Talking shop with Other Machine Company

The O'Reilly Solid Podcast: Danielle Applestone on running a machine tool startup and empowerment through desktop manufacturing.

Register for Solid 2015, where you can see Danielle Applestone’s session — How to make an Othermill: From milk jugs to your door — and much more.

Othermill

An Othermill. Photo: Other Machine Co.

For this week’s episode of the Solid Podcast, Jon Bruner and I sat down with Danielle Applestone, CEO of the Other Machine Company — purveyors of one of my favorite personal digital fabrication tools: a desktop CNC router called the Othermill (see a demo video).

Grown out of the Machines that Make project at MIT’s Center for Bits and Atoms and incubated at Saul Griffith’s Otherlab in San Francisco, Other Machine Company launched a successful Kickstarter to finance completion of the Othermill back in May of 2013.

For readers not familiar with this particular type of kit, I’ll go into a bit more detail: a CNC (Computer Numerical Control) mill is a machine tool that can be controlled by a computer to move some kind of rotary cutter (such as an endmill or drill bit) to remove material from a workpiece. This is a type of “subtractive manufacturing” process.

With all of the fuss around 3D printing (known in the industry as “additive manufacturing”) these days, I personally don’t think that CNC machining gets enough attention. Although 3D printing is certainly an exciting technology in its own right, it cannot currently compete with CNC machining in terms of cost, supported material types, and range of applications. Read more…

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Better currency through programming

The O'Reilly Radar Podcast: Vitalik Buterin on bitcoin, the blockchain, Ethereum, and the future of money.

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In this Radar Podcast, I chat with Vitalik Buterin, founder of Ethereum and co-founder of Bitcoin Magazine. We met at our Bitcoin & the Blockchain summit in San Francisco to talk about the disruptive potential of the bitcoin and blockchain technologies. He also outlined some of the problems he’s trying to solve with Ethereum and weighed in on how the use cases of money are going to change over the next 10 to 20 years.

Buterin told me that his father initially introduced him to bitcoin in 2011, and he wasn’t immediately interested — in fact, he outright rejected it, thinking, “It looks like it has no intrinsic value, and it’s obviously not going to work.” As he kept hearing about, he decided to investigate more and came to the realization that ultimately led him to create the Ethereum platform:

I immediately recognized that the way bitcoin works is the way that money should work. It’s exactly the correct approach, where you have: here’s the address you’re supposed to send to, here’s how much you want to send, here’s the button to send it. It’s money made for the Internet, not like the credit card approach, where you just basically give everyone the details to take as much as they want from your bank account.

On a trip to Israel, Buterin encountered projects, such as Colored Coins and Mastercoin, using blockchain technology for things other than bitcoin currency. “They were trying to let people issue their own assets,” he said. “They were trying tack features on top, tack financial contracts on top.” The protocols, Buterin noted, were overly complicated and he realized there might be a better way: “You could make it much simpler just by replacing everything with a programming language, and then if you do that, then people can write as many features as they want in the programming language after the fact.”

Read more…

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A real-time processing revival

Things are moving fast in the stream processing world.

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Register for Strata + Hadoop World, London. Editor’s note: Ben Lorica is an advisor to Databricks and Graphistry. Many of the technologies discussed in this post will be covered in trainings, tutorials, and sessions at Strata + Hadoop World in London this coming May.

There’s renewed interest in stream processing and analytics. I write this based on some data points (attendance in webcasts and conference sessions; a recent meetup), and many conversations with technologists, startup founders, and investors. Certainly, applications are driving this recent resurgence. I’ve written previously about systems that come from IT operations as well as how the rise of cheap sensors are producing stream mining solutions from wearables (mostly health-related apps) and the IoT (consumer, industrial, and municipal settings). In this post, I’ll provide a short update on some of the systems that are being built to handle large amounts of event data.

Apache projects (Kafka, Storm, Spark Streaming, Flume) continue to be popular components in stream processing stacks (I’m not yet hearing much about Samza). Over the past year, many more engineers started deploying Kafka alongside one of the two leading distributed stream processing frameworks (Storm or Spark Streaming). Among the major Hadoop vendors, Hortonworks has been promoting Storm, Cloudera supports Spark Streaming, and MapR supports both. Kafka is a high-throughput distributed pub/sub system that provides a layer of indirection between “producers” that write to it and “consumers” that take data out of it. A new startup (Confluent) founded by the creators of Kafka should further accelerate the development of this already very popular system. Apache Flume is used to collect, aggregate, and move large amounts of streaming data, and is frequently used with Kafka (Flafka or Flume + Kafka). Spark Streaming continues to be one of the more popular components within the Spark ecosystem, and its creators have been adding features at a rapid pace (most recently Kafka integration, a Python API, and zero data loss). Read more…

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4 reasons why microservices resonate

Microservices optimize evolutionary change at a granular level.

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We just finished the first O’Reilly Software Architecture Conference and the overwhelming most popular topic was microservices. Why all the hype about an architectural style?

Microservices are the first post-DevOps revolution architecture.

The DevOps revolution highlighted how much inadvertent friction an outdated operations mindset can cause, starting the move towards automating away manual tasks. By automating chores like machine provisioning and deployments, it suddenly became cheap to make changes that used to be expensive. Some architects properly viewed this new capability as a super power, and built architectures that fully embraced the operational aspects of their design. The Microservice architectural style prioritizes operational concerns as one of the key aspects of the architecture.

Microservice architectures borrow a design aesthetic from Domain Driven Design called the Bounded Context. A bounded context encapsulates all internal details of that domain and has explicit integration points with other bounded contexts. Microservice architectures reify the logical DDD bounded context into physical architecture. For example, it is common in microservice architectures for services that must persist data to own their database: members of the service team handle provisioning, backups, schema, migration, etc. In other words, in microservice architectures, the bounded context is also a physical context. But that also means that this service implementation isn’t coupled to any other team’s implementation, clearing the path for independent evolution. I recently published some writing about the recent realization that architecture is abstract until operationalized. In other words, until you have deployed an architecture and upgraded parts of it, you don’t fully understand it.

Read more…

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Solid Podcast: Trip to McMoon’s

Dennis Wingo on the Lunar Orbiter Image Recovery Project, the project's Indiegogo campaign, and Skycorp.

The first "Earthrise" image, taken by the Lunar Orbiter 1 satellite and recovered by Dennis Wingo's Lunar Orbiter Image Recovery Project. Credit: NASA/Skycorp Incorporated

Before and after: The first “Earthrise” image, taken by the Lunar Orbiter 1 satellite and recovered by Dennis Wingo’s Lunar Orbiter Image Recovery Project. Credit: NASA/Skycorp Incorporated.

We’re kicking off our newest series, the O’Reilly Solid Podcast, with an episode recorded in the manager’s office of a McDonald’s at NASA’s Ames Research Center. David Cranor and I visited McMoon’s, as it’s known, to talk with Dennis Wingo, founder of two audacious “techno archaeology” efforts.

In the first episode, we discuss the Lunar Orbiter Image Recovery Project, which has rescued NASA’s first high-resolution images from satellites orbiting the moon. Dennis’ team reverse-engineered the extraordinary analog image transmission system that the satellites used in 1966 and 1967, digitized 14 tons of magnetic tape, and interpreted them to compose imagery at vastly higher resolution than NASA was originally able to recover from the satellites. Read more…

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