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Docker Raises $15M For Its Open-Source Platform That Helps Developers Build Apps In The Cloud | TechCrunch

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Comments:"Docker Raises $15M For Its Open-Source Platform That Helps Developers Build Apps In The Cloud | TechCrunch"

URL:http://techcrunch.com/2014/01/21/docker-raises-15m-for-popular-open-source-platform-designed-for-developers-to-build-apps-in-the-cloud/


The shift to scale out architectures and an app-centric culture has turned out well for Docker and its lightweight open-source “container” technology designed for developers to quickly move code to the cloud.

That’s evident in today’s news that the company has raised $15 million in a Series B round led by Greylock Partners, with minority participation from Insight Venture Partners and existing investors Benchmark Capital and Trinity Ventures. Also participating is Yahoo! Co-Founder Jerry Yang, who has participated in previous rounds.

Docker will use the funding to push toward the general availability of the Docker environment, develop commercial services that pair with the open-source technology and build a team to support the growing community.

The technology path is similar to the one VMware followed in its early days when IT managed their corporate-owned infrastructure. These were state-of-the-art data centers that had to be optimized to run enterprise software. For these IT managers, VMware became a critical part of the equation so multiple virtual machines could run on its hypervisor and server environment. VMware is lauded for the excellent job it did in managing its technology so the end-user was not impacted and the IT manager could manage the infrastructure effectively.

The similarity to VMware in its early days and the excitement that Docker has generated made it an attractive investment, said Jerry Chen, a general partner at Greylock who joined the venture capital firm in August. It is Chen’s first investment since joining Greylock.

“One of the things we learned at VMware is be as frictionless as possible,” Chen said in a phone interview today. “Docker has that ability as well.”

Docker also can be scaled from scratch. It can grow to multiple apps or be used on public or private servers, Chen said. And it can be scaled out in seconds, moved anywhere and all done without having to re-configure all over again.

“Docker is the right tech to fit the rapid updates,” Chen said.

Docker faces the challenge of making its technology easy-to-use with features that make it effective for a developer or a DevOps professional. For this new DevOps pro, Docker has to consider the management and orchestration of apps that are continuously updated using the Docker environment. For example, Docker will develop both public and private registries for developers to store their containers. It also plans to build management and orchestration tools that are needed as people and their organizations manage more and more Docker containers.

And then there is the community, which continues to grow at scale. Docker is now one of the world’s fastest-growing, open-source efforts. There have been more than 9,000 stars given to Docker on GitHub as well as more than 1,320 forks. To manage that growing community will take investment that the company will need to manage with product development.

It’s that community that helped Docker gain acceptance with Red Hat, which is integrating it into OpenShift, its PaaS environment. It has also been adopted by Google Compute Engine. eBay, Yandex and a host of other companies are using Docker in production environments.

Docker’s Background

Docker is the result of a pivot led by Solomon Hykes, who originally launched the company as DotCloud in 2009.

Originally designed as a platform as a service (PaaS), Docker showed promise for its flexible capabilities in providing developers with a service that supported multiple programming languages. But the competition from companies like Heroku and VMware’s Cloud Foundry made for a challenging market, further exacerbated by the lack of a widespread market acceptance for the benefits that PaaS providers offered.

But developers did need a way to move their code to cloud services in a lightweight way without the tax of heavy virtual machines that were difficult to move and required a degree of manual integration. The problem stemmed from the virtualization technology itself, which sits below the operating system. It virtualizes the server, not the app. And because of that, the operating system has to move in order to run the app wherever it might be transported. Once delivered, it has to be booted up and configured to run with the database and the rest of the stack that it depends on.

With Docker, the container sits on top of the operating system. The only thing that moves is the code. The developer does not have to boot and config. Instead, the container syncs with the cloud service.

Hykes launched the open-source effort last spring and the acceptance has been almost unprecedented.

“I have never seen a technology take off as quickly as Docker and get the type of broad-based adoption that it is getting,” said Dan Scholnick of Trinity Ventures in a phone interview last week. “If you look at the absolute numbers — the number of Docker containers downloaded, the number of docker containers created — they are off the charts. What is more interesting, the adoption is not just coming from startup or certain types of companies. The adoption is across companies of all sizes and industry verticals. It is a combination of high-growth and broad-based adoption that is really amazing.”

There really are no equivalents to Docker. There are alternatives to it but as a Linux container it is the most widely used in the market. Its deepest competition will stem from VMware and virtualization providers that market to developers. And that’s not it. Cloud Foundry has its own form of a Linux container, which raises a question about how Docker fulfills its promise as a technology platform. The container is one part of the puzzle. It’s the foundation, but there are tool developers who can seize the opportunity to develop technologies that compete with Docker while also participating in its ecosystem.


Warren Buffett Offers $1 Billion For Perfect March Madness Bracket - Forbes

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Comments:"Warren Buffett Offers $1 Billion For Perfect March Madness Bracket - Forbes"

URL:http://www.forbes.com/sites/kellyphillipserb/2014/01/21/warren-buffett-offers-1-billion-for-perfect-march-madness-bracket/


Warren Buffett speaking to a group of students from the Kansas University School of Business (Photo credit: Wikipedia)

It’s not every day that Brady gets sent packing, Manning sends potential travelers scrambling to the midwest and a Stanford grad makes news for freaking out on reporter Erin Andrews but that’s exactly what happened on Sunday. It was a play-off day to remember and had many already looking forward to the Super Bowl.

Not everybody is thinking about football.

Today, Warren Buffett had tongues wagging about basketball. The billionaire, who ranks 4th on Forbes list of top billionaires, with an estimated worth of $53 billion, wants to make you an offer: he’ll give you $1 billion for a perfect March Madness bracket.

Nope, that’s not a typo.

Buffett, together with his company, Berkshire Hathaway, are offering $1 billion to any person who can correctly pick the winners of all 63 games in this year’s NCAA men’s college basketball tournament. Buffett and Berkshire Hathaway are partnering with Quicken Loans, owned by fellow billionaire Cleveland Cavaliers owner Dan Gilbert, to offer the ‘Quicken Loans Billion Dollar Bracket.’ Gilbert is listed at #384 of Forbes’ list of billionaires with an estimated net worth of $3.9 billion.

It’s a pretty safe bet to say that they’re good for it.

But are you up for the challenge?

The odds of winning are said to be one in 4,294,967,296. That feels about right, considering that it’s all I can do to make it past the first round with my final four teams still intact.

Don’t worry if you don’t have the chops to pick a perfect bracket. Quicken will still award a whopping $100,000 each to the 20 most accurate “imperfect” brackets for use in buying, refinancing or remodeling a home.

It’s enough to make you start reading ESPN every day, right? I have to think they’re already salivating at the notion. The network’s tweet earlier today about the contest has been retweeted over 38,000 times:

The details are as follows: pick all of the correct winners and win. If there is more than one winner (statistically, that’s nearly unfathomable), you’ll share the $1 billion prize.

The $1 billion will be paid in 40 annual installments of $25 million. Or if you don’t want to wait around that long, you can claim a lump sum payment of just half: $500 million.

There’s nothing to it to enter. No fee. In fact, it’s so simple, joked Buffett, throwing in a reference to a Geico commercial, “To quote a commercial from one of my companies, I’d dare say it’s so easy to enter that even a caveman can do it.”

You can enter the contest at any time beginning Monday, March 3rd, 2014 through Wednesday, March 19th, 2014. Brackets will be made available on Selection Sunday, March 16, 2014, and will initially be limited to 10 million entrants, but only one per household. For more information, you can check out the Quicken Loans page on Facebook.

What happens if you actually win the $1 billion? That’s more than 68 miles of cash in one dollar bills. Let that sink in for a minute.

And then forget about all of the houses and cars and cool gadgets you’ll buy.

Let’s focus on the tax consequences (trust me, Mr. Buffett would want you to). If you take the lump sum, you’re going to owe some serious cash to Uncle Sam. Assuming you’re single (though, with a $1 billion, I’d guess you won’t stay single for long), you’ll pay about $395,955,348 in federal income taxes. Keep in mind that while the highest bracket for 2014 (39.6%) kicks in at $406,750, our income tax system is progressive which means that you don’t pay a flat rate. Even as a billionaire, you pay the same rate (10%) on the first $9,075 as everybody else and so on.

That $395,955,348 in federal taxes assumes little to nothing in the way of deductions. I’m going to assume you’ll be phased out of most deductions and credits at that point – and really, would you want that to be the tax year where you met the 10% of adjusted gross income (AGI) for your medical expenses deduction?

Would there be any other tax breaks available to the winner? Mr. Buffett is, of course, infamous for touting the inequity of a system where the top income earners tend to pay proportionately less. That doesn’t seem to be the case here – not off the bat.

The folks who tend to benefit from our tax system do so because their income is not taxed as ordinary income. It’s taxed as something far more advantageous – like capital gains or certain kinds of dividends. That clearly wouldn’t be the case here. It’s also not wages or interest.

It’s not gambling income. Unlike most other office pools, you don’t pay to enter, and it isn’t a proper lottery, raffle or game of chance. Gambling winnings are fully taxable but casual gamblers do get something of a break: you can report your losses (assuming you itemize) on your Schedule A as a miscellaneous deduction not subject to the 2% AGI limit. You can’t report more in losses than you claim in winnings but in this case, I think it’s a safe bet to say that losing a billion dollars in gambling would be far more painful than paying taxes for winning a billion dollars. Fortunately, with this contest, no one would be in the position to figure it out.

It’s not business income. Well, probably not. There are folks who enter games, contests and sweepstakes for a living (remember that Julianne Moore movie?) but that’s some serious dedication to the cause. Assuming that you did enough research and really dedicated yourself to winning, you could possibly treat it as your business (or more likely, a hobby). If you did, you would report your winnings (of course) but could also deduct any reasonable expenses associated with winning. If you classed it simply as a hobby, you would report winnings as “other income” (good ol’ line 21 on your federal form 1040) and claim related deductions against your winnings if you itemize. Those miscellaneous itemized deductions would be limited to those in excess of 2% of your AGI. You can’t carry excess deductions forward or backwards but again, as with potential gambling losses, if your deductions exceed your winnings in this regard, you have bigger things to worry about.

If, however, you can carve out an argument that your work to enter qualified as a business, you would report your winnings – but this time on a Schedule C. You would also claim your related deductions on a Schedule C. In that event, your deductions wouldn’t be limited to a percentage of your AGI. Kind of brilliant if you could pull it off. But don’t get too giddy: this one will require some serious dedication and extensive documentation to pull off. And if you try too hard to prove your point – and you’re lying – you’ll likely get smacked with a pretty serious penalty. So unless you really do the whole contest circuit for a living, skip to the next paragraph.

