging Ronald Coase! Ronald Coase to Finance!

Kristian Hammond on truly democratizing data and the value of AI in the enterprise

The O'Reilly Radar Podcast: Narrative Science's foray into proprietary business data and bridging the data gap.

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In this week’s episode, O’Reilly’s Mac Slocum chats with Kristian Hammond, Narrative Science’s chief scientist. Hammond talks about Natural Language Generation, Narrative Science’s shift into the world of business data, and evolving beyond the dashboard.

Here are a few highlights:

We’re not telling people what the data are; we’re telling people what has happened in the world through a view of that data. I don’t care what the numbers are; I care about who are my best salespeople, where are my logistical bottlenecks. Quill can do that analysis and then tell you — not make you fight with it, but just tell you — and tell you in a way that is understandable and includes an explanation about why it believes this to be the case. Our focus is entirely, a little bit in media, but almost entirely in proprietary business data, and in particular we really focus on financial services right now.

You can’t make good on that promise [of what big data was supposed to do] unless you communicate it in the right way. People don’t understand charts; they don’t understand graphs; they don’t understand lines on a page. They just don’t. We can’t be angry at them for being human. Instead we should actually have the machine do what it needs to do in order to fill that gap between what it knows and what people need to know.

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Four short links: 25 November 2015

Four short links: 25 November 2015

Faking Magstripes, Embedded Database, Another Embedded Database, Multicamera Array

  1. magspoofa portable device that can spoof/emulate any magnetic stripe or credit card “wirelessly,” even on standard magstripe readers.
  2. LittleD — open source relational database for embedded devices and sensors nodes.
  3. iondb — open source key-value datastore for resource constrained systems.
  4. Stanford Multicamera Array — 128 cameras, reconfigurable. If the cameras are packed close together, then the system effectively functions as a single-center-of-projection synthetic camera, which we can configure to provide unprecedented performance along one or more imaging dimensions, such as resolution, signal-to-noise ratio, dynamic range, depth of field, frame rate, or spectral sensitivity. If the cameras are placed farther apart, then the system functions as a multiple-center-of-projection camera, and the data it captures is called a light field. Of particular interest to us are novel methods for estimating 3D scene geometry from the dense imagery captured by the array, and novel ways to construct multi-perspective panoramas from light fields, whether captured by this array or not. Finally, if the cameras are placed at an intermediate spacing, then the system functions as a single camera with a large synthetic aperture, which allows us to see through partially occluding environments like foliage or crowds.

Vanessa Cho on GoPro’s design approach

The O’Reilly Design Podcast: Designing for hardware and software, and recruiting and building design teams.

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In this week’s Design Podcast episode, I sit down with Vanessa Cho, head of UX and research for the software and services group at GoPro. Cho, along with her hardware colleague Wesley Yun, will be speaking at O’Reilly’s inaugural Design Conference in January. We talk about designing for hardware and software, building design teams, and what she looks for in new recruits.

Here are a few highlights from our conversation:

I started at GoPro 18 months ago. I was one of the first designers, and in that time, we’ve grown to 18 designers — 18 designers in 18 months. We’ve spent a lot time recruiting and honing down on what is really important to us.

At GoPro, we’re building a hybrid model that allows us to harness specialized skills while delivering speed and scale. What we have is embedded UX generalists for each of the product teams who can champion the customer experience and help define the product and the value of it. Simultaneously, we have a group of shared services, which is filled with specialists, researchers, visual designers, content strategists, and then also me as a manager, that work to help support the UX generalists that are embedded in the team. They’re ensuring that the team not only is working well together but it’s delivering consistent, on-brand quality work. This model … requires a lot of collaboration and communication between the individuals. … It also helps significantly that I have a very tenured, and mature, and collaborative team that always helps, not only on the software side, but also on the hardware side. We have excellent partnership there. Read more…


Mike Kuniavsky on the tectonic shift of the IoT

The O'Reilly Radar Podcast: The Internet of Things ecosystem, predictive machine learning superpowers, and deep-seated love for appliances and furniture.

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In this week’s episode of the Radar Podcast, O’Reilly’s Mary Treseler chats with Mike Kuniavsky, a principal scientist in the Innovation Services Group at PARC. Kuniavsky talks about designing for the Internet of Things ecosystem and why the most interesting thing about the IoT isn’t the “things” but the sensors. He also talks about his deep-seated love for appliances and furniture, and how intelligence will affect those industries.

Here are some highlights from their conversation:

Wearables as a class is really weird. It describes where the thing is, not what it is. It’s like referring to kitchenables. ‘Oh, I’m making a kitchenable.’ What does that mean? What does it do for you?

There’s this slippery slope between service design and UX design. I think UX design is more digital and service design allows itself to include things like a poster that’s on a wall in a lobby, or a little card that gets mailed to people, or a human being that they can talk to. … Service design takes a slightly broader view, whereas UX design is — and I think usefully — still focused largely on the digital aspect of it.

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Jai Ranganathan on architecting big data applications in the cloud

The O’Reilly Data Show podcast: The Hadoop ecosystem, the recent surge in interest in all things real time, and developments in hardware.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.


Given the quick pace of innovation in the data ecosystem, we like to take a step back from the details of individual components, architecture, and applications, in order to take a wider view of the landscape of big data. This allows us to evaluate the progress of technology and infrastructure along the way, shifting our attention from the details of individual components like Spark and Kafka, to larger trends.

Some of the larger trends we’ve been exploring include the capabilities of distributed machine learning and the tradeoffs and design decisions involved in cloud architecture and stream processing.

In this episode of the O’Reilly Data Show, I sat down with Jai Ranganathan, senior director of product management at Cloudera. We talked about the trends in the Hadoop ecosystem, cloud computing, the recent surge in interest in all things real time, and hardware trends:

Large-scale machine learning

This sounds a bit like this should already exist in really good form right now, but one of the things that I’m really interested in is expanding the set of capabilities for distributed machine learning. While there are systems out there today that do do this, I think relative to what you can experience from a singular environment learning scikit-learn or R, the set of things you can do in a distributed fashion is limited. …  It’s not easy to distribute various algorithms and model-building techniques. I think there is still a lot of work for us to do to improve that experience. … And I do want to have good open source options like MLlib. MLlib may be the right answer. I would be perfectly happy if that’s the final answer, but we do need systems just to provide the kind of depth that you typically are used to in the singular environment. That’s just a matter of time and investment because these are non-trivial problems, but they are things that people are working on.

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