Have you ever wanted to learn something, but weren’t sure where to start? Maybe you want to learn a language, programming or business. Maybe you want the confidence to tackle supposedly “hard” subjects like math, finance or physics. Today I’m going to show you how.

I’m going to describe the process I’ve used to condense a lot of learning into a short period of time. This is the same process I used to learn MIT’s 4-year computer science curriculum in twelve months, teach myself languages, business and intellectual subjects like physics and psychology.

This article is going to be a bit longer (~3500 words), so you may want to bookmark it for later.

I’m going to focus on the strategy for learning, meaning how you choose to break down a nebulous goal like “learn to speak French” or “understand personal finance” into something concrete and actionable. As much as possible, I’ll try to provide links to specific low-level tactics I use, such as the Feynman technique, visual mnemonics or active recall as well.

This strategy is just one possibility. If you’ve found success with another, by all means, go ahead! I only want to share the method I’ve been honing for years across a variety of different subjects.

The Steps in 2-Minutes

If you’re short on reading time, I’ll summarize the steps for you:

1. Take your learning goal, and craft it into a compelling, obsession-worthy mission.
2. Find material to learn from, structure it into a flexible curriculum.
3. Define feedback mechanisms to constantly direct your future learning efforts and ensure high-intensity, active recall.
4. Test and enforce a schedule that is sustainable over the entire lifetime of the project.
5. Develop a long-term retention strategy (formal or informal).

There’s a few points that may be different from what you’re used to:

The first is that the learning goal is oriented around a obsessive mission. Many people trying to learn something adopt a haphazard, casual approach. In general, I’ve found this wastes a lot more time and produces lesser results.

The second is that the strategy is defined by high-feedback practice. In a classroom setting, students can be forgiven for neglecting this step because it is already partially provided in the form of assignments and quizzes. When teaching yourself something, it is very easy to slip into learning tasks that are devoid of feedback and so it takes months to realize you’re off course.

Finally, the process is driven by mentally intensive, active learning methods. Although this can be uncomfortable at first, the speed of that results come makes it worthwhile. You can spend months on a slower strategy, get discouraged and give up which could be fixed by going through some initial discomfort but seeing results quickly.

Now, onto the steps…

Step One: Craft an Obsession

Almost anything can be achieved with the right motivation. The motivation you bring to a project forms the foundation for all your efforts. If that foundation is unstable, you don’t have a chance at success even if you use all the “correct” learning techniques.

My approach has been to choose short-term obsessive missions for learning new things. The word obsession usually has a negative connotation, being paired with “dangerous” or “unhealthy”. But obsession can also be a positive force. By structuring your project around a compelling mission, you focus your enthusiasm for the subject (or the rewards it can bring in your life) onto a single target.

The MIT Challenge was a good example of this. I took the vague goal I had of wanting a computer science education, and crafted it so that it would become very interesting to me. Had I instead started off with the aim of “learning a lot about computer science” I doubt I could have accomplished nearly as much in ten years, let alone one.

Your missions don’t need to be as ambitious or all-consuming, however. Even a project that only takes a couple hours a week can still be compelling.

Here are a few ingredients I’ve found helpful for taking a vague goal and crafting a mission you can get excited about:

1. Give it a name. Naming your project helps you define it. A name helps you identify the boundaries of what you’re trying to accomplish with this particular mission, and which you aren’t. Having a name also helps you think about the project as a unified whole instead of a random collection of loosely related learning tasks.
2. Pick a specific objective. Narrow your ambitions onto something concrete. Instead of just trying to learn a language, have a goal of speaking only in the target language for an entire day, for example.
3. Constrain the scope. Instead of just defining what you’d like to accomplish, also define which things are outside of the scope. This doesn’t mean you have to avoid learning anything outside of those constraints, but it helps you prioritize the vague desire many autodidacts have to “learn everything” onto something attainable in a project.
4. Hit the challenge sweet spot. The ideal amount of challenge is that it should be hard enough that you aren’t sure whether you’ll be successful, but not so hard that you give up. If you’ve put off learning something because it scares you, try lowering the challenge. If you’ve given up because you’ve been bored before, try increasing the challenge.

Building a compelling mission isn’t too difficult, once you try. The majority of the time people skip this step, in my mind, is because they either don’t realize it’s important, or they falsely convince themselves that there’s no way learning about *insert subject* could be compelling.

Step Two: Build a Flexible Curriculum

The next step is to gather material. The problem is rarely that there isn’t any material available, but that the material can be hard to find or that good material can be drowning in irrelevant or lousy content.

I’ve found it important to choose material from a wider net than others may cast. This way you can shift between resources to meet your goals. Here are some points to look for when trying to find material:

• Open courses and MOOCs (edX, Coursera, Udemy, MIT’s OCW)
• Textbooks (look in the comments here for good ones)
• Pre-packaged courses
• Book lists (Personal MBA, Good Reads, etc.)
• Videos (YouTube, Khan Academy, Patrick JMT, etc.)
• Journal articles
• Private instruction/tutoring
• Educational software

Depending on the size of your project, you may want to spend a few hours looking for different options. I must note, that with the exception of some MOOCs and pre-packaged courses, you’ll almost always need to draw from multiple sources.

Another piece of advice—don’t let a lack of complete courses bother you. I did two-thirds of the MIT Challenge just using suggested textbooks and minimal guidance from MIT’s OCW, and in most cases the deficiency was negligible. The difficulty is almost always from the subject, not a lack of resources.

Once you’ve identified material, you need to develop a flexible curriculum around it. By flexible, I mean that, unlike school, the curriculum is something that you can modify and adjust depending on your progress.

When I’d go through a class during the MIT Challenge, I’d often have a few resources to choose from: videos, textbook, external tutorials and articles. My curriculum would be to pursue one resource, but use the feedback I was getting to adjust it. After watching videos, for example, I could use the textbook or articles to fill the missing gaps.

This is even more true when your goal isn’t to learn a particular set of knowledge, but to acquire a useful skill. When I’m learning a new programming language, I often go through several different resources, switching whenever my feedback indicates my weaknesses are more easily fixed using a different resource.

The final key with a curriculum is to not get overwhelmed. The purpose of picking out material isn’t to try to cover all of it. Instead, it should be to give you a starting point for structuring your learning efforts. Even if a different resource turns out to be slightly more efficient later, you can adjust.

I’ve found it useful to do most of this step prior to starting my project. For me, gathering material is distracting from the task of actually learning from it. This is why investing a day or two into researching, bookmarking, downloading and purchasing all of the material you might use in advance is so helpful.

Side note: If you’re not sure about a paid resource, check if it has a free trial/money-back offer. Most have free trials, so you can do a pilot with it before committing your money. For those that don’t, used and library options can significantly reduce the cost. I’d often get textbooks for under ten dollars during the MIT Challenge, so cost is rarely the limiting factor.

Step Three: Define Feedback Mechanisms

Feedback is essential to learning. The first reason is because it helps you guide your progress. If you’re failing practice problems or can’t code a simple program, you know you need to adjust your learning methods.

The second reason is that thinking about feedback mechanisms tends to promote efficient learning methods. One feedback tool you might use is practice problems, which has demonstrated effectiveness in increasing long-term retention.

How do you incorporate feedback?

The two most straightforward ways are by producing something or practicing something. Although not guaranteed to provide feedback, if you’re doing either of these as a significant amount of your learning time, you’ll probably be getting feedback.

Combining learning a programming language, for example, with a set of mini-projects where you actually write valid code ensures that you’re getting feedback. Learning about design while building models or illustrations gives you a chance to observe whether the lessons are creating improvement.

Practicing speaking a language with native speakers ensures that all your learning efforts with SRS, audio courses or phrase books is actually helping you speak. Practice problems for math or physics ensure your conceptual understanding is growing.

Which feedback mechanism you use will depend on what resources you have and what the subject is. Even if you can’t pick a perfectly suited feedback mechanism, you can incorporate smaller feedback drills to ensure you’re not completely without feedback. These smaller mechanisms can include: self-quizzing on learned material, writing Feynmans without reference material or using software like Anki.

The best feedback goes directly toward your project’s mission. If your mission is to perform a skill or speak authoritatively about a topic, then practicing that skill or writing about the subject are ideal feedback mechanisms. If your goal is to have a particular set of knowledge, self-testing and explaining the knowledge to yourself are good mechanisms.

Step Four: Enforce a Schedule

Many self-learners can successfully reach this point in their project, but fail on the next one: actually doing all the work. It’s one thing to tell yourself you’re going to learn about biology or history. It’s another thing to actually execute the curriculum you’ve devised and accomplish the mission.

The first half is in preparation. Without a compelling mission, it’s easy to get bored and quit. Without a curriculum, it’s easy to get lost and give up. Without feedback mechanisms in place, it’s easy to not learn anything at all.

The second half is in establishing a schedule that allows you to follow through with the reading, watching and practicing you need to do. Here are a couple frameworks I’ve found helpful for successfully implementing such a system:

1. The “Every Day” Plan

The first strategy is to do a little bit of work every day. I did this with a friend on a project to learn languages (which I’ll hopefully be sharing more on in the summer). Because of conflicting schedules and the desire to stay at the same pace, we decided to do an hour lesson, in the morning, every day.

In the past, I’ve done similar approaches to book-reading projects. When I want to cover a large swatch of information on a particular domain, I would get several books and devote 30-60 minutes reading them at the same time each day.

The process is simple:

1. Define a certain time period, every day, when you’ll do your work. It doesn’t need to be a long time period to be effective.
2. Commit to following this time period, without exception, for at least three weeks. The number is arbitrary, but I’ve found that enforcing the habit strictly in the beginning is essential.