At the end of the day, this is a contest. It’s a great contest with an unbelievable prize. But it’s still a contest. If you win a prize in a lucky number drawing or other contest, you must include your winning in your income on form 1040 at line 21. It’s “other income.” It’s a significant amount of “other income” but still other income. There’s no offset, no reduction. In fact, there’s only one way to avoid reporting this income – and it can be found in the instructions for the form 1040: “if you refuse to accept a prize, do not include its value in your income.” But that would be seriously crazy. You should, at the very least, ask your tax professional (or favorite tax blogger) if she – I mean, he or she – wants it.

Bottom line: some or all of that $1 billion will be taxable. But it’s still a billion dollars to start with – so complaining about taxes should be done quietly.

It’s the ultimate March Madness. The NCAA tourney – which will play second fiddle to the real contest this year – starts March 18.

Want more taxgirl goodness? Pick your poison: receive posts by email, follow me on twitter (@taxgirl), hang out with me on Facebook or check out my YouTube channel. If you want to keep an eye on documents I’ve posted, check out my profile on Scribd. And finally, you can subscribe to my podcast on the site or via iTunes (it’s free).

BBC News - Argentina restricts online shopping as foreign reserves drop

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URL:http://www.bbc.co.uk/news/world-latin-america-25836208


21 January 2014Last updated at 23:49 ET

Argentina has introduced new restrictions on online shopping as part of efforts to stop foreign currency reserves from falling any further.

Anyone buying items through international websites will now need to sign a declaration and produce it at a customs office, where the packages have to be collected.

The procedure will need to be repeated for every new purchase.

Argentina's reserves of hard currencies dropped by 30% last year.

The government of President Cristina Fernandez de Kirchner has introduced a number of restrictions on transactions with foreign currency.

Items imported through websites such as Amazon and eBay are no longer delivered to people's home addresses. The parcels need to be collected from the customs office.

Individuals are allowed to buy items up to the value of $25 (£15) from abroad tax free every year, but it has been hard for custom officials to keep accurate records of consumers' transactions.

Once the $25 level is reached, online shoppers in Argentina need to pay a 50% tax on each item bought from international websites.

Currency controls

The government hopes that new declaration will make it easier for customs officials to enforce the import tax, says the BBC's Ignacio de los Reyes in Buenos Aires.

New currency controls were introduced a week after Ms Fernandez was re-elected in 2011.

Among the restrictions introduced more recently was a 35% tariff on credit card transaction abroad.

Despite the government's efforts, Argentina's reserves are now below $30bn (£18bn) - their lowest level since 2006.

Currency controls, which were common in most countries until the mid-1980s were dropped in Argentina in 1991. Finance Minister Domingo Cavallo pegged the local currency, the peso, to the dollar.

The plan collapsed 10 years later, when the government was forced to devalue its currency.

The country eventually froze bank accounts and defaulted on its debts. It has since struggled to attract foreign loans at market rates.

Here Are 24 Countries Where Windows Phone Outsells The iPhone (And Why It Does) - Forbes

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URL:http://www.forbes.com/sites/gordonkelly/2014/01/21/here-are-24-countries-where-windows-phone-outsells-the-iphone-and-why-it-does/


Statistics may say Windows 8 is a flop but, contrary to popular opinion, Windows Phone is far from down and out in the battle for our mobile affections. In fact in many parts of the world sales are rocketing past the iPhone.

This month MicrosoftMicrosoft broke these areas down country by country and confirmed exactly where Windows Phone is specifically outselling the iPhone. The list reads as follows:

Chile, Colombia, Czech Republic, Egypt, Ecuador, Finland, Greece, Hungary, India, Italy, Kenya, Kuwait, Malaysia, Mexico, Nigeria, Pakistan, Peru, Poland, Saudi Arabia, South Africa, Thailand, Ukraine, United Arab Emirates and Vietnam.

Interestingly in 14 of these markets – Chile, Czech Republic Finland, India, Italy, Greece, Hungary, Malaysia, Mexico, Poland, South Africa, Thailand, Ukraine, Vietnam – Windows Phone has taken second place in the sector, stepping past former strongholds for BlackBerry and Symbian which had also previously edged out iOS.

Furthermore, the context for this data is solid. It comes from global smartphone sales in Q3 2013 by independent market analyst IDC, which also points out Microsoft only led iPhone sales in seven countries in Q3 2012. Sales are also up 156 percent during the last 12 months, triple Android’s annual growth and 6x that of iOS.

But there are significant caveats.

• 156 percent is easier to achieve on a younger platform with a smaller market share
• Italy and Finland aside, Windows Phone is primarily growing is poorer nations where the iPhone is prohibitively expensive
• Q3 is typically a slow period for AppleApple with sales dipping ahead of expected iPhone refreshes
• iPhone sales boom over the Christmas period and Q4 figures have yet to be announced
• Windows Phone sales are most in the low end with the Lumia 5210 (top) and 520 accounting for 42.4 percent of all shipments
NokiaNokia accounts for 93.2 percent of all Windows Phone handset sales highlighting little traction or interest from companies where Microsoft does not have control

As such, it is possible for cynics to argue Windows Phone is gaining in markets where the iPhone doesn’t compete and making little headway where it does. Except this isn’t true either.

Windows Phone Is Fast Becoming A Hit In Europe

Click to enlarge

A supporting release for researcher Kantar in December (figures right) points out that Europe as a whole is quickly warming to Windows Phone at the expense of iOS. Across the ‘EU5’ (Britain, Germany, France, Italy and Spain) iOS market share fell 5 percent over the last 12 months to 15.8 percent while Windows Phone has leapt from 4.8 percent to 10.2 percent (Android grew from 64.5 percent to 70.9 percent).

Most marked is the aforementioned Italy, where a 12.8 percent market shift in 12 months now sees Windows Phone lead iOS 16.1 percent to iOS on 10.1 percent, but there is also Spain where both platforms are tied on 4.3 percent (though Android has a massive 90.1 percent). France also sees an 11 percent shift between Windows Phone and iOS in the last year with the former now at 12.5 percent versus the latter’s 15.9 percent.

Britain remains iOS’s biggest ally in Europe where its market share of 28.7 percent is nearly triple Windows Phone’s 11.9 percent, though that still represents an 11.3 percent shift over the last 12 months in Microsoft’s favor.

As such, only two nations really remain iOS fortresses: the US, where iOS dominates Windows Phone 40.8 to 4.8 percent, and Japan where iOS rules all the platforms with 61.1 percent of the market.

Conclusion

So what can we make of this? In short that generalists on both sides are talking nonsense. Windows Phone is still struggling to shift higher end devices, but clear traction is not restricted to developing countries. Developed countries, particularly in Europe where iOS has previously been dominant, are showing strong shifts towards Windows Phone.

Should Apple care? Perhaps not. In only shipping two relatively expensive models with high margins it still dominates the earnings across all platforms (including Android) but this will cause it to lose market share. Should it enter the low end, Apple arguably only risks cannibalizing its own sales and profit margins.

But make no mistake, Windows Phone is no joke. Its force may not be felt in the US but it is growing fast and winning friends around the rest of the world. Yes, much of this may be in the low end, but that has never been a bad gateway to more premium products long term.

Also on Forbes:

How a Math Genius Hacked OkCupid to Find True Love - Wired Science

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URL:http://www.wired.com/wiredscience/2014/01/how-to-hack-okcupid/


Mathematician Chris McKinlay hacked OKCupid to find the girl of his dreams.  

Emily Shur

Chris McKinlay was folded into a cramped fifth-floor cubicle in UCLA’s math sciences building, lit by a single bulb and the glow from his monitor. It was 3 in the morn­ing, the optimal time to squeeze cycles out of the supercomputer in Colorado that he was using for his PhD dissertation. (The subject: large-scale data processing and parallel numerical methods.) While the computer chugged, he clicked open a second window to check his OkCupid inbox.

McKinlay, a lanky 35-year-old with tousled hair, was one of about 40 million Americans looking for romance through websites like Match.com, J-Date, and e-Harmony, and he’d been searching in vain since his last breakup nine months earlier. He’d sent dozens of cutesy introductory messages to women touted as potential matches by OkCupid’s algorithms. Most were ignored; he’d gone on a total of six first dates.

On that early morning in June 2012, his compiler crunching out machine code in one window, his forlorn dating profile sitting idle in the other, it dawned on him that he was doing it wrong. He’d been approaching online matchmaking like any other user. Instead, he realized, he should be dating like a mathematician.

OkCupid was founded by Harvard math majors in 2004, and it first caught daters’ attention because of its computational approach to matchmaking. Members answer droves of multiple-choice survey questions on everything from politics, religion, and family to love, sex, and smartphones.

On average, respondents select 350 questions from a pool of thousands—“Which of the following is most likely to draw you to a movie?” or “How important is religion/God in your life?” For each, the user records an answer, specifies which responses they’d find acceptable in a mate, and rates how important the question is to them on a five-point scale from “irrelevant” to “mandatory.” OkCupid’s matching engine uses that data to calculate a couple’s compatibility. The closer to 100 percent—mathematical soul mate—the better.

But mathematically, McKinlay’s compatibility with women in Los Angeles was abysmal. OkCupid’s algorithms use only the questions that both potential matches decide to answer, and the match questions McKinlay had chosen—more or less at random—had proven unpopular. When he scrolled through his matches, fewer than 100 women would appear above the 90 percent compatibility mark. And that was in a city containing some 2 million women (approximately 80,000 of them on OkCupid). On a site where compatibility equals visibility, he was practically a ghost.

He realized he’d have to boost that number. If, through statistical sampling, McKinlay could ascertain which questions mattered to the kind of women he liked, he could construct a new profile that honestly answered those questions and ignored the rest. He could match every woman in LA who might be right for him, and none that weren’t.

Chris McKinlay used Python scripts to riffle through hundreds of OkCupid survey questions. He then sorted female daters into seven clusters, like “Diverse” and “Mindful,” each with distinct characteristics.

Maurico Alejo

Even for a mathematician, McKinlay is unusual. Raised in a Boston suburb, he graduated from Middlebury College in 2001 with a degree in Chinese. In August of that year he took a part-time job in New York translating Chinese into English for a company on the 91st floor of the north tower of the World Trade Center. The towers fell five weeks later. (McKinlay wasn’t due at the office until 2 o’clock that day. He was asleep when the first plane hit the north tower at 8:46 am.) “After that I asked myself what I really wanted to be doing,” he says. A friend at Columbia recruited him into an offshoot of MIT’s famed professional blackjack team, and he spent the next few years bouncing between New York and Las Vegas, counting cards and earning up to $60,000 a year.

The experience kindled his interest in applied math, ultimately inspiring him to earn a master’s and then a PhD in the field. “They were capable of using mathema­tics in lots of different situations,” he says. “They could see some new game—like Three Card Pai Gow Poker—then go home, write some code, and come up with a strategy to beat it.”

Now he’d do the same for love. First he’d need data. While his dissertation work continued to run on the side, he set up 12 fake OkCupid accounts and wrote a Python script to manage them. The script would search his target demographic (heterosexual and bisexual women between the ages of 25 and 45), visit their pages, and scrape their profiles for every scrap of available information: ethnicity, height, smoker or nonsmoker, astrological sign—“all that crap,” he says.

