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Study Hacks Blog

Getting (Unremarkable) Things Done: The Problem With David Allen’s Universalism

Getting Beyond Getting Things Done

I first read David Allen’s Getting Things Done (GTD) in 2003. I was a junior at Dartmouth and Allen’s ideas resonated at a time when my obligations were starting to overwhelm me. I committed to his system.

After a few years, by which time I was at MIT for graduate school, I found myself frustrated with the whole GTD canon and was ready to abandon it altogether.

My issue was simple: it wasn’t helping me become better.

I was good at full capture and regular review, and, accordingly, was quite organized. This was a good time in my life to ask me to submit a form or tackle a complicated logistical process. You could be confident that I would capture, process, and then accomplish it.

But I was missing the intense and obsessive wrangling with the hard problems of my field — the type of habit that made people in my line of work exceptional. My commitment to GTD had me instead systematically executing tasks, one by one, like an assembly line worker “cranking widgets” (to use a popular Allen aphorism).

I didn’t need to be cranking widgets. I needed to instead be crazily focused.

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Einstein’s Rubber Ball

Detecting Deep Work An interesting nugget from Robert Greene’s new book, Mastery: when Einstein was working on the theory of relativity, he held a rubber … Read more

Some Notes on Deep Working

Diving into Deep Work Last week I introduced the deep work philosophy — an approach to knowledge work that (in theory) increases the quality and … Read more

Knowledge Workers are Bad at Working (and Here’s What to Do About It…)

An Inconvenient Observation

Knowledge workers are bad at working.

I say this because unlike every other skilled labor class in the history of skilled labor, we lack a culture of systematic improvement.

If you’re a professional chess player, you’ll spend thousands of hours dissecting the games of better players.

If you’re a promising young violin player, you’ll attend programs like Meadowmount’s brutal 7-week crash course, where you’ll learn how to wring every last drop of value from your practicing.

If you’re a veteran knowledge worker, you’ll spend most of your day answering e-mail.

As I’ve argued here in my new book, this represents a huge opportunity for knowledge workers. If you can adopt a culture of systematic improvement, similar to what’s common in other skilled fields, you can potentially accelerate your career far beyond your inbox-dwelling, discomfort-avoiding peers (and cultivate passion for your livelihood in the process).

But how do you adopt this approach in your specific job? This is the most common question I’m asked in response to this idea.

In this post, I want to propose a (tentative) answer…

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Mastering Linear Algebra in 10 Days: Astounding Experiments in Ultra-Learning

The MIT Challenge

My friend Scott Young recently finished an astounding feat: he completed all 33 courses in MIT’s fabled computer science curriculum, from Linear Algebra to Theory of Computation, in less than one year. More importantly, he did it all on his own, watching the lectures online and evaluating himself using the actual exams. (See Scott’s FAQ page for the details of how he ran this challenge.)

That works out to around 1 course every 1.5 weeks.

As you know, I’m convinced that the ability to master complicated information quickly is crucial for building a remarkable career (see my new book as well as here and here). So, naturally, I had to ask Scott to share his secrets with us. Fortunately, he agreed.

Below is a detailed guest post, written by Scott, that drills down to the exact techniques he used (including specific examples) to pull off his MIT Challenge.

Take it away Scott…

 

How I Tamed MIT’s Computer Science Curriculum, By Scott Young

I’ve always been excited by the prospect of learning faster. Being good at things matters. Expertise and mastery give you the career capital to earn more money and enjoy lifestyle perks. If being good is the goal, learning is how you get there.

Despite the advantages of learning faster, most people seem reluctant to learn how to learn. Maybe it’s because we don’t believe it’s possible, that learning speed is solely the domain of good genes or talent.

While there will always be people with unfair advantages, the research shows the method you use to learn matters a lot. Deeper levels of processing and spaced repetition can, in some cases, double your efficiency. Indeed the research in deliberate practice shows us that without the right method, learning can plateau forever.

Today I want to share the strategy I used to compress the ideas from a 4-year MIT computer science curriculum down to 12 months. This strategy was honed over 33 classes, figuring out what worked and what didn’t in the method for learning faster.

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