The advantage of this strategy is that the effort quickly becomes a habit. This is the approach to use if your project is not going to be full-time and it will require some self-discipline to execute. The other strategies I’ll mention can also be effective, but they have greater risks that you’ll drop the ball when your motivation wanes.

2. The Obsessive Burst

This strategy is one I’ve used on projects which interested me deeply, and were short (in the span of a few weeks). The idea is simply to work on the project during most of your off-hours until it is completed.

This method only works if you’re genuinely motivated enough to pull it off or there is a compelling external reason for such devotion. If you’re rolling your eyes at this possibility, do yourself a favor and opt for strategy #1 instead.

The advantage of this method is that it utilizes your initial motivation. Some projects that could be finished quickly, I opted for this approach because I knew that my motivation would wane after a few weeks and I wanted to see results quickly. The disadvantages are obvious, but in some instances they don’t matter.

3. The Precommitted Schedule

A final strategy I’ll mention is simply to precommit to a certain goal, or certain hours. If you’re making use of tutoring or outside help, simply committing to your tutor to have finished an amount by each lesson will give you motivation. Opting for a structured MOOC or course plan can also be helpful, since they provide you with constraints you’re required to follow.

Another alternative is to set up short-term exams which you need to pass along the way. This could be useful in studying for a larger self-study exam (SAT, MCAT, GMAT, LSAT, CFA, etc.). Basically, you could break down practice exams into segments and resolve to be able to ace a particular segment by the end of the week, giving you the motivation to learn that section without procrastinating.

Anti-Strategies (or Plans that Rarely Succeed)

In contrast with the above three mentioned strategies, I’ve also found some approaches that tend to work poorly. This doesn’t mean they never succeed, but rather that they require disproportionately more motivation or self-discipline to execute. These include:

• Working on your project whenever you feel like.
• Not establishing particular scheduled hours or deadlines.
• Planning to begin a learning task later, without providing a compelling reason why it should be delayed.

In the end, you know yourself and your motivation. If getting stuff done isn’t a problem for you—don’t worry about this step. If it is, I’d recommend using strategy #1 in most cases. It’s a good default go-to approach when you’re not sure which one to apply.

Step Five: Long-Term Retention

This final step is an optional one. For many learning projects, I pursue this step informally because I know my lifestyle and goals will allow me to circle back to the knowledge I acquired previously at some point.

For those who are worried that such an informal approach may lead to losing a lot of the knowledge acquired, taking additional steps can be useful. Adding a strategy for long-term practice and retention can make sure that you don’t forget things years later.

Learning for Long-Term Retention

My first weapon against the long-term decay of memory is to learn it better, the first time around. I’ve found that learning with the goal of understanding promotes the best long-term retention compared to memorized facts.

Consider learning physics. Most students spend a great deal of time memorizing formulas and the situations where they apply. Smart students spend time trying to build the intuitive principles for what the formulas are saying and why they work.

Sometimes learning to understand isn’t a short-term goal. Learning how to solve a particular problem with an equation takes a lot less time than trying to build an intuition around how it works, but years later the equations will be forgotten and the intuition will remain.

This is why I recommend metaphors, visualization, diagrams and the Feynman technique when learning. They promote the process of decoding an abstract idea into an intuition that you can keep with you much longer than memorized trivia.

This doesn’t mean understandings are immune to forgetful minds, but simply that they persist longer.

Here are some other mechanisms you can use to ensure long-term retention:

1. The Orbit Strategy

Think of how the moon orbits the Earth, returning to the same relative position each month. This strategy works similarly—after completing a project, set a notice on your calendar a few months or years into the future. Once the time comes, do a mini project to reactivate those skills.

I intend to do this broadly with the programming and computer science knowledge I acquired during the MIT Challenge. By doing a mini project every 6-12 months, I hope to sustain my skills even when I’m at a stage in my life where they aren’t a main part of my career.

I recently executed this successfully with French. Even though it had been over two years since I lived in France (and spoke French infrequently) I made the goal of going back to Paris for a month and speaking exclusively en français. I was surprised that I was even able to improve my French from where I had left it after that burst.

If the goal is only sustaining, not improving, then the period of the orbits doesn’t need to be fixed. Increasing the spacing between each burst can probably sustain the same level up to a point. An exception would be very high levels of skill (which decay more quickly) and where the skill itself changes rapidly (such as programming).

2. Scheduled Practice

Another strategy is to schedule practice or recall regularly, in small doses. I know that Benny Lewis, who speaks around ten languages fluently, uses this approach to maintain his ability. By speaking the languages every week or so, he can continue to sustain and improve his abilities over the long-term.

I do this myself with many subjects I’m interested in. I subscribe to blogs on those topics (say linguistics or economics) and use the regular posting as a way to stay connected to them.

3. Formal Systems (SRS)

If these strategies are too informal for you, then you can opt for implementing an even more structured review using a spaced repetition system such as Anki. This would be particularly useful if you needed to retain a large corpus of factual information you aren’t using frequently. I suspect medical and law students, for example, would benefit from having the factual details of their courses inputted into Anki, which they would then get reminders of long after the class was taken, so the knowledge doesn’t fade.

Implementing the 5 Steps

This is just a framework for planning and executing a self-education project. As such, you may have a lot more questions about handling the specifics. Here are a few articles I’ve written on the details of learning efficiently:

• How to understand hard ideas.
• How to take notes. (PDF)
• How to read faster.
• How to read a textbook.
• How to use metaphors/visualizations to study for an exam.
• How to quickly learn a free online course.
• How to get work done.
• How to focus on what you’re learning.
• How to access ‘hidden’ courses from MIT.
• How to learn really hard subjects.
• How to translate your self-education into an actual degree.

I cover all of this comprehensively in my course, but those above free resources should be a good starting point if you’re not ready to invest in it.

The Benefits of Learning Well

Self-education can seem like a luxury at times. Or it can look like an exercise in intellectual wastefulness—something that doesn’t materially improve your life. I’ve found the opposite is true: learning more gives you an enormous advantage in almost any area of life you choose to apply it toward.

The people I know with the best careers, relationships and lives are the ones who learn continuously. I always strive to have a learning project at all times, and following these steps have been essential to make them successful, instead of something I idly start and never finish.

Image courtesy of Hash Milhan.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

In the process of getting better at something there are two mistakes that hold you back. The first kind is the mistake of not knowing. Not knowing how the market works, which major to choose, what to do.

If I wanted to start a business selling industrial solvents, I suffer under the first error. I have no idea how the industry works (or much about solvents, for that matter). Ignorance holds me back.

Ignorance, however, isn’t too hard to fix. If I spent several months researching, I could probably have a decent idea of how the industry works. If I spent several years working in it, I’d know even more. The first error has a straightforward remedy—learn more.

The second kind of mistake, and far more insidious, is the mistake of believing things that happen to be wrong. If you’ve convinced yourself that a hill is a valley, it will take a lot of climbing before you realize you were wrong. I worry more about the second mistake.

The Map and the Territory

We spend our lives devising theories for explaining the world. These theories form crude maps of the impossibly complex terrain of our lives. We have a map for our careers, a map for our relationships, a map for our beliefs about the meaning in our lives.

Maps are good. Even a map that is wrong occasionally is a lot better than no map. Philosophical skepticism may have its adherents but it’s utterly impractical. You must have beliefs about the world to make decisions, and even imperfect ones are better than nothing.

But the map is not the territory. The territory is alien, strange and perhaps even incomprehensibly complex. Any map-making process undertaken by an individual over the course of one lifetime is going to be error-ridden.

The rational thing to do, is a cost-benefit analysis. If we can invest less resources to fix our map than the benefits of a correct map yield, fix the map. Yet human beings rarely do the rational thing.

Confirmation Bias and Protecting Our Maps

It turns out we don’t follow the rational process for map fixing. Through a set of interesting experiments, psychologists could show that instead of trying to hunt for the information that would force us to change our maps, we instead seek to information confirming what we already “know”.

The experiment was ingenious. Subjects were given a set of three numbers, such as 2, 4, 6 and told it fits a secret pattern. The task was to guess the identity of the secret pattern by suggesting further sets of three numbers, which the experimenter would say either fit or didn’t fit the pattern.

Given only one data point, many possible hypotheses could be dreamt up by the participants. The numbers could be all even, for example, or the middle number could be the average of the first and last.

The rational method for testing these hypotheses would be to choose counterexamples. If you believed the numbers were all even, try 3, 4, 6 and see if it is validated. If it did, you’d know that your only-evens rule was not the correct rule.

This wasn’t how subjects proceeded, however. Instead, they picked examples which confirmed their previous hypothesis. All-evens testers would pick 4, 8, 10 or 2, 6, 12 as candidate patterns, seeking validation for their theory.

The problem with this method was that the actual rule was “any ascending numbers” so the previous two examples would have been valid, but so would 1, 3, 12 or 3, 9, 11. The method of testing hypotheses sought confirmation, even when it couldn’t determine the secret rule.

What relevance does this have outside the laboratory? The relevance is that we look for information to support our theories, not to break them. We try to protect our maps instead of pointing out where they may be flawed. Worse, when we expend energy trying to improve our maps, the methods we default to are unsound.

Looking Around the Edges

The most profitable method to winning the secret-rule game of the experiment is not to pick random counterexamples. After seeing 2, 4 and 6 validate, picking 1, 17, 4 and seeing it fail doesn’t teach you too much. Instead, the best bet is to try to break the edges of your rule: make one odd, flip the order, make two the same.