To find the survey answers, he had to do a bit of extra sleuthing. OkCupid lets users see the responses of others, but only to questions they’ve answered themselves. McKinlay set up his bots to simply answer each question randomly—he wasn’t using the dummy profiles to attract any of the women, so the answers didn’t mat­ter—then scooped the women’s answers into a database.

McKinlay watched with satisfaction as his bots purred along. Then, after about a thousand profiles were collected, he hit his first roadblock. OkCupid has a system in place to prevent exactly this kind of data harvesting: It can spot rapid-fire use easily. One by one, his bots started getting banned.

He would have to train them to act human.

He turned to his friend Sam Torrisi, a neuroscientist who’d recently taught McKinlay music theory in exchange for advanced math lessons. Torrisi was also on OkCupid, and he agreed to install spyware on his computer to monitor his use of the site. With the data in hand, McKinlay programmed his bots to simulate Torrisi’s click-rates and typing speed. He brought in a second computer from home and plugged it into the math department’s broadband line so it could run uninterrupted 24 hours a day.

After three weeks he’d harvested 6 million questions and answers from 20,000 women all over the country. McKinlay’s dissertation was relegated to a side project as he dove into the data. He was already sleeping in his cubicle most nights. Now he gave up his apartment entirely and moved into the dingy beige cell, laying a thin mattress across his desk when it was time to sleep.

For McKinlay’s plan to work, he’d have to find a pattern in the survey data—a way to roughly group the women according to their similarities. The breakthrough came when he coded up a modified Bell Labs algorithm called K-Modes. First used in 1998 to analyze diseased soybean crops, it takes categorical data and clumps it like the colored wax swimming in a Lava Lamp. With some fine-tuning he could adjust the viscosity of the results, thinning it into a slick or coagulating it into a single, solid glob.

He played with the dial and found a natural resting point where the 20,000 women clumped into seven statistically distinct clusters based on their questions and answers. “I was ecstatic,” he says. “That was the high point of June.”

He retasked his bots to gather another sample: 5,000 women in Los Angeles and San Francisco who’d logged on to OkCupid in the past month. Another pass through K-Modes confirmed that they clustered in a similar way. His statistical sampling had worked.

Now he just had to decide which cluster best suited him. He checked out some profiles from each. One cluster was too young, two were too old, another was too Christian. But he lingered over a cluster dominated by women in their mid-twenties who looked like indie types, musicians and artists. This was the golden cluster. The haystack in which he’d find his needle. Somewhere within, he’d find true love.

Actually, a neighboring cluster looked pretty cool too—slightly older women who held professional creative jobs, like editors and designers. He decided to go for both. He’d set up two profiles and optimize one for the A group and one for the B group.

He text-mined the two clusters to learn what interested them; teaching turned out to be a popular topic, so he wrote a bio that emphasized his work as a math professor. The important part, though, would be the survey. He picked out the 500 questions that were most popular with both clusters. He’d already decided he would fill out his answers honestly—he didn’t want to build his future relationship on a foundation of computer-generated lies. But he’d let his computer figure out how much importance to assign each question, using a machine-learning algorithm called adaptive boosting to derive the best weightings.

Utah is Ending Homelessness by Giving People Homes | NationofChange

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URL:http://www.nationofchange.org/utah-ending-homelessness-giving-people-homes-1390056183


Earlier this month, Hawaii State representative Tom Bower (D) began walking the streets of his Waikiki district with a sledgehammer, and smashing shopping carts used by homeless people. “Disgusted” by the city’s chronic homelessness problem, Bower decided to take matters into his own hands — literally. He also took to rousing homeless people if he saw them sleeping at bus stops during the day.

Bower’s tactics were over the top, and so unpopular that he quickly declared “Mission accomplished,” and retired his sledgehammer. But Bower’s frustration with his city’s homelessness problem is just an extreme example of the frustration that has led cities to pass measures that effective deal with the homeless by criminalizing homelessness.

  • City council members in Columbia, South Carolina, concerned that the city was becoming a “magnet for homeless people,” passed an ordinance giving the homeless the option to either relocate or get arrested. The council later rescinded the ordinance, after backlash from police officers, city workers, and advocates.
  • Last year, Tampa, Florida — which had the most homeless people for a mid-sized city — passed an  ordinance allowing police officers to arrest anyone they saw sleeping in public, or “storing personal property in public.” The city followed up with a ban on panhandling downtown, and other locations around the city.
  • Philadelphia took a somewhat different approach, with a law banning the feeding of homeless people on city parkland. Religious groups objected to the ban, and announced that they would not obey it.
  • Raleigh, North Carolina took the step of asking religious groups to stop their longstanding practice of feeding the homeless in a downtown park on weekends. Religious leaders announced that they would risk arrest rather than stop.

This trend makes Utah’s accomplishment even more noteworthy. In eight years, Utah has quietly reduced homelessness by 78 percent, and is on track to end homelessness by 2015.

How did Utah accomplish this? Simple. Utah solved homelessness by giving people homes. In 2005, Utah figured out that the annual cost of E.R. visits and jail says for homeless people was about $16,670 per person, compared to $11,000 to provide each homeless person with an apartment and a social worker. So, the state began giving away apartments, with no strings attached. Each participant in Utah’s Housing First program also gets a caseworker to help them become self-sufficient, but the keep the apartment even if they fail. The program has been so successful that other states are hoping to achieve similar results with programs modeled on Utah’s.

It sounds like Utah borrowed a page from Homes Not Handcuffs, the 2009 report by The National Law Center on Homelessness & Poverty and The National Coalition for the Homeless. Using a 2004 survey and anecdotal evidence from activists, the report concluded that permanent housing for the homeless is cheaper than criminalization. Housing is not only more human, it’s economical.

This happened in a Republican state! Republicans in Congress would probably have required the homeless to take a drug test before getting an apartment, denied apartments to homeless people with criminal records, and evicted those who failed to become self-sufficient after five years or so. But Utah’s results show that even conservative states can solve problems like homelessness with decidedly progressive solutions.

Setting OmniGraphSketcher Free - The Omni Group

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URL:http://www.omnigroup.com/blog/setting-omnigraphsketcher-free


As you may know, last summer we made the difficult decision to stop selling OmniGraphSketcher and focus on our other applications. As part of this decision we elected to remove the app from our website instead of continuing to offer it as a free product as we have done with other apps in the past. Why? People have continued to use those free apps, but we don’t have enough time to work on them. That means that when bugs crop up (usually in new OS releases), we aren’t able to fix them in a timely manner and everyone ends up frustrated.

With OmniGraphSketcher, we’ve decided to go a different route: open source. Open source means everyone has access to OmniGraphSketcher’s code, and anyone who is so inclined can work on it. Bug fixes are no longer dependent on our CEO taking vacation time, and new users can discover the app without generating support costs for an project that is no longer a revenue stream.

We’re aware that there are drawbacks to open source software, but we’re convinced that setting GraphSketcher free is the best option going forward. We’re proud of the work that we did on OmniGraphSketcher, and we’d love it if as many people as possible were able to use it. In fact, the support that we received for OmniGraphSketcher after we discontinued it is one of the reasons we think this project will work well.

For non-developers, a download of the Mac app is available from the new project’s home on GitHub. On the iOS side, submission to the App Store is dependent on a couple of trickier things, but we’re hoping that GraphSketcher will make it back there without too much delay.

If you’re a developer who’s interested in poking around the GraphSketcher source code, just:

git clone --recursive git://github.com/graphsketcher/GraphSketcher

GraphSketcher relies on our existing open source frameworks, but building the app is still as simple as cloning, opening the workspace for the app you’d like to build (Mac or iPad), and hitting the play button.

So, while it’s not the end of the road for GraphSketcher, we are putting OmniGraphSketcher to bed. If you’re a fan of the old app we hope you’ll check out the new project and post your feedback to GitHub for future contributors to take a look at. And of course our support humans will continue to provide assistance to paid customers of OmniGraphSketcher for Mac and iPad on the versions of OS X and iOS which they support.

The New Aaron Swartz Documentary at Sundance : The New Yorker

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URL:http://www.newyorker.com/online/blogs/culture/2014/01/the-new-aaron-swartz-documentary-at-sundance.html


“The Internet’s Own Boy,” a documentary about the life and death of Aaron Swartz, premièred on Monday at the Sundance Film Festival, where it received a standing ovation. The life of Swartz as a coder and an Internet thinker is well known. A believer in free access to knowledge, in 2010 Swartz installed a computer in an M.I.T. supply closet and downloaded a large number of old academic articles. He was detected, caught, and charged by a federal prosecutor with thirteen felonies; in January of 2013, before his trial, Swartz killed himself. The documentary, shot in the course of that year, gives us relatively little new information about the legal controversy, but it is deeply revealing about who Swartz was.

The film confirms what everyone has said about Swartz—that he was difficult, foolish, and self-important in a way that is particular to smart young men, and that he was smart, idealistic, and vulnerable. He was one of those people who, beginning early in life, question everything, and notice how many of the answers are absurd. That instinct took him to the edge of society, like so many brilliant misfits, a disproportionate number of whom have created the American tech industry.

Swartz’s inability to adhere to social norms is well represented onscreen. For most of us, small talk and repetitious homework are mere annoyances, but for Aaron Swartz they were torture. As a freshman at Stanford, he preferred reading books to sitting with people in the dining hall, and, when asked if he was abnormal, told people that he found them abnormal for not preferring books. He left college after a year and helped to develop Reddit, but, after Wireds acquisition of the site, he found that he couldn’t stand the absurdities of a work environment any more easily than those of college.

Almost as if he knew he would die young, Swartz found it hard to waste time on the kind of make-work and nonsense that forms so much of a normal life. And so he escaped from schools and office environments into the online world, where he could do something worthwhile: change the status quo, using code. As a teen-age programmer, he made serious contributions to RSS, and he helped Lawrence Lessig to code Creative Commons.

Many misunderstood loners seek to quit society altogether, but Swartz did not. He seemed to crave being understood, and the footage of him chatting and relaxing with his girlfriends make for the lightest scenes in the film. He could connect with other people, and many of those who knew him well loved him deeply, even if they found him impossible. Instead of moving to the desert, Swartz felt an urgent need to do something of public significance. Wherever that feeling comes from, it can be inescapable. While engendering an admirable idealism, it also created Swartz’s most unpleasant trait, an exaggerated sense of self-importance, which the film does not hide.

Mixing coding with a sense of public purpose, Swartz spent his short life launching one project after another—little code bombs designed, in ways large and small, to change the way the world is. He had the quintessential programmer’s instinct: If you notice something lousy or absurd, instead of just complaining, why not fix it? That instinct has catalyzed tech projects from the personal computer to the search engine; in Swartz’s case, the projects were political and social instead of technical. Among other things, he wanted to liberate information that he thought was wrongly imprisoned, make life safer for whistle-blowers, and fight political corruption. Some of his code bombs were duds, but others made a big difference. And, of course, one blew up in his face.

