Our offices should encourage casual, cafe-style interactions, but allow people to disappear into personalized, private spaces when they want to be alone. Our schools should teach children to work with others, but also to work on their own for sustained periods of time. And we must recognize that introverts like Steve Wozniak need extra quiet and privacy to do their best work.

Before Mr. Wozniak started Apple, he designed calculators at Hewlett-Packard, a job he loved partly because HP made it easy to chat with his colleagues. Every day at 10 a.m. and 2 p.m., management wheeled in doughnuts and coffee, and people could socialize and swap ideas. What distinguished these interactions was how low-key they were. For Mr. Wozniak, collaboration meant the ability to share a doughnut and a brainwave with his laid-back, poorly dressed colleagues — who minded not a whit when he disappeared into his cubicle to get the real work done.

Fascinating.

A study done by consultancy QSM in 2005 seems to indicate that smaller teams are more efficient than larger teams. Not just a little more efficient, but dramatically more efficient. QSM maintains a database of 4000+ projects. For this study they looked at 564 information systems projects done since 2002. (The author of the study claims their data for real-time embedded systems projects showed similar results.) They divided the data into “small” teams (less than 5 people) and “large” teams (greater than 20 people).

To complete projects of 100,000 equivalent source lines of code (a measure of the size of the project) they found the large teams took 8.92 months, and the small teams took 9.12 months. In other words, the large teams just barely (by a week or so) beat the small teams in finishing the project!

Given that the large teams averaged 32 people and the small teams averaged 4 people, the cost of completing the project a week sooner with the large team is extraordinary: at $10,000 per person-month (fully loaded employee cost), the large teams would have spent $1.8M while the small teams only spent $245k. I can’t think of too many situations where gaining one week in the schedule could possibly justify this cost differential.

Worth additional study.

Traveling for 10 months around the world through 17 countries covering Africa, South East Asia, Australasia and North, Central and South America. The trip was centered around surfing and photography

Presenting in Hong Kong, Japan, the US and London

Writing a book for O'Reilly as I went, titled JavaScript Web Applications

Writing another book on CoffeeScript, soon to be published by O'Reilly.

Doing a ton of open source libraries, such as Spine, Spine.Mobile, GFX, and Juggernaut.

Building a startup prototype

Presenting at FOWA

And finally, landing a job at Twitter

A friendly reminder, you understand.

A few days ago I realized something surprising: the situation with time is much the same as with money. The most dangerous way to lose time is not to spend it having fun, but to spend it doing fake work. When you spend time having fun, you know you're being self-indulgent. Alarms start to go off fairly quickly. If I woke up one morning and sat down on the sofa and watched TV all day, I'd feel like something was terribly wrong. Just thinking about it makes me wince. I'd start to feel uncomfortable after sitting on a sofa watching TV for 2 hours, let alone a whole day.

And yet I've definitely had days when I might as well have sat in front of a TV all day—days at the end of which, if I asked myself what I got done that day, the answer would have been: basically, nothing. I feel bad after these days too, but nothing like as bad as I'd feel if I spent the whole day on the sofa watching TV. If I spent a whole day watching TV I'd feel like I was descending into perdition. But the same alarms don't go off on the days when I get nothing done, because I'm doing stuff that seems, superficially, like real work. Dealing with email, for example. You do it sitting at a desk. It's not fun. So it must be work.

Brilliant as always.

To summarize these results:

• The average players are working just as many hours as the elite players (around 50 hours a week spent on music),
• but they’re not dedicating these hours to the right type of work (spending almost 3 times less hours than the elites on crucial deliberate practice),
• and furthermore, they spread this work haphazardly throughout the day. So even though they’re not doing more work than the elite players, they end up sleeping less and feeling more stressed. Not to mention that they remain worse at the violin.

I’ve seen this same phenomenon time and again in my study of high achievers. It came up so often in my study of top students, for example, that I even coined a name for it: the paradox of the relaxed Rhodes Scholar.

This study sheds some light on this paradox. It provides empirical evidence that there’s a difference between hard work and hard to do work:

• Hard work is deliberate practice. It’s not fun while you’re doing it, but you don’t have to do too much of it in any one day (the elite players spent, on average, 3.5 hours per day engaged in deliberate practice, broken into two sessions). It also provides you measurable progress in a skill, which generates a strong sense of contentment and motivation. Therefore, although hard work is hard, it’s not draining and it can fit nicely into a relaxed and enjoyable day.
• Hard to do work, by contrast, is draining. It has you running around all day in a state of false busyness that leaves you, like the average players from the Berlin study, feeling tired and stressed. It also, as we just learned, has very little to do with real accomplishment.