The same strategy is effective in life: test around the edges of your map, so you’ll know where to redraw. By breaking your map in precise ways, you can get more information than seeking confirmation or pulling counterexamples out of a hat.

I’ll give an example from my business. When I first launched Learning on Steroids, it was unusually successful compared to my previous business efforts and I wanted to know which principles guided that so I could use those insights in the future. Here were some candidate hypotheses:

• Monthly billing over one-time sale.
• Conducting an email-based launch.
• Restricting capacity.
• Restricting registration time.
• Having a clearer service component (in earlier editions I made more emphasis on being able to reach me for feedback)

All of these could have been valid, some combination of them could be or it could be that none of them were the underlying causes of the recent success.

My approach to testing these hypotheses was to vary the different variables individually in my future launches. Later, I did launches that had one-time courses, no capacity restrictions and downplayed the service component. I couldn’t always test each variable in perfect isolation, but in nearly every launch the permutation of these variables was somewhat different.

In retrospect, my hypothesis now is that #2, conducting an email-based launch, is the only consistent winner. Restrictions on capacity has mixed results and restrictions on registration time has a minor, but positive effect. Service components were not important, but that could have been a feature of the price points tested.

My map is far from perfect now, but it is a lot better than it was when I started, which I believe is a large part of the reason my business is generating four times the revenue from when I had made those initial hypotheses.

Researching Edge Cases

You often don’t need to run an experiment to break your map on edge cases and update it to more accurate beliefs. Sometimes simply doing a bit of research can reveal edge-case counterexamples which force you to re-evaluate your thinking.

Cal Newport recently shared an example from his own journey trying to become a tenured academic. Instead of browsing through random examples and trying to confirm his previous hypotheses, he looked for a natural experiment: choose a group of PhD graduates from the same graduating class, but who differed greatly in their eventual success and look at what they did differently in their early careers.

Studying these two groups, the biggest differences were number of papers published (the successful group had more publications) and number of citations, a rough indicator for quality. Using that as a benchmark, Cal could easily hone in on the precise metrics success required in his field.

Research, as opposed to direct experimentation, is useful when the time frame you expect to see results is very long. I could directly experiment on my launch strategy because I could repeat it every 3-6 months. Cal was better off looking for natural experiments because the time frame to observe results was in decades.

Comfort in Contradiction

To me, the idea of map-breaking is unsettling and counterintuitive. Our brains aren’t hard-wired to think this way, so it always takes a deliberate effort to apply.

The challenge to me is being able to be comfortable with spending a lot of mental energy constructing explanatory theories, and then seeking to tear them down. We’d rather spend time building more, rather than admit what we’ve built may be on a shaky foundation.

One step I’ve found helpful to combat this urge (and is often derided by outsiders) is to simply allow yourself to temporarily hold contradictory beliefs. Believing that your theories themselves are a work-in-progress can allow you to recognize the validity of part of the map, even if you don’t know how to connect it to the other parts yet.

Ultimately, confirmation bias is in our nature, and can’t be completely avoided. With effort, however, I think we can remind ourselves to avoid it when we design the larger experiments or research projects to redraw the lines on our map.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Science prides itself on being able to validate hypothesis with controlled experiments. Take two subjects, vary only a single variable between them, whatever difference you generate must owe to that distinction. If only life were that easy.

Instead, life is full of confounding variables. We build theories for our lives the best way we can, but those are corrupted somewhat by hidden variables. What little we think we know about ourselves, we probably know less.

That’s why I’m always interested in blank slate moments. These are moments where there is an abrupt change of many different variables. They don’t have the precision of a controlled experiment, but they give you a chance to eliminate much of the noise.

Travel as a Blank Slate

I lived for almost a year in France, a few years ago. No one from my old life came with me, so for eleven months every relationship and friendship I had was a blank slate. Even the language and culture were different so I had to relearn how I communicated with other people.

I remember at one point getting in a fight with a girl I was dating. Had it not been in French, it would have been identical to one I had had back home. Everything else was different, except for me. I was creating the situation, even if I didn’t yet know how.

Job Changes as a Blank Slate

A good friend of mine recently switched offices. This time, instead of noticing the recurring constants, he noticed the changes. At his old job he had been a junior employee. While smart and competent, his early trainee period set his first impression with many of his colleagues of being the newbie.

When he switched jobs, the impression was radically different. Now he’s one of the most sought-after employees in the office, with perks and pay that are unusual for someone of his experience. The old environment was holding him back, but it took a blank slate moment for him to realize it.

Entrepreneurial Blank Slates

I’ve been blogging and selling ebooks/courses for over seven years now. I’m sure if I wanted to write a book about how to do it, I could (I have zero desire to, but that’s another matter). In my head, I’ve convinced myself that I know a lot about it.

But where are my blank slate moments? Almost every moment of this blog was built on the previous one. A/B testing can test details, but you can’t split test your strategy. How can I be so convinced I’m right?

Admittedly, I’m probably wrong about many of the things I’m convinced are true. The problem is I don’t know which ones.

When I started Learning on Steroids, I became convinced that monthly subscriptions were a better business model for me than static product sales. But my newer data seems to cast doubt on that theory. It might be that Learning on Steroids was successful simply because of the product, and the subscription model was inconsequential.

The Cost of Blank Slates

When I look over the long-term trajectory of my life, blank slate moments were the inflection points in many areas. The sudden changes allowed me to realize the truth of a situation and break out of stagnation.

Blank slate moments are most useful when they’re a temporary deviation of an ongoing trend. Living in France taught me about where I was failing in my relationships because every variable changed but me. However, had I changed cities every three months, the frequent change would itself be a kind of variable that stays constant. In that hypothetical possibility, I might have deduced that my fight wasn’t my fault, but the fault of moving around so much.

Clearly generating endless blank slate moments in the same way isn’t helpful. Blank slates hamper progress—how could you possibly get any insight into what it takes to start a successful business if you kept switching businesses every week. Worse, when the way the blank slate is generated is itself recurring, it introduces a new variable which perverts the data.

Positive blank slates are minimally disruptive, but they provide maximal information by quickly randomizing many of the otherwise confounding variables.

Generating Positive Blank Slates

I’ve thought a lot about how I can generate positive blank slates in my life. Here’s some ideas I’ve come up with:

1. Relocation. Travel, on its own, has only been minimally useful for me. To me, living in a new place long enough that you invest in a normal life there seems to have the best impact. The challenge is balancing the depth that allows for a true blank slate without creating a huge disruption in your life.
2. Short-term experiments. Experiments can be more controlled than a blank slate. When you just switch one variable, you get more precise information than when you change hundreds of them. I’ve found that short-term experiments, however, often limit you in the kinds of things you can change, so you test easy-to-change variables instead of the ones that might actually generate insight.
3. Orienting Projects. My MIT Challenge was a blank slate in a lot of ways, as my working life changed very abruptly. Completing it reminded me that physical travel isn’t the only way to cause a blank slate.

I haven’t built a complete framework for pursuing blank slate moments yet. However, a good rule of thumb seems to be always engaging in some kind of blank slate, especially if you can leverage the change to be useful instead of disruptive.

Ultimately I’ve found blank slate moments more useful, not as a tool to learn more about yourself or the world, but as a tool to weed out the convictions you have that might be wrong.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

I’ve written before about the importance of habits. By setting up consistent rituals of action, behavior becomes automatic. Automatic behavior means you don’t need nearly the same amount of self-discipline to finish projects as someone who works on them in a haphazard way.

Habits are built on sameness. By making your triggers, schedule and internal rules of thumb consistent, you reduce the mental overhead to get those actions done. The consistent rhythm of your behavior makes continuing that pattern easier.

But here lies a problem—learning isn’t optimized by rhythm. Deliberate practice suggests the opposite, that you should break routines to drive growth. Too much consistency inevitably leads to a plateau where weaknesses ossify and improvement becomes harder.

Hence the dilemma: we want to be able to maximize growth by breaking through plateaus, however, we also need stability and consistency so that we can sustain our effort.

Resolving the Dilemma

I’ve thought hard about this problem. One area I seem to face it is in my writing. On the one hand, having a weekly writing habit allows me to ensure I’m putting in time writing each week. Whenever I’ve let this habit slip, writing becomes much more difficult. On the other hand, I want to improve my writing ability, which may not happen if I mostly write under the same conditions, week after week.

Which should take precedence? Should I sacrifice the consistency of my current writing habit because it might push me past my current level? Should I continue writing as I am and try to grow from other areas? I don’t have an easy answer.

This also strikes me as a problem many of you might face in your jobs. The job you get paid for isn’t also the one that causes you to learn the most. Your boss or clients may want you to do the work you find easiest and most routine, likely because you’re already good at it. Yet it’s precisely the work you’re not an expert at yet which will help you master your craft.

Possible Solutions

I’ve considered a few approaches that might work to resolve this dilemma.

1. Learning Projects

A project typically runs in the span of months, not days or hours. Therefore it might be a good strategy to always have a learning oriented project, but make the project long enough that you could reasonably build the habits to support it each time.

I’ve been doing this in my own work. Last year I did the MIT Challenge which, although it was directed towards computer science, was also a project to improve my writing by giving me a better understanding of the learning topics I write about. I’m working on a similarly scaled upcoming project which should hopefully have the same effect.

One weakness of this is extra effort. Projects, as opposed to narrowly focused practice sessions, have a lot of work which is necessary but doesn’t drive growth. Designing projects efficiently isn’t always easy.