The footage of Swartz growing older and more handsome anchors the film, his stridency belied by his large, needy eyes. Swartz grew up in an age of total capture, meaning that there is video footage from most of his life—as a young boy climbing trees, as a precocious teen-ager sprouting facial hair, and as a scruffy young man speaking at political rallies. It is an intimate film, and by the end you feel that you know Swartz. The awareness that he will eventually take his own life makes it especially hard to watch him as a happy little boy, laughing and playing. The death is less a Hollywood drama than it is a slow-moving descent into despair, after Swartz is caught and charged, as Cory Doctorow puts it in the film, for “taking too many books out of the library.” A felony is a weighty thing for anyone, but Swartz, serious to a fault, saw conviction as a mark that would stain his life indelibly.

There is some commonality between Aaron Swartz and Christopher McCandless, who died in the Alaskan outback, the subject of Jon Krakauer’s book “Into the Wild.” Neither man could really accept the world, and both of them died young. But, unlike in McCandless’s case, the but-for cause of Swartz’s death was clear: a relentless federal prosecutor who piled on the felony charges and refused to drop them, despite the fact that the crime did no real damage, and that the database owner, JSTOR, had asked that the charges be dropped. Yes, Swartz took his own life, and he bears responsibility for that act. But, as the film shows, his prosecution was a cruel and unnecessary episode that is unworthy of a country that calls itself free.

Swartz’s suicide is so dark that the director Brian Knappenberger tries to end the documentary on a slightly positive note. As protestors march, he notes that the reform measure called Aaron’s Law was introduced in Congress in 2013. Its purpose is to improve the outrageous statute used to prosecute Swartz, the Computer Fraud and Abuse Act, which makes much of the American population potential felons and entrusts our continued freedom to prosecutorial forbearance. The ending may make the audience feel better, but Aaron’s Law, in reality, has gone nowhere, thanks to opposition by firms like Oracle and the Justice Department. The cloud, in this case, has no silver lining.

Congressional inaction is hardly news, but here the fault lies with the White House as well. The Obama Administration has considerable leeway for reform; as with the N.S.A.’s overreach, it could, at a minimum, hold a review of C.F.A.A. prosecution and consider some limits in how the law is used. But after holding a symbolic White House meeting last spring in response to an online petition, the Administration has done nothing. Aaron, I am sorry to say, has died in vain.

Read Larissa MacFarquhar’s Profile of Aaron Swartz.


Ivan Ristić: SSL Labs: Stricter security requirements for 2014

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URL:http://blog.ivanristic.com/2014/01/ssl-labs-stricter-security-requirements-for-2014.html


January 21, 2014

Today, we're releasing a new version of SSL Rating Guide as well as a new version of SSL Test to go with it. Because the SSL/TLS and PKI ecosystem continues to move at a fast pace, we have to periodically evaluate our rating criteria to keep up.

We have made the following changes:

  • Support for TLS 1.2 is now required to get an A. If this protocol version is not supported, the grade is capped at B. Given that, according to SSL Pulse, TLS 1.2 is supported by only about 20% servers, we expect this change to affect a large number of assessments.
  • Keys below 2048 bits are now considered weak, with the grade capped at B.
  • Keys below 1024 bits are now considered insecure, and given an F.
  • MD5 certificate signatures are now considered insecure, and given an F.
  • We introduce two new grades, A+ and A-, to allow for finer grading. This change allows us to reduce the grade slightly, when we don't want to reduce it to a B, but we still want to show a difference. More interestingy, we can now reward exceptional configuations.
  • We also introduce a concept of warnings; a server with good configuration, but with one ore more warnings, is given a reduced grade A-.
  • Servers that do not support Forward Secrecy with our reference browsers are given a warning.
  • Servers that do not support secure renegotiation are given a warning.
  • Servers that use RC4 with TLS 1.1 or TLS 1.2 protocols are given a warning. This approach allows those who are still concerned about BEAST to use RC4 with TLS 1.0 and earlier protocols (supported by older clients), but we want them to use better ciphers with protocols that are not vulnerable to BEAST. Almost all modern clients now support TLS 1.2.
  • Servers with good configuration, no warnings, and good support for HTTP Strict Transport Security (long max-age is required), are given an A+.

I am very happy that our rating approach now takes into account some very important features, such as TLS 1.2, Forward Secrecy, and HSTS. Frankly, these changes have been overdue. We originally meant to have all of the above in a major update to the rating guide, but we ran out of time, and decided to implement many of the ideas in a patch release.

MY NEXT BOOK: If you like this blog post, you will loveBulletproof SSL/TLS and PKI. This book will contain everything you need to know about SSL/TLS and PKI for web application development and deployment, covering both theory and practice. An early release will be available soon.

In the meantime, go download and read my OpenSSL Cookbook ebook. It's free.

How I Went From 100 To 0 Things (Or How I Was Robbed of All My Stuff)

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URL:http://levels.io/100-to-0-things/


It took 6 months for me to get from 300 to 100 things.

It took just 5 minutes for me to get from 100 to 0 things.

I just spent the last 9 months traveling and working in Thailand, Vietnam, Singapore, Myanmar, Hong Kong and China from just my backpack. I worked on my startups and had an amazing time full of adventures.

Last week the political unrest in Bangkok became a little too rowdy for my tastes when grenades started being thrown around (more on that in another post). So I decided to fly back to the safe haven of my birth country, Holland, and wait the situation out. Since Thailand is my favorite place to stay, if that gets unstable, my options quickly run out. Luckily, my parents had a nice place in the east of the country where I could stay for awhile. It’s big, safe and in a pretty silent town with fast internet. So a good place to work and it’d be fun to see them again!

Returning from some of the most dirty, wild and dodgy (and fun) places all over Asia, I had never had a single thing stolen from me. Now stricken by irony, it took exactly 7 days to lose all my things in my parent’s home.

Recap

I woke up at 8AM to my mom screaming in shock that her $2k ring and other jewelry was gone. Thieves had broken into the door and had walked straight into the front room where literally all my stuff was and then proceeded to scavenge the rest of the house.

Instantly, I ran downstairs to see if my stuff was still in the front room where I had set up my office. As I’m a minimalist, I don’t have a lot of stuff. A MacBook Pro, an iPhone, some credit cards and clothes.

Except for my clothes and toothbrush, they’d taken everything. MacBook, iPhone, debit and credit cards and much, much more.

I was in shock and we all were. We called the police and tried to record all the places the burglars had been.

Digital lockdown

With my MacBook gone, it now meant the thieves had open access into my entire digital life. Even though my MacBook had a password, it’s easy to go around it. I now had to lock everything down immediately, from my personal and work mail to my YouTube accounts, my online businesses to my Linode servers and my Bitcoin wallet (with substantial funds in there). Since they had my iPhone too, they now also had potential access to my passwords manager as well as all my two-factor codes (on the Google Authenticator app). Even though my iPhone was secured by a passcode, everything is crackable these days.

In short, in just minutes, they could now destroy my entire personal life, destroy my online businesses and proceed with identity theft (taking out credit cards and borrow money) and maybe even bankrupt me. I’ve seen these things happen to other people before.

I scrambled as it had been a few hours since the break-in. There was one computer left in the house on which I immediately started changing all my passwords and resetting my two-factor authentication of all my online accounts. From Gmail to iCloud to YouTube, It literally took 12 hours to reset everything, and I still missed a few accounts.

While doing this, I actually thought this would make for a great start-up. Have an emergency number you can call which auto-locks all your accounts. Although then, you’d add an extra security risk with them having all your passwords. But it shouldn’t cost 12 hours to change all your accounts, like it did with me. There’s people with even more user accounts than me. And what about break-ins at businesses?

My files


The next thing to hit me was that the burglar now had access to 512 GB of my files on my MacBook’s drive. As I said, everything I do is digital as I’m 100% paperless, and from my work itself, my work’s administration, bills, passport copies, my photos and videos, I had to come to terms that it was now potentially on the street. That made me paranoid. I figured, it’d more probable that the thieves would simply wipe the MacBook and put it on eBay. Lucky for me, I’m not important or famous. But does that really matter? Identity theft happens to common people and is a huge industry with lots of money to be made, especially if you have someone’s entire hard drive full of their administration, papers and even passport copies.

Our lives have become completely digital

The risk has become clear to me now. Since we store everything digitally now, it means when a burglar takes a laptop, they pretty much have someone’s life. A lot of us literally cannot have our devices being stolen. It’s a worse danger now than having our houses burglarized. Our houses just have stuff. Our devices have our entire life in digital form. That’s scary as fuck.

If someone takes your laptop, they have you by the balls (or ovaries).

From shock to fear

I went through the classic stages. First I was in shock of just losing everything I owned. Then in denial where I shrugged the whole burglary off with “it’s just stuff, it’s all replaceable” and literally felt great. The next day, that became anger at the thieves. I wanted to go outside, find them and beat them up. And then fear.

The fear hit me when I started thinking about the fact that the night of the break-in, before I went to bed, I had closed my bedroom door. When I woke up, my bedroom door was wide open. My parents nor me opened my door. It was the burglars who had been in my bedroom at 5am while I was asleep. Deep asleep and with ear plugs in I had no recollection of it. That in itself scared me shitless. I’m fine with a thief standing in my house when I’m awake, as I might be able to jump them. But not being able to know who’s there when I’m sleeping means I just lost any sense of safety when sleeping.

Luckily, that subsided after a week after we reinforced the entire house, installed heavy locks everywhere, and installed an alarm system.

The ordeal of replacing everything

It takes weeks to replace all your stuff when it gets stolen, not to mention restoring all your backups and getting your system in the same state it was before it was stolen. All in all, the cost of work in getting your life back in order is a multiple of the actual materialistic cost you get back from insurance. It’s an ordeal.

So, what did I learn?

iCloud and Prey are pretty useless


I had Apple iCloud’s “Find My Device” and Prey enabled on both my iPhone and MacBook in case they were stolen, but to no avail. Thieves aren’t born yesterday. They know they shouldn’t connect to WiFi, thereby making it impossible for your device to alert iCloud or Prey of its location. Not a single report came in. They’re good services, but if the thieves are smart the odds you’re getting anything back are slim.

Enable file encryption

I didn’t really trust file encryption because I thought I might lose files because of it and therefore I never enabled Mac OSX’s built-in FileVault hard drive encryption. I should have though. It’d save me from worrying about who’s going through all my files now.

Store backups away from your device

My backup drive was literally NEXT to my MacBook. By sheer luck, I had just backed up my internal drive the day before and the thieves did not take it. They might have had some mercy after all? Thank the holy powers that may be.