Brilliant stuff.

For the last three decades sports psychologists have been assembling a picture of how elite athletes hit 95-mph fastballs or return 150-mph tennis serves. The intuitive explanation is that the Ryan Howards and Rafael Nadals of the world simply have faster nervous systems—quicker reflexes, which give them more time to react to the ball. But it turns out that when elite hitters, from baseball and tennis to badminton to cricket, are hauled into the lab, their reaction speeds are no better than those of people chosen off the street.

In tests involving pressing a button in response to a flashing light, most subjects—athletes and nonathletes alike—take about 200 milliseconds, or a fifth of a second. (You can test yourself online at humanbenchmark.com) So, researchers conclude, a fifth of a second is about the bare minimum needed for the eye to take in information and convey it by electrical impulse to the brain, and for the brain to relay a message to the hands. "Once that pitch reaches the last 200 milliseconds," Thomas says, "you can't change your decision anymore. You're already swinging where you're swinging—and a lot can happen in the last 200 milliseconds of a pitch."

Two hundred milliseconds is almost half the entire flight time of a big league heater; the batter must start his swing before the ball is halfway to home plate. And given that the window for actually making solid contact with a fastball is about five milliseconds, or 1/200th of a second, it's a wonder that anyone ever hits it. In fact, the only way to accomplish it—the technique that separates the expert from the amateur—is to see the future.

I know this from table tennis -- the pros aren't faster, they just know where to be and they get there earlier. It's interesting to see the number of places this applies, from sports to, well, *many* things.

Thoughts? How can I improve this diagram?

“What the hell is a ‘degree’ and why do we need one?”, the ancient brain counters.

“Because that’s what you’re supposed to do,” the rational brain responds.

And this is where the problem occurs.

The rational part of the brain is promoting an abstract societal value. It knows that for a middle class American, earning a college degree is an expected milestone on your path to integration into the middle class economy

But the ancient brain doesn’t do well with abstract societal values, which are a recent addition to humankind on the scale of evolutionary time. One way to understand deep procrastination, therefore, is as a rejection of an ambiguous, abstract answer to the key question of why you’re going through the mental strain required by the college experience.

I think any other approach is a temporary one.

As computing resources continue to grow, efficiency falls further behind another concern when writing code, though. That concern is the cleanness of the code itself. Mostly, this boils down to readability and comprehensibility. Programmers need to be able to read and comprehend your code — programmers that will come along after you have moved on and even when you come back to your own code in six months.

Without readability and comprehensibility, you cannot easily reuse your code. You will be forced to reinvent the wheel many times over if you decide it is easier to write from scratch than use what you have already written when that would otherwise serve perfectly well. When writing open source software, the same problem applies to other people. Even worse when writing open source software, if your code is unreadable or — once read — incomprehensible, nobody else will bother looking at the code very much; you will not get any feedback on it other than (perhaps) complaints about that, you will not get code contributions, and ultimately your “open source” will be so opaque as to be effectively closed. It may be easier for others to just rewrite the needed functionality and put your project “out of business”. This is happening to GNU Screen right now.

I've always known this. An interface to a tool, and it's ability to be understood and used properly, is more important than how effective it is. But this is only true when all tools are mostly effective. That's why this has only become true in the last decade or so. Before then it was all about effectiveness.

Michael Galpert rolls over in bed in his New York apartment, the alarm clock still chiming. The 28-year-old internet entrepreneur slips off the headband that’s been recording his brainwaves all night and studies the bar graph of his deep sleep, light sleep and REM. He strides to the bathroom and steps on his digital scale, the one that shoots his weight and body mass to an online data file. Before he eats his scrambled egg whites with spinach, he takes a picture of his plate with his mobile phone, which then logs the calories. He sets his mileage tracker before he hops on his bike and rides to the office, where a different set of data spreadsheets awaits.

“Running a start-up, I’m always looking at numbers, always tracking how business is going,” he says. Page views, clicks and downloads, he tallies it all. “That’s under-the-hood information that you can only garner from analysing different data points. So I started doing that with myself.”

This stuff fires me up. Combined with cooking, cooking equipment, cleaning things up, buying furniture, reading, writing, etc.--it's all about engineering the life you want.

Highly enthused I am.