2. Setting General-Purpose “Deep Focus” Hours

Another strategy I’ve seen employed by Cal Newport is to simply chunk out time for deep focus work. In practice this could mean that you set aside 2 hours every morning to deliberately pushing your skillset further. This way you benefit from having the regular deep-focus habit, but the content of that habit changes each time so you don’t plateau.

A possible disadvantage of this is that it constrains what kinds of deliberate practice you can explore. Not all valuable learning experiences can fit inside identical constraints, so you may have less flexibility to improve learning as you could with a project.

3. Environment Shifting

Another idea is to not change what you’re doing, but change where you’re doing it. This way the environment forces both habit changes and learning.

As a writer, this could mean that I make an effort to write for a different publication, write a book or start working with an editor. These environmental changes would create an external change meaning less willpower is required to complete the project, while preventing my skills from stagnating.

Switching jobs, companies, industries or cities could all be an environmental switch that could create this effect. The weakness here is that sometimes the environment you need to kickstart growth isn’t available.

What do you think? What’s your strategy for coping with the need for both change and stability? Share your thoughts in the comments.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

I spoke at an event recently about learning and my MIT Challenge. The talk was about which memory and insight-building methods I found useful during my experiment.

After the talk, one of the audience members came and asked me whether I felt the success of the project was mostly due to efficient learning methods or hard work.

This reminded me of the first weeks of the challenge. This was when I was still worried that the project may be impossible to complete, so I put in a lot of effort. I wanted to go somewhat faster than scheduled, to give me a bit of slack as I moved to harder courses. Mostly I wanted to convince myself that the project was doable at all.

The schedule I adopted was pretty simple: wake up at six, work until six. I usually took two twenty minute breaks plus a twenty minute break for lunch. In total, around eleven hours of work each day, six days per week, for the first three months (I slowly eased back on the schedule for the following nine).

My goal would be to finish the lectures of a class in 2-3 days. A class usually had 35 hours worth of lecture content, so this meant watching about 18 hours in one day. You can squeeze that into 11 if you play it back at 1.5-2x the speed.

Reflecting on this, I wagered it was mostly hard work.

Focus is Paramount

The difficulty isn’t putting in the time. Anyone can force themselves to sit in a library and study all day. The hard part is sustaining focus.

Focus matters more than time spent. Most tasks can be completed in a fraction of their normal time with complete focus. This is especially true for learning whereby the most efficient methods also tend to be the most mentally taxing.

Focus matters more than raw effort. The sun can’t burn paper without a lens to focus its rays. Similarly, you can burn yourself out working on a project, but if that effort isn’t focused, you won’t make tremendous progress.

I don’t think I need to preach this point. When I’ve asked readers what their biggest obstacle to learning is, the number one answer is always focus. Focus is essential, but it is also incredibly hard to do.

Learning to Focus from Meditation

Focus is difficult, but it can be learned. I don’t think I could have completed the MIT Challenge, if I hadn’t learned the skill of focusing. Some may find it easier than others, like all things, but I believe anyone can get better at focusing through practice.

I became convinced that focus was learnable when I first studied meditation. I’m far from an expert meditator, but what I gleaned from my early practice attempts years ago, was that focus can be trained.

Many forms of meditation are based on the idea of mental focus. Some have you focus on a particular sound or concept to quiet your mind. Others have you focus on intense visualization which, with practice, can push you into a semi-dreamlike state as you force out the sensory input of the outside world.

I don’t meditate much these days, although I have nothing against the practice. But I do believe being introduced to meditation gave me the conceptual tools for training focus in other areas of my life.

For anyone who is interested in improving focus, I’d try doing a bit of basic research on meditation. Transcendental seems to be quite popular, but I haven’t personally tried it so I can’t vouch for it. When I started I just picked a few random books from the library and tried out a couple methods for free.

I don’t think it’s the meditation itself which helps with focus. Meditation is an inwardly-focused activity which is very different from the outwardly-focused tasks most people want to be able to focus on. But learning a couple breathing techniques and methods for focusing inwardly, they give you a sense of what is required for focusing in your work.

Practicing Focus

The first feeling I had when starting to meditate was how boring it was. Sitting awake, eyes-closed in a quiet room, I felt intensely restless. I wanted to get up and doing something and my mind felt like an uncontrollable flow of thoughts, constantly jumping from topic to topic.

They teach you when meditating to ignore this feeling. Not to suppress thoughts, but to just let them float by without jumping on them. Eventually, you get into the desired meditative state, which depends on the style of meditation you’re trying to practice.

I think this is strongly analogous to focus in your work. When you’re sitting to work on a particular project, you feel restless. You want to check something on Facebook, read a blog article, check your email or listen to music.

Following the same analogy, however, I think you can continue a meditative state of work by learning, not to suppress those feelings, but just to ignore them. Eventually you can cultivate mental stillness and allow yourself to focus on what you need to work on.

A difference between meditation and focus, however, is mental engagement. I usually find meditation to be relaxing, but focus is draining. Practicing focus is more like a mixture between meditating and endurance running.

Mastering Focus

The unfortunate part is that the only way to get good at this is through practice. Just like strengthening a muscle, focus can only be improved by doing it more.

The two methods I’ve found helpful for practicing focus are cutting distractions and setting up time blocks.

Eliminating distractions is the most obvious way to improve focus. When I’m preparing to write an article, I’ll often sit in a chair with a blank document for 30-45 minutes as I think through possible ideas to write about. No music, no internet, no phone.

The best way you can help your focus training efforts is to purposefully eliminate all distractions. This way the only enemy you need to combat is the distractions of your own mind. Meditative techniques can help a lot with those.

The next strategy I’ve found effective is to clearly delineate chunks of time for focus. The problem many people have with focus is that they don’t establish which times are focus times and which are not. By setting up a particular set of hours in the day where you don’t allow interruptions or distractions, you can get a lot more done.

All training should be progressive, so note how long you can sustain your focus, record it and then aim to slowly improve on it. If you can only hold your focus on reading a book for twenty minutes, that’s fine. Try to go for twenty-five next time.

Limits to Focus

I don’t believe a person’s ability to focus is perfectly mutable. You’ll still need breaks and you’ll still need succumb to distractions. That doesn’t negate the utility of practice, just in the way that the human body puts limitations on maximum strength doesn’t mean you can’t get stronger by lifting weights.

I believe the real value of focus is that you save time. Learning to focus means you need less time to get the same work done. Although my MIT experiment was difficult, I point out that I still had every evening off and I always had one day per week where I didn’t do any work. Focus may be difficult, but I believe it is far more liberating than the alternative.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Here’s an interesting article on the effectiveness of various study techniques—and in particular—which ones have evidence supporting them.

Some of my thoughts on the key findings:

Self-Explanation and Reading

Elaborative learning and self-explanation were found to be moderately effective. This is similar to the Feynman technique, but I’d argue the use of the method was different (I mainly use this method to hunt out specific misunderstandings, not as a general catch-all which is usually too time consuming as indicated by the research).

Summarizing and highlighting were found to be ineffective. I was surprised about the finding on summarizing, but the note on highlighting was what I expected. Perhaps some of the problem with summarizing is that it lacks a feedback mechanism to know whether you’ve actually gathered the key details?

Rereading was found to be ineffective. No surprises here. Passive review strategies are less effective than active ones.

Visualization and Mnemonics

The keyword mnemonic (using visual links to memorize words) was labelled “ineffective” but probably a better description of the actual findings is that it has more narrow usage.

Visualization while reading was found to be ineffective. I found this interesting, given my advice to students to visualize. However, it seems like the issue may be that visualization while reading is distracting. In addition, I’ve always felt the major benefits of visualizing are for abstract subjects, which don’t naturally lend themselves to images, as opposed to the concrete subjects measured here.

Practice and Spacing Were The Most Effective Methods

Practice was one of the most effective methods studied. This was a staple of the MIT Challenge, probably making up 50% of the total time I spend working through the courses. I found it interesting that practice questions were still effective even when you created the questions yourself—a good alternative if practice exams are unavailable.

Spacing effects were shown as being effective as well. During the MIT Challenge I tried to make use of spacing as much as possible, doing classes in parallel after the first few classes. It was an unfortunate weakness of the tight time-constraint premise of the challenge, however. I’d recommend most students looking to follow my self-education attempt to spread out their learning over a longer period of time and at a lower intensity than I did.

I find the spacing effects on learning to be somewhat tricky because I’ve also found focus to be enormously effective for getting things done. Juggling a couple different learning projects at the same time to be far harder to manage than being dedicated to only one or two.

Ultimately, there may be a trade-off between spacing and focus—you want the spacing to ensure better long-term recall, but you want the focus to actually get the work done.

Science, Experience and What Actually Works

Scientific research on the efficacy of different learning techniques has been a very useful counterbalance for me to the methods I’ve developed through practical experience.

I’m only a single data point. Although I collect a lot of observations and data from my students in my courses, the empirical rigor of a self-selected online course is not the same as a scientific paper. Placebo effects and the lack of control groups mean that it’s important to return to the psychological research for checks and balances.

However studies are often designed to measure the outcome of a very narrow set of conditions. If the conditions change from the experiment, the results may be very different. I end up relying a lot on practical experience to fill in those gaps in my own self-learning efforts.

Opinions I’ve Changed Since Learn More, Study Less

I find myself learning a lot about learning, as more research like this comes out and I get exposed to different learning situations and am forced to adapt. While I still support the most of the main points of Learn More, Study Less, I’ve evolved considerably on my views since then.