I did have 4 copies of backups stored off-site around the country but they were all 9 months old (since I had just returned). In any case, I should’ve never ever left my ONLY backup drive next to my computer.

Use online backups


I didn’t have a cloud backup because I don’t trust a third party with my data. These days, the most popular web services get hacked and I think it’s just a matter of time before the first backup service leaks terabytes of customer’s files. Services like BackBlaze are great since they offer 265-bit AES client-side encryption. But then, there’s stories that the NSA might or might not have a backdoor in the encryption. The thing is, I might have to consider online backups soon, as an extra defense of not losing my files due to theft like this.

Keep stuff in eye-sight, wherever you are


For the last 9 months while I was traveling, I always had my stuff on my body (in my backpack) or in eye sight. I never got robbed and I learnt that getting robbed is a lot harder than we think. You can avoid dodgy areas and if you feel unsafe, you can simply leave. But when you haven’t got your stuff near you, there’s always opportunity for people to break-in and take it.

There had never been any break-ins in my parents house since they bought it 30 years ago, and I wrongly assumed there’d never be. So now, I can’t really assume any place is safe and always have to keep my valuables near me or locked up. Which is a fucking shame.

Conclusion

Today, I lost all the stuff I owned, but more importantly I lost a little faith in the world that I thought I’d somehow always be safe. On the other hand, I shouldn’t be a cry-baby about it. It took me over 27 years, or about 10,000 days, to get robbed. That’s still a 1 in 10,000 probability. Pretty good. So this was bound to happen at some point.

The irony doesn’t escape me that in all my years of traveling the world, I haven’t been robbed a single time. And when I come home, it took just 7 days to lose all my stuff.

At least I hit the holy grail of minimalism today. No stuff! Now guess what? Feels like shit!

Note: Let me know any tips you have for me in thethread on Hacker News and consider this an order to encrypt and backup your drives :)

P.S. I'm on Twitter now with more of my adventures.

Debunking Princeton

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URL:https://www.facebook.com/notes/mike-develin/debunking-princeton/10151947421191849


Like many of you, we were intrigued by a recent article by Princeton researchers predicting the imminent demise of Facebook. Of particular interest was the innovative use of Google search data to predict engagement trends, instead of studying the actual engagement trends. Using the same robust methodology featured in the paper, we attempted to find out more about this "Princeton University" - and you won't believe what we found!

In keeping with the scientific principle "correlation equals causation," our research unequivocally demonstrated that Princeton may be in danger of disappearing entirely. Looking at page likes on Facebook, we find the following alarming trend:

Now, Facebook isn't the only repository of human knowledge out there. A search of Google Scholar revealing a plethora of scholarly articles of great scholarliness turned up the following results, showing the percentage of articles matching the query "Princeton" by year:

The trend is similarly alarming: since 2009, the percentage of "Princeton" papers in journals has dropped dramatically.

Of course, Princeton University is primarily an institution of higher learning - so as long as it has students, it'll be fine. Unfortunately, in investigating this, we found a strong correlation between the undergraduate enrollment of an institution and its Google Trends index:

Sadly, this spells bad news for this Princeton entity, whose Google Trends search scores have been declining for the last several years:

This trend suggests that Princeton will have only half its current enrollment by 2018, and by 2021 it will have no students at all, agreeing with the previous graph of scholarly scholarliness. Based on our robust scientific analysis, future generations will only be able to imagine this now-rubble institution that once walked this earth.

       

While we are concerned for Princeton University, we are even more concerned about the fate of the planet — Google Trends for "air" have also been declining steadily, and our projections show that by the year 2060 there will be no air left:

As previous researchers [J. Sparks, 2008] have expressed in the past, this will have grievous consequences for the fate of all humanity, not just our academic colleagues in New Jersey.

Although this research has not yet been peer-reviewed, every Like for this post counts as a peer review. Start reviewing!

P.S. We don’t really think Princeton or the world’s air supply is going anywhere soon. We love Princeton (and air). As data scientists, we wanted to give a fun reminder that not all research is created equal – and some methods of analysis lead to pretty crazy conclusions.

Research by Mike Develin, Lada Adamic, and Sean Taylor.

The Techtopus: How Silicon Valley’s most celebrated CEOs conspired to drive down 100,000 tech engineers’ wages | PandoDaily

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URL:http://pando.com/2014/01/23/the-techtopus-how-silicon-valleys-most-celebrated-ceos-conspired-to-drive-down-100000-tech-engineers-wages/


By Mark Ames
On January 23, 2014

In early 2005, as demand for Silicon Valley engineers began booming, Apple’s Steve Jobs sealed a secret and illegal pact with Google’s Eric Schmidt to artificially push their workers wages lower by agreeing not to recruit each other’s employees, sharing wage scale information, and punishing violators. On February 27, 2005, Bill Campbell, a member of Apple’s board of directors and senior advisor to Google, emailed Jobs to confirm that Eric Schmidt “got directly involved and firmly stopped all efforts to recruit anyone from Apple.”

Later that year, Schmidt instructed his Sr VP for Business Operation Shona Brown to keep the pact a secret and only share information “verbally, since I don’t want to create a paper trail over which we can be sued later?”

These secret conversations and agreements between some of the biggest names in Silicon Valley were first exposed in a Department of Justice antitrust investigation launched by the Obama Administration in 2010. That DOJ suit became the basis of a class action lawsuit filed on behalf of over 100,000 tech employees whose wages were artificially lowered — an estimated $9 billion effectively stolen by the high-flying companies from their workers to pad company earnings — in the second half of the 2000s. Last week, the 9th Circuit Court of Appeals denied attempts by Apple, Google, Intel, and Adobe to have the lawsuit tossed, and gave final approval for the class action suit to go forward. A jury trial date has been set for May 27 in San Jose, before US District Court judge Lucy Koh, who presided over the Samsung-Apple patent suit.

In a related but separate investigation and ongoing suit, eBay and its former CEO Meg Whitman, now CEO of HP, are being sued by both the federal government and the state of California for arranging a similar, secret wage-theft agreement with Intuit (and possibly Google as well) during the same period.

The secret wage-theft agreements between Apple, Google, Intel, Adobe, Intuit, and Pixar (now owned by Disney) are described in court papers obtained by PandoDaily as “an overarching conspiracy” in violation of the Sherman Antitrust Act and the Clayton Antitrust Act, and at times it reads like something lifted straight out of the robber baron era that produced those laws. Today’s inequality crisis is America’s worst on record since statistics were first recorded a hundred years ago — the only comparison would be to the era of the railroad tycoons in the late 19th century.

Shortly after sealing the pact with Google, Jobs strong-armed Adobe into joining after he complained to CEO Bruce Chizen that Adobe was recruiting Apple’s employees. Chizen sheepishly responded that he thought only a small class of employees were off-limits:

I thought we agreed not to recruit any senior level employees…. I would propose we keep it that way. Open to discuss. It would be good to agree.

Jobs responded by threatening war:

OK, I’ll tell our recruiters they are free to approach any Adobe employee who is not a Sr. Director or VP. Am I understanding your position correctly?

Adobe’s Chizen immediately backed down:

I’d rather agree NOT to actively solicit any employee from either company…..If you are in agreement, I will let my folks know.

The next day, Chizen let his folks — Adobe’s VP of Human Resources — know that “we are not to solicit ANY Apple employees, and visa versa.” Chizen was worried that if he didn’t agree, Jobs would make Adobe pay:

if I tell Steve [Jobs] it’s open season (other than senior managers), he will deliberately poach Adobe just to prove a point. Knowing Steve, he will go after some of our top Mac talent…and he will do it in a way in which they will be enticed to come (extraordinary packages and Steve wooing).

Indeed Jobs even threatened war against Google early 2005 before their “gentlemen’s agreement,” telling Sergey Brin to back off recruiting Apple’s Safari team:

if you [Brin] hire a single one of these people that means war.

Brin immediately advised Google’s Executive Management Team to halt all recruiting of Apple employees until an agreement was discussed.

In the geopolitics of Silicon Valley tech power, Adobe was no match for a corporate superpower like Apple. Inequality of the sort we’re experiencing today affects everyone in ways we haven’t even thought of — whether it’s Jobs bullying slightly lesser executives into joining an illegal wage-theft pact, or the tens of thousands of workers whose wages were artificially lowered, transferred into higher corporate earnings, and higher compensations for those already richest and most powerful to begin with.

Over the next two years, as the tech industry entered another frothing bubble, the secret wage-theft pact which began with Apple, Google and Pixar expanded to include Intuit and Intel. The secret agreements were based on relationships, and those relationships were forged in Silicon Valley’s incestuous boards of directors, which in the past has been recognized mostly as a problem for shareholders and corporate governance advocates, rather than for the tens of thousands of employees whose wages and lives are viscerally affected by their clubby backroom deals. Intel CEO Paul Otellini joined Google’s board of directors in 2004, a part-time gig that netted Otellini $23 million in 2007, with tens of millions more in Google stock options still in his name — which worked out to $464,000 per Google board event if you only counted the stock options Otellini cashed out — dwarfing what Otellini made off his Intel stock options, despite spending most of his career with the company.

Meanwhile, Eric Schmidt served on Apple’s board of directors until 2009, when a DoJ antitrust investigation pushed him to resign. Intuit’s chairman at the time, Bill Campbell, also served on Apple’s board of directors, and as official advisor — “consigliere” — to Google chief Eric Schmidt, until he resigned from Google in 2010. Campbell, a celebrated figure (“a quasi-religious force for good in Silicon Valley”) played a key behind-the-scenes role connecting the various CEOs into the wage-theft pact. Steve Jobs, who took regular Sunday walks with Campbell near their Palo Alto homes, valued Campbell for his ability “to get A and B work out of people,” gushing that the conduit at the center of the $9 billion wage theft suit, “loves people, and he loves growing people.”

Indeed. Eric Schmidt has been, if anything, even more profuse in his praise of Campbell. Schmidt credits Campbell for structuring Google when Schmidt was brought on board in 2001:

His contribution to Google — it is literally not possible to overstate. He essentially architected the organizational structure.

Court documents show it was Campbell who first brought together Jobs and Schmidt to form the core of the Silicon Valley wage-theft pact. And Campbell’s name appears as the early conduit bringing Intel into the pact with Google:

Bill Campbell (Chairman of Intuit Board of Directors, Co-Lead Director of Apple, and advisor to Google) was also involved in the Google-Intel agreement, as reflected in an email exchange from 2006 in which Bill Campbell agreed with Jonathan Rosenberg (Google Advisor to the Office of CEO and former Senior Vice President of Product Management) that Google should call [Intel CEO] Paul Otellini before making an offer to an Intel employee, regardless of whether the Intel employee first approached Google.

Getting Google on board with the wage-theft pact was the key for Apple from the start — articles in the tech press in 2005 pointed at Google’s recruitment drive and incentives were the key reason why tech wages soared that year, at the highest rate in well over a decade.

