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schwit1 writes "Stomping on the brakes of a 3,500-pound Ford Escape that refuses to stop–or even slow down–produces a unique feeling of anxiety. In this case it also produces a deep groaning sound, like an angry water buffalo bellowing somewhere under the SUV's chassis. The more I pound the pedal, the louder the groan gets–along with the delighted cackling of the two hackers sitting behind me in the backseat. Luckily, all of this is happening at less than 5mph. So the Escape merely plows into a stand of 6-foot-high weeds growing in the abandoned parking lot of a South Bend, Ind. strip mall that Charlie Miller and Chris Valasek have chosen as the testing grounds for the day's experiments, a few of which are shown in the video below. (When Miller discovered the brake-disabling trick, he wasn't so lucky: The soccer-mom mobile barreled through his garage, crushing his lawn mower and inflicting $150 worth of damage to the rear wall.) The duo plans to release their findings and the attack software they developed at the hacker conference Defcon in Las Vegas next month–the better, they say, to help other researchers find and fix the auto industry's security problems before malicious hackers get under the hoods of unsuspecting drivers."

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Building a company from the ground up is no easy task. That's why most entrepreneurs make mistakes along the way, and first-time entrepreneurs make the most. 

Unfortunately for many, the failure rate is extremely high — generally 50% to 70% of small businesses fail within the first 18 months. To learn from those who've been in the trenches, we combed through a recent Quora thread that asks: "What are the most common mistakes first-time entrepreneurs make?

Below are the most helpful pieces of advice for first-time entrepreneurs: 

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Kim Kyung-Hoon

When I heard that the rate of recycling PET plastic bottles in China is almost 90%, I was surprised. Because I have noticed since moving to Beijing that the Chinese have no real concept of separating...

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GoogleDevelopers


Automating Your Front-End Workflow With Yeoman 1.0

Writing a modern web app these days can sometimes feel like a tedious process; frameworks, boilerplates, abstractions, dependency management, build processes...
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California Governor Edmund G. Brown Jr. yesterday declared this week as "Wildfire Awareness Week" in recognition of last week's devastating fires northwest of Los Angeles. His proclamation noted, "In an average year, wildfires burn 900,000 acres of California's timber and grasslands." Rains that moved into the area on Monday helped extinguish the fires that started last Thursday along US Route 101 near Camarillo Springs and Thousand Oaks, endangering some 4,000 homes. -- Lloyd Young ( 31 photos total)
A man on a rooftop looks at approaching flames as the Springs Fire continues to grow on May 3 near Camarillo, Calif. The wildfire has spread to more than 18,000 acres on day two and is 20 percent contained. (David Mcnew/Getty Images)     

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Panda: A System for Provenance and Data

Google Tech Talk October 26, 2012 (more info below) Presented by Jennifer Widom, Stanford University. ABSTRACT The goal of the Panda (Provenance and Data) project has been to develop a general-purpose system for modeling, capturing, storing, exploiting, and querying data provenance in a wide range of applications. Abstractly, provenance (also referred to as lineage) describes where data came from and how it has been processed over time. In Panda we consider "data-oriented workflows" whose nodes are arbitrary queries and transformations, challenging us to integrate data-based and process-based provenance, to handle a spectrum from well-understood to opaque transformations, and to develop compositional formalisms and algorithms suitable for arbitrary workflows. On the system side, we strive to enable efficient provenance operations while keeping the capture overhead low. In this talk, we lay the foundations for data-oriented workflows, then discuss how provenance is defined and captured in this environment. We describe the basic provenance-enabled operations of backward tracing, forward tracing, forward propagation, and refresh, and explain how we support these operations in three settings: provenance as general predicates, provenance as attribute mappings, and provenance in workflows composed exclusively of Map and Reduce functions. We briefly describe the prototype Panda system, and we discuss possible follow-on work: extensions to the provenance model and operations <b>...</b>
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GoogleTechTalks
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