Here are the major opinions I’ve shifted:

• Repetition isn’t a bad thing (even if repetition alone probably is).
• Speed reading is only narrowly useful. In most learning situations, reading at a deeper level of processing and reading more slowly is better.
• Practice and active recall should be a bulk of your strategy. I haven’t given enough emphasis on active recall in the past, even though it should probably form a large chunk of your learning time.
• Spaced repetition software can be quite useful. I’ve flipped my thinking on this point since I mentioned it earlier. To me the disadvantages of decontextualized and unprioritized knowledge are outweighed by the automatic structuring of review and active recall.
• Don’t highlight. I used to have highlighting as part of an active reading strategy, but now I’m inclined to avoid it altogether. Taking sparse notes is better.
• Holistic learning is still valid for law and languages. I expressed doubts in my initial ebook as to whether learning via connections was appropriate for densely factual subjects, but since then I’ve found it useful for these subjects nonetheless.

A major challenge for me is that, in spending a lot of time learning, my opinions grow with time. Hopefully my minor reversals and shifts in emphasis don’t irk or confuse longtime readers too much. The alternative is to be dogmatic, an unsupportable long-term strategy.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Catch-22s are problems which have circular or paradoxical solutions. Named after Joseph Heller’s famous book by the same name, about a soldier who can avoid dangerous combat if he is insane (but applying for the provision is proof of sanity).

Many situations in life are close to Catch-22s, problems by which the method of solution which have the solution itself as a prerequisite. Building a successful business is considerably easier with access to capital and connections. Capital and connections are much easier to obtain if you’ve run a successful business.

Men who have had difficulty with women often lament that women claim to love genuine confidence, which comes from past experiences of success, which would seem to rely on having the confidence in the first place.

Of course, none of these are perfect Catch-22s. For every successful entrepreneur or relationship, there had to be a first success. A success that defied the circular logic that supports further successes. Overcoming these initial successes is hard, and worth studying since it may turn out to be more important than later, and grander successes, that we typically pay attention to.

Pulling Yourself Up By Your Bootstraps

Bootstrapping is another idiom that points to a seemingly circular situation. The concept, which means to achieve something using minimal resources, comes from an early 19th century American phrase to, “pull oneself over a fence by one’s bootstraps.”

Bootstrapping, that impossible of solutions, is the cure for that seemingly impossible of problems—the Catch-22s we all face in life.

The philosophy of bootstrapping is popular in entrepreneurship circles. The idea of building a company with initially limited resources is a powerful one. While many businesses required enormous investment to make viable—many haven’t. I spent about a hundred dollars to get this website started. Every month since, the business has paid for itself.

Bootstrapping isn’t a preference, it’s often a necessity. I don’t frown on entrepreneurs who invest large amounts of money in a project before it returns profit. Sometimes that is a smarter strategy than being stingy. But I started my first attempt at entrepreneurship in a small-town, with no connections at fifteen. I had to save for college and all I had was the part-time income of working as a lifeguard.

Bootstrapping applies to life, not just business. It’s the skills necessary to build something with zero resources or, seemingly, any of the prerequisites for success. Habits, discipline, social skills, confidence and competence are all, to a certain extent, driven by these exponential forces which make it easier to continue than to start.

Studying Small Beginnings

Often the advice that matters at one stage becomes irrelevant at another. As a blogger now, I don’t chase for links. I know that enough people read my blog that, if my content is good, it will spread. Spamming my articles to other blogs isn’t a good use of my time, and may even be a detrimental force since it doesn’t allow people to discover it organically.

But when I started I had zero traffic. If I didn’t tell people about what I was writing, nobody would read it. My early method was to track down blogs that frequently linked to articles similar to mine—and give them a friendly heads-up whenever I wrote an article.

The concept of marginal benefit comes into play. The marginal benefit of telling someone about your article when you have zero traffic can be quite high. Now I’ve found that same marginal benefit is often low, or sometimes even negative. It makes far more sense to build strong relationships with other bloggers before finding ways to share traffic. That has a high marginal benefit, but such opportunities are often unavailable to new bloggers.

Because the marginal costs and benefits are very different in an early phase than in a mature phase of growth, it doesn’t make sense to copy the methods of someone far along in their development. Study small beginnings, not only grandiose middles.

Bootstrapping Life

Career and entrepreneurial activities have an obvious bootstrapping component. This is often why they experience exponential growth over some range of their progress—the effects create the causes resulting in a compounding effect.

Other areas of life have less pronounced Catch-22s as well. Consider self-discipline. Self-discipline is trained through exercising self-discipline. The positive reinforcement of succeeding at discipline-requiring tasks strengthens that resource. However, it’s much easier to succeed at them when you already possess discipline in the first place.

I found something similar in my early attempts at habit formation. I was so used to being lazy and giving up at everything, that it was hard to even get the early successes I needed to reinforce those behaviors. I failed a lot at simple challenges because my self-discipline muscles were weak.

Is it Bootstrapping or Immutable Character?

I used to look at the feats of the people I admired and feel inadequate. How could they start companies, have adventures and succeed across so many areas of life when I failed at so many. They persevered through difficulty, and I gave up.

I wish someone had told me that those character traits are often bootstrapped as well. Discipline, courage, charisma and all the ingredients of success are manufactured. Even if you don’t feel you possess them now, you can generate the experiences you need to have them in the future.

Scientists who measure personality traits notice consistency over time. While I don’t doubt that our genes play significant roles in our development, part of me wonders whether those traits are truly unchangeable or whether their apparent persistence is due to the Catch-22 required. Divergence from a different starting point is not because change is impossible, but because it requires bootstrapping. Bootstrapping is arduous, so when examining populations we see most people flowing down the stream they were cast into, not swimming into a new one.

Swimming upstream is hard. But, if you work at it, eventually that upstream swim becomes downstream and what was improbable becomes inevitable.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

I love questions like this one because they’re the kind people get upset about for no reason.

When you try to say that your network of professional friends is important to your career, you get tons of angry socially maladroit engineer-types ranting about it. Technical competence, and points on an IQ test are what matters most, and any suggestion that social fluency or relationships matter too is inherently unmeritocratic.

I’ve also heard plenty of entrepreneurs who believe that everything is outsourceable. Why bother learning something like programming or design when you can pay someone else?

Why does it have to be a dichotomy? There’s no reason they both don’t matter. Which matters more absolutely is actually irrelevant, the difference is whether more skills or more people matter more marginally to your situation.

Where Would You Get More Marginal Benefit?

Marginal benefit is a very useful concept from economics. The idea is that many activities have diminishing return. The 80/20 rule is essentially a more specific restatement of diminishing return, with the first twenty percent of opportunities generating eighty percent of the total results.

Advice giving is hard because it needs to make an assumption not just about the absolute worth of an activity, but where you are on the marginal benefit curve. What might be good advice for someone in the state of high marginal benefit may be lousy for someone further down it.

The networking/skill-development trade-off is a clear example. If you never cultivate connections and your network consists only of people you met by happenstance, there’s a good chance that each extra hour you invest in networking might benefit you more than an extra hour improving your craft.

However, the opposite is true if you’re a socialite with no job skills. Knowing a lot of people is useless if you’re useless. If you can’t do the things people will pay for, it doesn’t matter who you know.

This is why I find the debates people have over these issues silly. If you are just comparing absolute benefit, then you’re arguing an irrelevant point. The only thing that matters is where there is more marginal benefit, and that will vary case-by-case.

Skills and Relationships Form a Self-Reinforcing Cycle

The situation gets more complicated because people and skills form a positive feedback loop. The better people you know, the faster your skills can improve. The better your skills, the more important people you can meet.

Consider the first point: that a better network drives better skills. This is because many opportunities for rapid skill growth are not available to everyone. They are in limited supply, and like all opportunities, they flow through relationships.

This can have an impact in subtle ways. When I started this blog, my writing was predictably lousy. It’s nearly impossible to be a good writer when you’ve never written before, and I was no exception. The question is, how do you become a better writer?

Feedback is a big part of improving as a writer. If you don’t have feedback on what people like and don’t like about your writing, you’ll improve much more slowly. How do you get feedback? From readers. How do you get readers? From traffic. How do you get traffic? Generally from other websites linking to you. How do you get other websites to link to you? It helps to befriend people who run other websites.

In most cases it’s even more direct. My friends have helped me become a better entrepreneur, often by giving me insights into my business it would have taken years to uncover. You might have growth opportunities in your career that can only come through other people. In many cases you can’t separate your skills from relationships.

Now consider the opposite direction of causality: that better skills help you meet more important people. Here the reasoning is a little easier to follow. People like people who are high-value. If you have built valuable skills that people want, then more people will want to meet you.

The best freelancers I know for a particular skill charge exorbitant rates and turn down most their work. They’re so good that people can’t ignore them.

Cycling Introversion and Extroversion for Career Growth

The problem is that the habits and behaviors that help you build your skills are often contradictory with the ones that help build relationships. I just spent almost a week meeting people and having drinks at SxSW, but I certainly wasn’t getting any work done. Similarly, meeting with new people while hard at work is a major distraction.

I think the conflict between these two behavior types is a major reason for the debate surrounding which is better to focus on. Most people can agree that both are necessary to some extent, but integrating them into one personality can feel almost impossible.

My solution has been to cycle between modes of introversion and extroversion. By flipping between the two, I can continue to work on both parts which are important for my career, but not sabotage my progress by holding conflicting habits.

When I’m in introversion mode—I like to get work done. I prefer to meet with fewer people, work in isolation most of the day, wake up early and generally implement as fully as possible all the productivity advice I recommend on this blog.