Campbell helped bring in Google, Intel, and, in 2006, Campbell saw to it that Intuit — the company he chaired — also joined the pact.

From the peaks of Silicon Valley, Campbell’s interpersonal skills were magical and awe-inspiring, a crucial factor in creating so much unimaginable wealth for their companies and themselves. Jobs said of Campbell:

There is something deeply human about him.

And Schmidt swooned:

He is my closest confidant…because he is the definition of trust.

Things — and people — look very different when you’re down in the Valley. In the nearly 100-page court opinion issued last October by Judge Koh granting class status to the lawsuit, Campbell comes off as anything but mystical and “deeply human.” He comes off as a scheming consigliere carrying out some of the drearier tasks that the oligarchs he served were constitutionally not so capable of arranging without him.

But the realities of inequality and capitalism invariably lead to mysticism of this sort, a natural human response to the dreary realities of concentrating so much wealth and power in the hands of a dozen interlocking board members at the expense of 100,000 employees, and so many other negative knock-off effects on the politics and culture of the world they dominate.

One of the more telling elements to this lawsuit is the role played by “Star Wars” creator George Lucas, who emerges as the Obi-Wan Kenobi of the wage-theft scheme. It’s almost too perfectly symbolic that Lucas — the symbiosis of Baby Boomer New Age mysticism, Left Coast power, political infantilism, and dreary 19th century labor exploitation — should be responsible for dreaming up the wage theft scheme back in the mid-1980s, when Lucas sold the computer animation division of Lucasfilm, Pixar, to Steve Jobs.

As Pixar went independent in 1986, Lucas explained his philosophy about how competition for computer engineers violated his sense of normalcy — and profit margins. According to court documents:

George Lucas believed that companies should not compete against each other for employees, because ‘[i]t’s not normal industrial competitive situation.’ As George Lucas explained, ‘I always — the rule we had, or the rule that I put down for everybody,’ was that ‘we cannot get into a bidding war with other companies because we don’t have the margins for that sort of thing.’

Translated, Lucas’ wage-reduction agreement meant that Lucasfilm and Pixar agreed to a) never cold call each other’s employees; b) notify each other if making an offer to an employee of the other company, even if that employee applied for the job on his or her own without being recruited; c) any offer made would be “final” so as to avoid a costly bidding war that would drive up not just the employee’s salary, but also drive up the pay scale of every other employee in the firm.

Jobs held to this agreement, and used it as the basis two decades later to suppress employee costs just as fierce competition was driving up tech engineers’ wages.

The companies argued that the non-recruitment agreements had nothing to do with driving down wages. But the court ruled that there was “extensive documentary evidence” that the pacts were designed specifically to push down wages, and that they succeeded in doing so. The evidence includes software tools used by the companies to keep tabs on pay scales to ensure that within job “families” or titles, pay remained equitable within a margin of variation, and that as competition and recruitment boiled over in 2005, emails between executives and human resources departments complained about the pressure on wages caused by recruiters cold calling their employees, and bidding wars for key engineers.

Google, like the others, used a “salary algorithm” to ensure salaries remained within a tight band across like jobs. Although tech companies like to claim that talent and hard work are rewarded, in private, Google’s “People Ops” department kept overall compensation essentially equitable by making sure that lower-paid employees who performed well got higher salary increases than higher-paid employees who also performed well.

As Intel’s director of Compensation and Benefits bluntly summed up the Silicon Valley culture’s official cant versus its actual practices,

While we pay lip service to meritocracy, we really believe more in treating everyone the same within broad bands.

The companies in the pact shared their salary data with each other in order to coordinate and keep down wages — something unimaginable had the firms not agreed to not compete for each other’s employees. And they fired their own recruiters on just a phone call from a pact member CEO.

In 2007, when Jobs learned that Google tried recruiting one of Apple’s employees, he forwarded the message to Eric Schmidt with a personal comment attached: “I would be very pleased if your recruiting department would stop doing this.”

Within an hour, Google made a “public example” by “terminating” the recruiter in such a manner as to “(hopefully) prevent future occurrences.”

Likewise, when Intel CEO Paul Otellini heard that Google was recruiting their tech staff, he sent a message to Eric Schmidt: “Eric, can you pls help here???”

The next day, Schmidt wrote back to Otellini: “If we find that a recruiter called into Intel, we will terminate the recruiter.”

One of the reasons why non-recruitment works so well in artificially lowering workers’ wages is that it deprives employees of information about the job market, particularly one as competitive and overheating as Silicon Valley’s in the mid-2000s. As the companies’ own internal documents and statements showed, they generally considered cold-calling recruitment of “passive” talent — workers not necessarily looking for a job until enticed by a recruiter — to be the most important means of hiring the best employees.

Just before joining the wage-theft pact with Apple, Google’s human resources executives are quoted sounding the alarm that they needed to “dramatically increase the engineering hiring rate” and that would require “drain[ing] competitors to accomplish this rate of hiring.” One CEO who noticed Google’s hiring spree was eBay CEO Meg Whitman, who in early 2005 called Eric Schmidt to complain, “Google is the talk of the Valley because [you] are driving up salaries across the board.” Around this time, eBay entered an illegal wage-theft non-solicitation scheme of its own with Bill Campbell’s Intuit, which is still being tried in ongoing federal and California state suits.

Google placed the highest premium on “passive” talent that they cold-called because “passively sourced candidates offer[ed] the highest yield,” according to court documents. The reason is like the old Groucho Marx joke about not wanting to belong to a club that would let you join it — workers actively seeking a new employer were assumed to have something wrong with them; workers who weren’t looking were assumed to be the kind of good happy talented workers that company poachers would want on their team.

For all of the high-minded talk of post-industrial technotopia and Silicon Valley as worker’s paradise, what we see here in stark ugly detail is how the same old world scams and rules are still operative.

Court documents below…

October 24, 2013 Class Cert Order


[Illustration by Brad Jonas for Pando]

Microsoft Investor Relations - Press Releases

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URL:http://www.microsoft.com/investor/EarningsAndFinancials/Earnings/PressReleaseAndWebcast/FY14/Q2/default.aspx


Earnings Release FY14 Q2

Related Information

FY14 Earnings Release Schedule

Q3-Thursday, April 24

IMPORTANT NOTICE TO USERS (summary only, click here for full text of notice); All information is unaudited unless otherwise noted or accompanied by an audit opinion and is subject to the more comprehensive information contained in our SEC reports and filings. We do not endorse third-party information. All information speaks as of the last fiscal quarter or year for which we have filed a Form 10-K or 10-Q, or for historical information the date or period expressly indicated in or with such information. We undertake no duty to update the information. Forward-looking statements are subject to risks and uncertainties described in our Forms 10-Q and 10-K.

Bayer CEO Marijn Dekkers explains: Nexavar cancer drug is for "western patients who can afford it.” | Knowledge Ecology International

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URL:http://keionline.org/node/1910


Today health advocates were shocked by the direct and appalling statements attributed to Bayer CEO Marijn Dekkers. Published in Businessweek on January 21, 2014 and written by Bloomberg reporter Ketaki Gokhale, a news story about disputes over drug patents (link here) ended with an account of the India compulsory license on the cancer drug Nexavar, and practically exploded. Dekker is quoted as saying Bayer did not intend the cancer drug to be sold to cancer patients in India, adding “We developed it for western patients who can afford it.” From the Bloomberg/Businessweek story:

Under India’s patent laws, compulsory licenses can be awarded for some products still under patent if the original isn’t available locally at a reasonable price. Natco Pharma Ltd. (NTCPH) applied directly to India’s patents office and was awarded the nation’s first compulsory license in March 2012 to make a copy of Bayer’s Nexavar cancer drug at a 97 percent discount to the original product. In March last year, Bayer lost its bid to stop Natco from making the generic drug and is appealing the decision at the Mumbai High Court. Bayer Chief Executive Officer Marijn Dekkers called the compulsory license “essentially theft.” “We did not develop this medicine for Indians,” Dekkers said Dec. 3. “We developed it for western patients who can afford it.”

Apparently the December 3, 2013 quote is from an earlier largely overlooked event hosted by the FT. For more context, note that the Bayer price for Nexavar was $65 thousand USD, per year, in India, and that Bayer is currently arguing that the $65 thousand price is "reasonably affordable" to the India Supreme Court. Dekker's comments will likely be quoted extensively in India, and I would not be surprised to see them quoted in a decision rejecting the Bayer appeal. It is worth noting that Dekkers, who holds both Dutch and U.S. citizenship, is a member of the Board of Directors of General Electric, another company that takes a very hard line on intellectual property issues. (See, for example: http://keionline.org/node/1822).

The U.S. government has weighed in on the side of Bayer in the patent case, at the highest levels of the India and the US governments. For more on background on the Nexavar case, see: KEI's February 17, 2013 Statement in Nexavar India compulsory licensing case. http://keionline.org/node/1657

And: http://keionline.org/search/node/nexavar

Article 44


Model Your Users: Algorithms Behind the Minuum Keyboard | The Minuum Keyboard Project

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URL:http://minuum.com/model-your-users-algorithms-behind-the-minuum-keyboard/


When you’re creating a new keyboard technology, there’s a ton of work that goes into both the interaction design, and into the algorithms behind the scenes. While the design of our keyboard is best understood simply by using it, the real “magic” that makes our one-dimensional keyboard possible lies in the statistical algorithms that make it tick.

If you haven’t already seen or used the Minuum keyboard, the brief summary is that we let you compress the conventional keyboard down to just one row of keys, opening up the possibility of typing anywhere where you can measure one dimension of input.

 

By shrinking the keyboard in this way we soon had to grapple with a basic fact: human input is imprecise, and the faster you type the more imprecise it gets. Rather than trying to improve user precision, we instead embrace sloppy typing.

This only works because we use disambiguation in addition to auto-correction. While “auto-correction” implies that you made a mistake that needed correcting, “disambiguation” accepts the fundamental ambiguity of human interaction, and uses an understanding of language to narrow things down. Think of it like speech recognition: in a noisy bar, the problem isn’t that your friends are speaking incorrectly; human speech is ambiguous, and the noisiness of the environment sure doesn’t help. You can only understand them because you have prior knowledge of the sorts of things they are likely to say.

Which leads us into the wonderful world of…

Bayesian statistics!

Minuum combines two factors to evaluate a word, a spatial model which understands how precise you are when you tap on the keyboard (we perform user studies to measure this), and a language model which understands what words you’re likely to use (we build this from huge bodies of real-world text). If you tap on the keyboard five times, and those taps somewhat resemble the word “hello”, we use the following Bayesian equation to test how likely it is that you wanted the word “hello”:

Let’s break that equation down: the probability that you wanted the word “hello” given those taps, is proportional to the product of the spatial and language terms. The spatial term gives the likelihood that wanting to type the word “hello” could have led you to input that sequence of taps; the language term gives the probability that you would ever type the word “hello”.

Minuum’s job is to find the word that maximizes p(word|taps). In the example above, Minuum is generating a score for the word “hello”. To find the best word, Minuum would compare this score to the scores for other words, calculated the same way. The closer your taps are to the correct locations for a given word, the greater the spatial term for that word; the more common a word in English (or French, German, Italian or Spanish if you have one of those languages enabled) the greater the language term.

A simple spatial model

Minuum uses a fairly complicated spatial model (remember the spatial model represents how people tend to actually type on the keyboard). This model can handle many kinds of imprecision, such as extra and omitted characters. A simple model that works surprisingly well, however, is to treat the probability density of a tap as a Gaussian centered at the target character.

This shows that if you mean to type a “t”, the most likely point you tap on the keyboard is right on the “t”, but there is still a significant probability that you tap on a nearby location closer to the “v” or the “g”.

A simple language model

The simplest language model is just a count of word frequencies. Take a large body of text (a corpus), and count how many times each word shows up.

Word Frequency if 1,115,786 IV 5335

To compare two potential words, say “if” and “IV”, according to the above table “if” is around 200 times more likely to be typed than “IV”.

This simple model, like the simple spatial model, works quite well in practice. Further improvements can come from using context such as the word immediately before the current entry.

Word(s) Frequency what if 13,207 what of 1,380

The phrase “what if” is about ten times more common than “what of”, so even though “if” and “of” are both very common words, given the context “what”, we can confidently guess that “if” is the intended word.

Words are high-dimensional points

I understand problems best when I can picture them geometrically. My intuitive understanding of the disambiguation problem finally clicked when we had an insight: words are points in high-dimensional space, and typing is a search for those words! Skeptical? Let me explain.
Minuum is a single line, so tapping your finger on Minuum can be represented by one number, In the figure below, for instance, a tap on “q” could clock in between 0 and 0.04, and a tap on “p” at 0.98 to 1.

A continuum of letters from 0.0 from 1.0

A two-letter word, consists of two taps, and so can be represented as a pair of numbers. The word “an”, typed perfectly, is represented as {0.06, 0.67}, and the word “if” as {0.83, 0.40}. The figure belows shows the positions of some common 2-letter words in this “word space”.

The exact same logic applies to longer words: “and” is {0.06, 0.67, 0.29}, “minuum” is {0.79, 0.83, 0.67, 0.71, 0.71, 0.79}. Above three dimensions, unfortunately, it’s much harder to visualize.

A user’s sequence of taps is also a point in this word space, which we can call the input point. The “closer” a word’s point is to the input point, the higher that word will score in the spatial term of the Bayesian equation above. Odds are, whatever you meant to type is “nearby” to what you actually typed in this space.

So let’s visualize some words!

We can generate a full map of the top two-letter words recommended by Minuum, based on any possible pair of input taps; here, more common words tend to end up with larger areas. By hovering over the graph, you can see what other words would be recommended as alternative candidates.

Two-letter predictions with no context

Two-letter word predictions with previous word “what”

Toggle context
Toggle the context button above to see what happens when we use a better language model to account for the user having previously typed the word “what”. Clearly, “if” is more likely and “in” is less likely to be recommended when we account for context, because “what if” is more common than “what of”, while “what in” is less common than “what I’m”.

Of course, Minuum uses more context than just the previous word, and also learns your personal typing tendencies over time, so this picture is different for each user.

Statistical modelling for better interfaces

All this complexity allows Minuum to shed some constraints of conventional keyboards (working even as a one-row keyboard on a 1” screen!)

What does this show? That interfaces are better when they understand the user! Google Instant is awesome because it knows what you’re looking for after a couple keystrokes. Siri would be impossible without complex language modeling. Minuum can simplify keyboards only by combining strong spatial and language models of real human input. If you’re dealing with a complex interface, consider how you can statistically model user behaviour to simplify the interaction required.

The U.S. Crackdown on Hackers Is Our New War on Drugs | Wired Opinion | Wired.com

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URL:http://www.wired.com/opinion/2014/01/using-computer-drug-war-decade-dangerous-excessive-punishment-consequences/


Before Edward Snowden showed up, 2013 was shaping up as the year of reckoning for the much criticized federal anti-hacking statute, the Computer Fraud and Abuse Act (“CFAA”). The suicide of Aaron Swartz in January 2013 brought the CFAA into mainstream consciousness, so Congress held hearings about the case, and legislative fixes were introduced to change the law.

Recognizing the powerful capabilities of modern computing and networking has resulted in ‘cyber panic’ in legislatures and prosecutor offices across the country.

Finally, there seemed to be a newfound scrutiny of CFAA prosecutions and punishment for accessing computer data without or in excess of “authorization” — which affected everyone from Chelsea Manning to Jeremy Hammond to Andrew “Weev” Auernheimer (disclosure: I’m one of his lawyers on appeal). Not to mention less illustrious personalities and everyday users, such as people who delete cookies from their browsers.

But unfortunately, not much has changed; if anything, the growing recognition of the powerful capabilities of modern computing and networking has resulted in a “cyber panic” in legislatures and prosecutor offices across the country. Instead of reexamination, we’ve seen aggressive charges and excessive punishment.

This cyber panic isn’t just a CFAA problem. In the zeal to crack down on cyberbullying, legislatures have passed overbroad laws criminalizing speech clearly protected by the First Amendment. This comes after one effort to use the CFAA to criminalize cyberbullying — built on the premise that violating a website’s terms of service was unauthorized access, or the equivalent of hacking – was thrown out as unconstitutionally vague.

The panic has even spread to how crime is investigated. To prevent digital contraband from coming into the United States, border officials can now search electronic devices without any suspicion of wrongdoing. To get to illicit files on a seized computer, the government can force you to decrypt your computer and threaten you with jail for noncompliance. To get information about one customer, the FBI can demand a service provider turn over the key that unlocks communications from all of the service’s customers. And let’s not even get started on what the NSA has been up to.

The Problem of Excessive Punishment

There’s no doubt that there are good intentions here: to catch bad guys, keep people safe, and preserve some order in a chaotic and changing world. But this “cyber panic,” particularly with the excessive and aggressive use of the CFAA, comes with a real consequence: locking up people in prison for years.

Take the case of Matthew Keys, a former social media editor at Reuters, charged with violating the CFAA in federal court in Sacramento. He allegedly turned over the username and password of a server belonging to the Tribune Company to members of Anonymous, who made changes to the article of a headline in a Los Angeles Times story online. Among other changes, the headline was changed from “Pressure builds in House to pass tax-cut package” to “Pressure builds in House to elect CHIPPY 1337.” It seems like a clear-cut case of vandalism, a prank that caused some damage but little other harm.

Under California law, physical vandalism – like spray painting graffiti on a building — can be punished as either a misdemeanor or a felony, with probation available for both types of charges. If probation is granted, the longest sentence a defendant can serve as a condition of probation is one year in county jail.

But look at the punishment awaiting Keys. He didn’t get charged with a misdemeanor; he got indicted on three felony charges, for which he faces a harsh prison sentence. No, he won’t get anything close to the 10-year maximum. But a cursory calculation of his potential sentence under the federal sentencing guidelines suggest he’s looking at a sentence between 21 and 27 months — about three years of his life — if he decides to go to trial and loses.

Here are more details on how such sentencing works:

…Federal sentencing is based on two things: the seriousness of a crime and the person’s criminal history. The two factors are plotted on a table, with the y-axis a scale of 1 to 43 “levels” that determines the seriousness of a crime, and the x-axis a scale of I to VI that measures criminal history. At sentencing, the judge must determine both scores, plot them on the table, and determine the sentencing range in months, which the court can follow or disregard at its own discretion. …Someone like Keys, who has no criminal history, is in criminal history category I. The starting point for most CFAA crimes is level 6, which is low on the scale but can quickly increase. …Assuming the allegations in Key’s search warrant are correct, the Tribune company spent $17,650.40 to fix the damage, resulting in an increase of 4 levels for causing more than $10,000 and less than $30,000 in damage. Because Keys is charged with causing damage to a computer, he receives another 4 level increase. And because he likely abused a position of trust, he receives another 2 level increase, for a total offense level of 16 — which has a sentencing range between 21 and 27 months for a person in criminal history category I. (That places Keys in “Zone C” of the Sentencing Table, which means the Guidelines don’t authorize a grant of probation, though the judge could impose probation if she wanted to.)

As a country and a criminal justice system, we’ve been down this road of excessive punishment before: with drugs. Prosecutors and lawmakers need to take a step back and think long and hard about whether we’re going down the same road with their zeal towards computer crimes.

Hanni Fakhoury is a former federal public defender and a current Staff Attorney at the Electronic Frontier Foundation (EFF) who focuses on criminal law, privacy, and free speech litigation and advocacy. Follow him on Twitter @hannifakhoury.

For many years, there was a radical disparity in how federal law treated crack and powder cocaine. A person who possessed 5 grams of crack cocaine could be charged with a felony. But it took 500 grams of powder cocaine to get the same felony punishment. This 100-to-1 ratio was born in the 1980s, when Congress was concerned that crack — predominantly used in urban areas by people of color — was becoming an epidemic and a violent one at that.

This extreme disparity only ensured that a disproportionate amount of people of color ended up in prison. Receiving little rehabilitation while incarcerated and struggling to find work or otherwise reintegrate into society once released, convicts would return to crime, get caught, and be sentenced as a recidivist. That meant a longer jail sentence and the continuation of a destructive cycle.

But over the last few years, there has been significant progress towards narrowing this gap. In 2010, Congress passed — and President Obama signed— legislation that reduced the 100-to-1 ratio down to 18-to-1. Attorney General Eric Holder upped the ante this past summer, announcing a series of broader policy reforms that would work to reduce harsh drug sentences by giving prosecutors flexibility to avoid charging a defendant with crimes that carry mandatory minimum prison sentences. And at the end of last year, President Obama pardoned thirteen people and commuted the sentences of eight prisoners who were sentenced under the old ratio and were therefore serving long sentences for crack cocaine convictions.

These reforms took over 20 years. But as technology marches faster than the slow pace of legal change, we don’t have that kind of time to apply the lessons learned from the failed “war on drugs” experiment to the growing wave of computer crime prosecutions.

And It Doesn’t Even Work

The government’s mindset is that technology and the internet can wreak havoc. Disseminating the login credentials of a powerful media company to vandalize a few websites, for example, has the potential to cause more damage than spray-painting graffiti on a highway sign.

That is undoubtedly true. But will aggressive, excessive punishment really deter others here? This country’s experience with the war on drugs suggests the answer is a resounding no.

We shouldn’t let the government’s fear of computers justify disproportionate punishment.

The problem is pronounced with much of the politically motivated online crime that has splashed the headlines. As a generation of people who grew up plugged in and online realized there is no way to voice their complaints within the mainstream political establishment, they decided to take their protests to the medium they know best. Harsh punishment is only going to reinforce and harden that generation’s pessimism towards the government.