When I’m in extroversion mode, I’m quite different. I stay out later. I drink and party. I meet people, and generally don’t worry about being too productive. In fact, “being productive” in the domain of relationships can even be detrimental, as you look like you’re trying to use people instead of building trust and connections.

These two modes often have incompatible habits, so there is a certain amount of work transitioning between them. When I’m coming off an introversion period, I sometimes have to force myself to socialize more than I want. When I’m switching back to it, I sometimes need a few weeks to readjust to the quieter pace.

How Often Should You Cycle?

For me, I feel the ratio of introverted to extroverted behaviors that maximize my benefits is around 70/30. However, this ratio itself is highly dependent on your career and where you sit along those marginal benefit curves. An engineer working at a corporate job may be closer to 90/10. A start-up entrepreneur in a marketing position may be 30/70.

The length of time in each phase depends on your schedule. When I was doing the MIT Challenge, I was in introversion mode for almost the entire year straight. Other times I’ll switch back and forth every week or month.

Cycles don’t need to be polar opposites either. This month is mostly extroverted for me, travelling to conferences, meeting people and speaking. But I’m still getting my regular work done and trying to make moderate progress on my projects.

What defines the cycle is simply a temporary shift of your priorities. When you’re in an extroverted mode, you’ll sacrifice a little productivity for your relationships. When in introverted mode, you’ll sacrifice a little of your connectedness for getting work done.

Done properly, I think cycling the introverted and extroverted behaviors we all possess is a better solution than to try to perpetually maintain balance. It also helps resolve the inner conflict many of us feel in our careers over whether we need to spend more time making connections or working hard.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Okay, file this piece of advice in the pile that nobody is going to follow (even though they probably should): you should read more textbooks.

Let’s assume for a second that you’re one of the few people who does read to learn more about the world. Let’s also assume that you’re interested in topics that are heavily researched: finance, health, nutrition, science or psychology. This probably eliminates most people, but I’m guessing as a reader of this blog you’re more inclined to such intellectual pursuits.

Ask yourself where you get information about these topics. Blogs? News? Popular non-fiction books?

There’s nothing inherently wrong with these sources. Some blogs and popular non-fiction books are crap—but many are not. Sometimes sacrificing empirical rigor can also be useful if the content is more pragmatic or impactful.

But if you do care about a subject, it probably makes sense to read at least one general textbook on it. That textbook may not fill you with the detailed knowledge of a PhD, but it can give the foundation for evaluating many other ideas.

Why Not Textbooks?

The value of reading a textbook (or, better, doing an online course) is that it gives you a baseline for examining other aspects of that field. Taking one physics course would be enough to know why perpetual motion machines are scams.

Similarly, if you’re going to read books on the financial crisis, political blogs or start investing money—maybe it makes sense to have read one book on basic economics. I find it baffling that people have complex economic and political philosophies but haven’t learned concepts like supply and demand.

Ditto for psychology. One psychology textbook will hardly make you an expert. But it will at least make you aware that truths can’t be concluded from a single study, or that generalizing from a very narrowly designed experiment is dangerous.

The point of reading at least one textbook is to give an awareness of (a) the fundamental concepts most people agree with in a field and (b) where experts disagree.

Opinions and Experts

This blog is my opinion. That’s not necessarily a bad thing, as long as you realize that’s what it is. I’m not infallible, so there are probably quite a few opinions I’ve shared that are false.

I read other blogs that are mostly opinions. I like those blogs because they challenge me to think about topics, or introduce me to ideas I wouldn’t previously considered. As such, I try to strive to do the same in my own writing, open up new questions rather than just provide answers.

This is also true for the things where I’m an “expert”. I write about learning methods based on my experience and from working with students. I also try to use the science as best I can to guide the methods that I then test extensively.

Even then, I’m probably wrong about at least a few things. This is why I strive hard to push my own knowledge on the topic so that I am constantly adjusting or reevaluating past ideas.

The Danger of Only Using Secondhand Expertise

As a blogger, however, I’m also guided by other constraints. I need to write things people want to read. I would never write anything I knew to be intentionally false or misleading, but sometimes that means I write less about a topic that is boring, even if it is equally important.

For example, I consider doing practice questions with solutions to be one of the most important methods for learning technical subjects. I’ve stated this before, but there really isn’t much more to that. Just do a lot of practice questions.

My methods like the Feynman technique, metaphors, visualization are things I spend significantly more time covering because they are unusual to students. Despite that, they probably only took up about 20% of the time during the MIT Challenge, in comparison to about 40-50% doing practice problems.

All writers face these constraints. Science journalism tends to hype results more than the research would warrant. Pop-psych books tend to make the field appear more unanimous in opinion than it really is. Bloggers categorize the unusual or interesting details first.

Reading a textbook, which is less influenced by these constraints, can give you some awareness of these biases and correct for it in your thinking. I won’t stop reading blogs or popular books—textbooks are dry and often impractical—but having knowledge of one or two helps me balance some of the biases inherent in popular writing.

Degrees of Belief

I’ve written before that the only appropriate way to look at knowledge is through degrees of belief. This means that almost nothing (aside from logical truths) is known perfectly. Instead everything is known to different degrees of certainty.

Scientific principles like relativity are so well-established we can safely say they are correct to some minuscule measurement with enormous accuracy. That’s enough to warrant rounding down that doubt to zero for most situations.

Higher-order theories in social sciences or popular opinion have more doubt built in. That doesn’t mean they need to be rejected, simply that you give yourself more room to reject them later if better theories are generated.

For now, I’m confident in the learning techniques I use, but I’m always looking for better models that might have more evidence and therefore better reliability. The hard part is realizing that this is an ongoing process. You can never just put your hands up and say, “Done!”

Different sources of information have different degrees of evidence as well. A blog article providing an opinion has significantly less evidence than dozens of controlled, well-repeated studies on a particular fact. When the two directly conflict, I side with the research.

However, often the research isn’t in yet. In these cases, I enjoy others’ opinions since it lets me entertain speculative theories while also allowing me room to continue investigating. I’ve enjoyed all of Malcolm Gladwell’s books, but it would be ridiculous to assume that he isn’t making any assumptions or leaps to stitch together a cogent narrative. I’m willing to accept this uncertainty, but I wouldn’t mistake it for fact.

Thinking in degrees of belief is not an easy task. I also understand the attitude that we need to draw a line somewhere, above which all facts are compelled to be believed, below which anything can be safely ignored. But, ultimately I think this is a weak position as well. It is often abused to allow you to accept some opinions but not others with exactly the same volume of evidence.

Well-Rounded Knowledge

My advice is to read one textbook on a subject for every 4-5 popular books or 50-100 articles you read about it.

Reading only textbooks is probably impractical. I want knowledge not just for knowledge’s sake, but to do something useful with it. Reading a book about exercise that distills research into practical tips is probably more useful than a textbook in physiology. Same for personal finances, learning, productivity or nutrition.

Reading only popular nonfiction is probably misleading. If you’re going to read a dozen books in personal investing, it probably makes sense to at least understand the rationale behind the efficient market hypothesis. Some authors will do this for you, but a lot of it won’t because of the constraints mentioned earlier.

Picking Textbooks

I’ve used the word “textbook” here loosely, but broadly I’d say it means two things:

1. The book tries to describe established viewpoints, rather than argue for a particular one (except where there is consensus). Academic textbooks are good for this because universities usually try to pick books without any severe bias.
2. The book focuses on fundamental concepts necessary to understand the field, not just minor details or conclusions. A good textbook should teach you how to think about a field, not just what to think.

Since textbooks are rarely the trending topics on social media, finding good ones comes down to searching for them. Look for ones that have good Amazon reviews from researchers in the field, or ones that are used in classes at major universities. Older editions are often better, because you can get them used for cheap.

Have you read any textbooks that you felt were engaging and informative about a topic you’ve studied? Please share them in the comments!

Edit: March 6, 2013 – Reader, Luke, has posted a link to a fantastic resource for finding great textbooks. Check out LessWrong’s best textbooks on every subject.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

The most important skill is execution. Having great ideas, wise decisions or clever strategies comes second. The ability to get things done is paramount.

This why I sigh when I hear people complaining about being unable to stay motivated on a project because they aren’t sure whether it’s the right one. These people have it backwards—if you can’t get projects finished, it doesn’t matter if it is the right one.

Ask yourself this: if you gave yourself a project that you had to commit to, no matter what, for one year, could you see it through? What about five years? Ten?

Tying Yourself to the Mast

It took me awhile to realize that the get-it-done-no-matter-what discipline was the skill I lacked. Before I started this blog, I had many ideas that failed to realize. Business ventures that never hit the market. Personal goals that were abandoned soon after they had begun.

Every time they died, I had the same excuses. It wasn’t a good idea (but my next one was perfect, of course). I was bored with it. I wasn’t motivated.

Maybe these excuses were valid. Maybe the projects were bad ideas, boring or not motivating. But most ideas are. Few ideas are perfect in conception—it’s through the grueling process of execution that they get sculpted and improved.

The triggering point for a change in me was realizing that the thing I lacked wasn’t a good idea—but the ability to finish. So I changed my aim: for my next project, I would set tight constraints and finish it, no matter what.

I can’t say discipline was instantaneous, but reframing the problem helped considerably. Because I recognized that my inability to execute was my true weakness, I stopped trying to make excuses and focused on trying to get it done. Even if it was a bad idea, boring or not motivating.

Odysseus tied himself to the mast to avoid the allure of the siren’s song. Knowing he would be tempted, he constrained himself in advance so that he couldn’t make a rash decision. If you’ve struggled with execution in the past, you need to find your own mast to tie yourself to, so that you won’t give up for any reason.