This is not to say that “anything goes” online or that crimes should go unpunished. But we need to question whether locking people up for long periods of time — without addressing the root concerns about concentrated political power, civil liberties abuses, and transparency — will have the effect of deterrence or worse yet, a hardened cynicism that perpetuates the endless cycle of punishment. That’s true of even non-politically motivated cybercrime, or really, all crime … whether it involves a computer or not.

* * *

There may be hope yet.

Recently, 11 members of the “PayPal 14,” a group of individuals affiliated with Anonymous who DDoS’d PayPal in 2010 to protest its refusal to process donations to Wikileaks, pleaded guilty to felony CFAA charges in federal court. But their sentences were put off for one year (rather than receiving tough prison sentences). If the defendants stay out of trouble during that time, the felony convictions will be dropped when they come back to court, and they’ll be sentenced to misdemeanors instead. Most of the defendants will avoid jail time, and will have to pay $5,600 to PayPal in restitution.

But for most of these defendants, the experience of going through a federal criminal prosecution is going to be enough to deter them from doing something similar again. Not to mention the financial penalties and misdemeanor convictions. And for those who aren’t deterred? The punishment will appropriately increase the next time. There’s just no need to excessively punish all wrongdoers.

We shouldn’t let the government’s fear of computers justify disproportionate punishment. The type of graduated punishment in the Paypal 14 case is routine in low-level, physical-world criminal cases brought in state courts throughout the country; it can work with computer crime too.

It’s time for the government to learn from its failed 20th century experiment over-punishing drugs and start making sensible decisions about high-tech punishment in the 21st century. It can’t afford to be behind when it comes to tech, especially as the impacts of “cyber-panic” on users — beyond hackers — are very real.

Why I’m Betting on Julia

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URL:http://www.evanmiller.org/why-im-betting-on-julia.html


By Evan Miller

January 23, 2014

The problem with most programming languages is they're designed by language geeks, who tend to worry about things that I don't much care for. Safety, type systems, homoiconicity, and so forth. I'm sure these things are great, but when I'm messing around with a new project for fun, my two concerns are 1) making it work and 2) making it fast. For me, code is like a car. It's a means to an end. The "expressiveness" of a piece of code is about as important to me as the "expressiveness" of a catalytic converter.

This approach to programming is often (derisively) called cowboy coding. I don't think a cowboy is quite the right image, because a cowboy must take frequent breaks due to the physical limitations of his horse. A better aspirational image is an obsessed scientist who spends weeks in the laboratory and emerges, bleary-eyed, exhausted, and wan, with an ingenious new contraption that possibly causes a fire on first use.

Enough about me. Normally I use one language to make something work, and a second language to make it fast, and a third language to make it scream. This pattern is fairly common. For many programmers, the prototyping language is often Python, Ruby, or R. Once the code works, you rewrite the slow parts in C or C++. If you are truly insane, you then rewrite the inner C loops using assembler, CUDA, or OpenCL.

Unfortunately, there's a big wall in between the prototyping language and C, and another big wall between C and assembler. Besides having to learn three different languages to get the job done, you have to mentally switch between the layers of abstraction. At a more quotidian level, you have to write a significant amount of glue code, and often find yourself switching between different source files, different code editors, and disparate debuggers.

I read about Julia a while back, and thought it sounded cool, but not like something I urgently needed. Julia is a dynamic language with great performance. That's nice, I thought, but I've already invested a lot of time putting a Ferrari engine into my VW Beetle — why would I buy a new car? Besides, nowadays a number of platforms — Java HotSpot, PyPy, and asm.js, to name a few — claim to offer "C performance" from a language other than C.

Only later did I realize what makes Julia different from all the others. Julia breaks down the second wall — the wall between your high-level code and native assembly. Not only can you write code with the performance of C in Julia, you can take a peek behind the curtain of any function into its LLVM Intermediate Representation as well as its generated assembly code — all within the REPL. Check it out.


emiller ~/Code/julia (master) ./julia
 _
 _ _ _(_)_ | A fresh approach to technical computing
 (_) | (_) (_) | Documentation: http://docs.julialang.org
 _ _ _| |_ __ _ | Type "help()" to list help topics
 | | | | | | |/ _` | |
 | | |_| | | | (_| | | Version 0.3.0-prerelease+261 (2013-11-30 12:55 UTC)
 _/ |\__'_|_|_|\__'_| | Commit 97b5983 (0 days old master)
|__/ | x86_64-apple-darwin12.5.0
julia> f(x) = x * x
f (generic function with 1 method)
julia> f(2.0)
4.0
julia> code_llvm(f, (Float64,))
define double @julia_f662(double) {
top:
 %1 = fmul double %0, %0, !dbg !3553
 ret double %1, !dbg !3553
}
julia> code_native(f, (Float64,))
 .section __TEXT,__text,regular,pure_instructions
Filename: none
Source line: 1
 push RBP
 mov RBP, RSP
Source line: 1
 vmulsd XMM0, XMM0, XMM0
 pop RBP
 ret

Bam — you can go from writing a one-line function to inspecting its LLVM-optimized X86 assembler code in about 20 seconds.

So forget the stuff you may have read about Julia's type system, multiple dispatch and homoiconi-whatever. That stuff is cool (I guess), but if you're like me, the real benefit is being able to go from the first prototype all the way to balls-to-the-wall multi-core SIMD performance optimizations without ever leaving the Julia environment.

That, in a nutshell, is why I'm betting on Julia. I hesitate to make the comparison, but it's poised to do for technical computing what Node.js is doing for web development — getting disparate groups of programmers to code in the same language. With Node.js, it was the front-end designers and the back-end developers. With Julia, it's the domain experts and the speed freaks. That is a major accomplishment.

Julia's only drawback at this point is the relative dearth of libraries— but the language makes it unusually easy to interface with existing C libraries. Unlike with native interfaces in other languages, you can call C code without writing a single line of C, and so I anticipate that Julia's libraries will catch up quickly. From personal experience, I was able to access 5K lines of C code using about 150 lines of Julia— and no extra glue code in C.

If you work in a technical group that's in charge of a dizzying mix of Python, C, C++, Fortran, and R code — or if you're just a performance-obsessed gunslinging cowboy shoot-from-the-hip Lone Ranger like me — I encourage you to download Julia and take it for a spin. If you're hesitant to complicate your professional life with Yet Another Programming Language, think of Julia as a tool that will eventually help you reduce the number of languages that your project depends on.

I almost neglected to mention: Julia is actually quite a nice language, even ignoring its excellent performance characteristics. I'm no language aesthete, but learning it entailed remarkably few head-scratching moments. At present Julia is in my top 3 favorite programming languages.

Finally, you'll find an active and supportive Julia community. My favorite part about the community is that it is full of math-and-science types who tend to be very smart and very friendly. That's because Julia was not designed by language geeks — it came from math, science, and engineering MIT students who wanted a fast, practical language to replace C and Fortran. So it's not designed to be beautiful (though it is); it's designed to give you answers quickly. That, for me, is what computing is all about.

(By the way, if you're in the Chicago area, Leah Hanson is hosting a free workshop at the University of Chicago on Feb. 1. Join us!)

Want to learn more from your data? My desktop statistics software Wizard can help you apply statistics and communicate discoveries visually without spending days struggling with pointless command syntax. Check it out!

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Bitcoin Now Accepted at TigerDirect.com!

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URL:http://www.tigerdirect.com/bitcoin/


Bitcoin miners run specific software on their computers to help collectively solve very large and complex problems. Much like "SETI at Home" or "Folding at Home".

Every transaction that takes place using bitcoins is recorded in a public ledger called the blockchain. Basically, the block chain is a history of all confirmed transactions and a record of how much each bitcoin wallet has. Think of it like an army of accountants constantly writing who has how much and who paid whom in a huge piece of paper for all to verify (the blockchain). The process called bitcoin mining confirms each of these transactions before it is saved into the blockchain. On a few occasions, a transaction is really a reward of several bitcoins. This reward is what gives bitcoin miners the incentive to mine. Because the process of searching for bitcoin takes a lot of effort for computers, it is has come to be called "mining".

So, how do I get started on this mining gig?

Like many things, mining for bitcoins can be quite easy or very hard. Today, we have custom solutions for your needs.

Hardware:

AMD offers the ability to build your own custom mining PC. Optimize your machine to run the way you want on the budget you want.

Start here for AMD hardware.

Butterfly labs provides a complete solution to get started easily. They provide the hardware and software you need to get started mining!

Coming Soon!

Software:

You'll need a wallet to begin collecting and storing your Bitcoins. For info regarding a wallet, read our Get Started section or sign up now for a free wallet.

Once you have your wallet and hardware ready to go, all that's left is getting your own mining application and you're ready to start mining!

Mastering Vim in Vim - Wedding Party

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URL:http://nerds.weddingpartyapp.com/tech/2013/11/17/mastering-vim-in-vim/


Mastering Vim in Vim Nov 17th, 2013 | Comments

Vim has a deserved reputation for being difficult to learn. The included vimtutor program will teach you the absolute basics, enough to allow you to edit a file, but what if you want to achieve the extreme proficiency you’ve heard vim users are capable of?

There are many good articles about learning vim a quick google search away, andvimcasts has some great screencasts, but those resources are not explicitly geared towards helping you achieve mastery through practice. You might learn some interesting tricks, but how do you ensure you will be able to remember them long enough to use them? What you need is a vim study lab!

A vim study lab consists of a large set of the textual equivalent of study cards with some vim commands to make navigating the cards easier. As an added bonus, since the lab is within vim, you can try out any commands on the cards easily.

Here’s an example of a vim-study-lab-in-a-file. The file, when sourced, turns the vim buffer into the vim study lab. The file consists of two parts. The first part is a hunk of vimscript that is executed when you source the file. It creates keyboard commands that make it easy to move the cards in the queues.

The second part is the queues themselves, Study, and Known. (The idea is that once you know something so well that you don’t need to study it anymore, you can move it into the Known queue, just to keep it around for posterity.) The queues simply consist of a command and some information about it.

Here you can see it in action.

Here is a greatly shortened example of the queues:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 = Study ciw change the whole word without necessarily being selected on the first letter of the word `. jumps to exact position of last modification in the buffer g* Forward find word under cursor (fuzzy... will include partial matches) g; go backward in the change list in the buffer g, go forward in the change list in the buffer = Known * Forward find word under cursor (will not include partial matches)

To start, just copy the contents of the file (not the tiny snippet above) into a vim buffer, save it, then source the file by typing :so %

Now, hit ,, to move the first card from the top of the Study queue to the bottom. Rinse and repeat.

Over time your vim study lab can end up as a nice repository of newly aquired knowledge. Just add new cards as you pick up new tricks.

See any commands in our lab-in-a-file that you particularly liked? Did we miss anything awesome? Let us know in the comments or fork the gist!

Check out the discussion on Hacker News

Care about mastering front-end web or Android development? Consider joining us!

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