Stubbornness isn’t always a virtue. Often there are good reasons to give up. But those decisions are much easier to make when they are made on a firm foundation of discipline. If you base all your decisions on the temporary whims of laziness or fatigue, you’re likely to crash upon the rocks.

Training Discipline with 30-Day Trials

Small projects are a good starting point for training discipline. They are short enough that even if their concept is seriously malformed, you won’t waste too much time.

I started with thirty-day trials. This is where you commit to a new habit or behavioral change for an entire month. Good candidates are exercising every day, waking up at a particular time or giving something up, like drinking or smoking.

While the habit changes themselves are worthwhile, the biggest benefit of doing this practice was that it strengthened my self-discipline. Each trial was a burst willpower, like lifting a heavy weight. With enough repetitions, the weight becomes easier to lift.

I remember jogging at 6am in the morning after having spent the entire night awake at a party, to make sure I didn’t miss a day. I remember waking up at 5am every morning while reading the unabridged edition of Adam Smith’s The Wealth of Nations.

Neither of these were necessary. I’m sure if I had skipped one day, my fitness habit wouldn’t have suffered. It’s also unlikely I needed to grind myself through reading what turned out to be a long-winded and boring book.

But the reason I did these was that I had tied myself to the mast of those one-month 30-Day Trials. I had committed to that trial, so I would see it through, regardless of whether it was necessary or enjoyable.

I wasn’t always successful either. My exercise habit took four attempts before I finally made it through the last one. Discipline is a lot like physical strength. If you’ve never been to the gym before, you won’t be bench-pressing 300 lbs.

I’d like to say that building discipline is easy, that there’s just some productivity “hack” you can use to have more willpower. But there isn’t. The only way to strengthen your ability to stop giving up is to stop giving up.

Why Bother?

To many people, the idea of pushing yourself that hard is a little silly. Even if people don’t come outright and say it, there is a certain condescending attitude we have to the overly self-disciplined. Nobody likes a try hard, and building self-discipline is practically the definition of trying hard.

In many ways, ignoring the social pressure is harder than the act of discipline itself. I used to keep my goals private because I didn’t want to add social pressures to my internal ones. I don’t worry about it now, but that’s probably because I’ve already built a great deal of confidence from years of practice.

It’s not silly. While I remember vividly my 6am post-party jog and 5am reading sessions, I also remember another moment.

Around five years after starting this website, I had a major project flop after a year of lousy income. I felt convinced that my business wouldn’t make it—and despite spending thousands of hours of work—I was still washing my laundry in a bathtub to lower my expenses.

At the time, however, I was still finishing school. Starting a new business was impractical, so I had decided the best course of action was to hold out a bit longer. If this business wouldn’t work, after I graduated, I would move onto something else.

Despite my doubts, I put together another project and tried again. This time it was Learning on Steroids, which forms the basis of my income today. All of this happened just a few months after convincing myself I wouldn’t be able to make it work.

I can’t say what would have happened had things gone differently. But I can’t help wonder whether all those 6am jogs and early-morning reading sessions helped me stick through just a little longer.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Last fall, I finished the MIT Challenge. While the challenge was exciting and educational, the tight deadline didn’t give me any time for extracurricular projects. When I finished, I wanted to work on a small and fun project that would test some of the things I learned.

The result was WordSmith, a program that lets you play the game Scrabble against a computer. I’ve made the project free and open-source, so anyone can play it and also see how it works. Visit the download page to try it out.

For the rest of this article, I’m going to explain how the program works. While it’s not tremendously complex, it does show a little of the computer science knowledge I picked up through the MIT Challenge. If CS or Scrabble don’t interest you at all, feel free to skip this article, as I’ll be back next week with my normal writing.

Building an AI that Plays Scrabble

I’ve always enjoyed the game Scrabble. The game requires a mixture of vocabulary, strategy, pattern-recognition and luck. Unlike chess, AI-researchers’ favorite game, luck and outside knowledge play a role. Even a perfect Scrabble player may still lose due to unfortunate letters or an unlikely countermove.

Because of this, I wanted to see if I could make a computer that plays Scrabble. Not only because then I could play a game I enjoy without needing a ready opponent, but also because a strong computer opponent would teach me more about the game.

So, I built WordSmith. It was a fun project, not only because I enjoy Scrabble, but because the problem was interesting enough to require clever algorithmic tricks, but not so tricky I couldn’t do it part-time over three weeks while on vacation in Paris.

How WordSmith Plays Scrabble

WordSmith plays by searching the board for all possible tile placements and then chooses the best one. It evaluates this not only based on how many points a particular move gives, but also by using heuristics to avoid technically higher-scoring moves which are probably detrimental in the long-run.

When playing the AI, you’ll notice a blue progress bar as the AI “thinks”. What it is actually doing is trying every combination of letters it can play and keeping track of which moves are both valid and score enough points. I’ve built the AI to give up after 15 seconds (which is why the blue bar doesn’t always finish), but at least on my computer it does retrieve the highest possible scoring word over 95% of the time.

This doesn’t mean WordSmith is impossible to beat, however. I’ve beaten it roughly 10% of the time in the matches I’ve played. Scrabble is a lot of luck and often the technically best word isn’t considerably higher scoring than one an advanced player would pick.

WordSmith also uses heuristics. A heuristic is a fancy CS way of saying “rule of thumb”, meaning a simplified rule that will tend to increase performance when applied. I had started developing this capacity in the program, but there’s always the possibility of better heuristics, so this can give human players a strategic advantage.

The only heuristic I’ve tested that has made it into the final build of WordSmith is one that is conservative when playing good tiles. Blanks and high-point letters (Q, Z, X and J) are incredibly valuable, so if WordSmith can’t find a good use for it in the first turn it finds one, it will wait.

This omits some of the obvious rules of thumbs that human players actually use. Leaving a triple-word score spot open is dangerous, but WordSmith is oblivious to this misstep. Similarly, playing long words opens up more possible countermoves than defensive, compact playing.

In technical terms, ignoring the timeout feature, WordSmith is statically-perfect, but not dynamically-perfect. This means a clever player can outmaneuver WordSmith even though WordSmith never fails to recognize the highest-scoring word.

How I Built the Program

Now that I’ve explained how WordSmith works, I’m going to explain how the algorithm was actually designed. I’m going to try to be as non-technical as possible, but I want to also try to cover some interesting details.

The first step was to build a program that allows humans to play Scrabble. This was not too difficult, although the scoring algorithm was more complex than I had anticipated.

Once I could play Scrabble by myself, and ensure the rules were upheld, it was time to build a computer that could do the playing automatically. The basic algorithm is fairly simple:

1. Make a set of “slots” where all possible moves can be placed. Don’t consider the individual tiles yet, just look at where it is possible to place tiles in a valid move and make a list of these.
2. For each slot, try placing all permutations of your tiles onto the board. For blanks, that also means trying every single one of the 26 possible letters.
3. For each possible play, check whether it forms all valid words (not only in the principle direction, but in all new crosswords) and calculate the points. Keep track of the highest scoring play while you proceed.

This uses a very common AI strategy called searching. If you can visualize this algorithm, it is as if the computer is scanning through the space of all possible moves and looking for the best one.

Speeding up the AI

There’s just one problem with this approach—it’s incredibly slow. Mid-game plays may consider a few thousand slots, for each of these there are over 5000 permutations and over 3 million if we consider blanks. The scoring algorithm is not trivial either, so this means that considering all options in a brute-force way can take several hours or more on a modern computer. I wanted an AI that could play in around a dozen seconds.

From here a lot of tricks were used to prune the search space. I’ll briefly describe a couple:

1. Scoring is complex, testing whether the main word is valid is easy (O(1) hash-table lookup, for the CS geeks). Only words that were valid along the principle direction would be scored thoroughly.
2. Before placing a blank, figure out if there are any words which match that blank configuration. X_YGYK doesn’t mean anything with any letter, so we can skip trying all 26 times.
3. Since blanks have zero points, we only need to figure out if one letter assignment is valid, since all others will result in the same score. WI_ is the same whether you played WIZ or WIN (provided all crosswords are also valid in both cases).
4. The algorithm for calculating possible slots can produce duplicates, so making sure every slot is unique can cut the processing by 30-50%.

All of these algorithmic optimizations are still correct—they will still always produce the best scoring word, no exceptions. Even so, because the algorithm depends on the number of possible tiles (and blanks), considering every possible option can still take a long time in extreme cases.

I didn’t want to ever have to wait a few minutes to play my turn, even in an unusual case. To fix this, I set a time limit that the computer cannot exceed when deciding its move.

This also made use of another trick. By sorting the tile slots by length, I could make sure the small words (which are much faster to compute all possibilities for) are tested before long words. Using this method, I could then calculate that the algorithm is perfect 95% of the time with a time limit of 15 seconds on my machine.

This type of algorithm is also a common CS trick, very similar to the idea of lossy compression. The idea is that if you can still preserve most of the information (or in this case, computation) but you drastically reduce size or processing time, the tradeoff can be worth it. This is one reason why videos load so much faster than GIFs—one is intelligently compressed and the other is not.

Statistics and Other Fun Stuff

Once that was complete, the program was essentially finished. I expanded it to include a few other cool tricks that can give some insights into how to win at Scrabble.

One question I considered was, what are the most important words to know in Scrabble?

Now that I had a program that played each Scrabble move (statically) perfectly, I could actually test this. So I had the AI play against itself for nearly a sixty thousand moves and compiled the results. Some interesting factoids:

1. The most useful word in Scrabble is ‘QI’. (An accepted spelling of chi, the Chinese life force). This is followed by RE, ER, YE, IN, IT, TI, ZA. In fact, ‘QIS’, ‘QAT’ and ‘AYE’ are the only three-letter words to break the top 100.
2. Short words are far more useful to know than long ones. The most-used 5-letter word was 50x less common than the most-used 2-letter word.
3. The blank is the most useful tile, worth on average 12.9 more points. The letter ‘G’ is the least useful.

These statistics helped me train the algorithm. By collecting tile worth, I developed a heuristic which discounts high-value tiles so the computer won’t waste them in play. I then ran this modified AI against its predecessor a few hundred games and could show that its win-loss ratio was statistically significant.

I had hoped to build other heuristics, such as one that would avoid playing words that enabled high-point countermoves (such as leaving a precious triple-word score vulnerable). However testing these heuristics showed the AI performed worse, meaning either my formulation of the heuristic was misinformed, or that rule of thumb is less useful to my AI than I had previously believed.

One unfinished feature in the program was going to be a sliding difficulty scale. I had considered trying to limit the computer’s vocabulary to use only commonly known words. My strategy involved using Google’s n-gram library to set a threshold vocabulary so the computer wouldn’t use unusual words.

Scrabble and Having Fun

I didn’t make this program for any reason other than for the fun of making and playing it. As such, I’m releasing it free and open source.

The game isn’t as polished as many commercial applications. Mostly because UI design isn’t my specialty and I did all the graphics and sound myself. Also because working on the AI was what interested me for the application, so I didn’t spend much time making it pretty.

The code is also a lot messier than I had hoped. This was partially some poor planning, and partially because the algorithm grew more complex than I had expected as I added heuristics, statistics and other details.

The program is open-source, so if anyone wants to make improvements or modifications to it, feel free to send them to me. If they are worthwhile I’ll be happy to showcase them along with my own edition. In particular, I’m interested to see if anyone can demonstrate if different heuristics can reliably outperform my own WordSmith without changing the timeout on the algorithm.

However, if you just want to play the program, that’s fine too. I’ve even included a training mode that lets you check what the computer thinks you should play, so don’t worry if you’re not the best Scrabble player. Just go to the download page to get a copy.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

I’ve often heard the titular expression in conversations about professional success. Uttered with a sneer, it seems to point at the unfairness of life and the hopelessness of the masses of people without good connections.

It’s also an expression that is mostly true. Talent and effort matter, of course, but the gears of the machine are greased with favoritism. No discussion of career success can omit the importance of the people you know.

I don’t see this situation as being necessarily pessimistic. I want to argue two things:

1. This isn’t unfair.
2. Building connections is an equally important part of building a career as is the work you do.

Is a World Run by Connections an Unjust One?

Saying that, “it’s all who you know,” smacks of injustice. After all, what possible relevance could who you went to school with, or whether you play golf, have on your ability to do your job?

I have friends, usually the intellectually-powerful-yet-somewhat-introverted engineer and programmer types, who find the idea that relationships might matter more than talent as insulting. Either they reject outright the importance of having a network, or they view it as a distasteful reality.

This is nonsense. Relationships matter because they impact performance. Since they impact performance, it’s not unfair that some opportunity would flow through them.

When I’m hiring people to do work for me, my first place to ask is my friends to see if they have worked with anyone before. I’m often willing to pay more for these people than I would for someone I hire through job postings. To the prospective freelancer I’ll employ, this sounds unfair. After all, shouldn’t the job go to the person who is most qualified for the given price?

I’ve learned that technical proficiency is only a small part of what makes a good candidate. Being trustworthy, timely and easy to communicate with are often more important, yet these are features difficult to ascertain from a portfolio. Sterile recommendations from past employers I’ve never met before say almost nothing.

I’m willing to pay more for contractors that come recommended, because they do the job better. It’s not nepotism, it’s basic economics.

The same is true of business connections. I have a no-guest-post policy for this website, yet I’ve had people guest post here and probably will continue to do so for the future. The catch? I only let people write here if I read them and respect the work they’ve done prior to writing here. It may seem unfair, but it has meant each of my guest posts have done incredibly well for exposure and traffic, benefiting both me and the author.

A big way I would find out about someone is if they were in my network. As a result, my friends end up writing guest posts and I say no to strangers. They need to be interesting writers, certainly, but if they weren’t good at networking, I probably wouldn’t know them.

I can’t say whether the amount relationships matter is ideal. Maybe myself and others care about them too much, and as a result, create unfairness. All I would like to imply is that there is a just rationale for the titular expression, even if it may not be optimal.

If It’s Who You Know, What Should You Do?

I grew up in a small town, isolated from any possible useful connections or mentors. I’d also say I’m definitely not a born networker. While some people have a disposition towards easily making new contacts, it is definitely a skill I’ve had to train.

That said, I believe networking is a skill that can be learned. It’s not just having an extroverted personality or natural charm. Some of the best networkers I know are actually somewhat introverted.

Networking also doesn’t need to be sleazy. The people I know with huge networks are the complete opposite of that. If you feel sleazy, it means you’re doing it wrong.

As someone who isn’t at all a natural connector, I’ve found focusing on three core traits have helped me the most:

1. Be high value
2. Be generous
3. Be gregarious

#1 – Be High Value

People want to meet people of high value. If Barack Obama asked you to lunch, would you say no? Of course not. Obama is high value and even meeting with him over lunch could have tremendous benefit.

The desire to meet and befriend high-value people is an innate one. It’s why people want their photos taken with celebrities or have their autographs. It isn’t just a calculating maneuver, but a genuine urge to associate with people perceived to be higher value than oneself.

Therefore, the biggest step you can take to improve your networking ability is to be higher value. There’s a few broad strategies I’ve seen be successful for increasing your value in a networking context:

1. Do interesting things. I have a friend that has done martial arts training for the military and another who speaks over ten languages. These are people others want to meet, even if they have no interest in linguistics or fighting.
2. Improve your skills in a demonstrable way. Talent makes you higher value, so a big way to improve your network is to demonstrate you are good at something valuable.
3. Have other good connections. If you build your network enough, you can become a higher value person, simply by knowing other high value people. This one is not a beginner step, but it does explain some of the exponential returns you can get from networking.

#2 – Be Generous

A big turning point in my own networking was to stop trying to network with people who could help me, and focus on people I could help. I’m much more likely to reach out to someone if I feel I can help them as much as they could help me.

Everyone wants to network with people who can easily help them. I’ve found it much better to focus on who I can help first. As a result, I contact a lot more bloggers who have really great writing, but are still new, than I try to befriend already-famous authors.

Being generous is also about anticipating what others might need. Whenever I get an email with, “Let me know if there’s any way I can help!” I usually ignore it. Why? Because I have no idea how that person can help, I barely know them.

When I reach out to someone, I try to research them carefully to figure out how I might be able to help them, and read for clues in our initial conversation. Maybe they can use advice, website traffic or would benefit from connecting with another friend of mine?

Generosity isn’t a last step, it’s an attitude you need to have at all parts of the process. The best networkers I know are relentlessly focused on helping their friends do well before themselves.

#3 – Be Gregarious

The final step is to be eager and willing to meet new people. If you wait at home on Friday nights hoping for the phone to ring, you’re not going to get a lot of dates. The same is true with building connections.

Because I started my business in such an isolated place, I grew up on networking via the internet. With Skype and video calls, this isn’t a terrible way to get to know people, especially since it drastically broadens who you can contact.

But even if internet contacts can still be valuable, there is still a lot of value in meeting people face-to-face. I strive to travel regularly to meet people I’ve met online, and I try to take any opportunity I can to meet other interesting people in-person.

Being gregarious is most useful when you don’t currently need a favor. Too many people go into networking mode at conferences or social gatherings only when they need something. This contradicts the spirit of generosity and results in spurned connections, even if it works occasionally.

An Introvert’s Guide to Networking

I’ve hesitated writing about this topic recently, not because I do poorly, but because I’ve had the chance to meet people who are spectacular. I’ve changed my mind because I realize that this is part of the problem: many spectacular networkers are naturally good at it, so regular people feel it’s not a skill that can be learned.

The fact that it is, indeed, who you know for many opportunities, doesn’t need to be an obstacle. Getting to know people is definitely a skill that can be learned. Because it depends on being a better person, it’s a good skill to learn, not just a distasteful necessity.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Anything you try to improve will have a growth curve. Imagine you ran everyday and you tracked your speed to finish a 5-mile course. Smoothing out the noise, over enough time you’d probably get a graph like this:

Here, improvement works on a logarithmic scale. As you get better, it gets harder and harder to improve. Elite athletes expend enormous effort to shave seconds off their best times. Novice athletes can shave minutes with just a little practice.

Logarithmic growth is the first type of growth. This is where you see a lot of progress in the beginning, but continuing progress is more difficult.

Now imagine a different graph. This time you’ve build a new website you update regularly and you’re measuring subscribers. This graph would likely look very different:

This is exponential growth, the second type of growth. Website traffic is often exponential because as a blog attracts more readers, there are more opportunities for word about the blog to spread. A blog with zero traffic also has zero word of mouth.

I’ve noticed most things tend to be either logarithmic or exponential growth. Despite this, linear progress is what most people expect. We tend to expect things to move in the same direction or rate as they have in the past. This violation of our expectation leads to some mistakes in how we set goals and act on them.

The Logarithmic Growth Mistake

The first kind of mistake is assuming straight-line growth, when reality is actually logarithmic. There are many situations which usually fit this pattern:

• Athletic performance
• Weight gain/loss
• Learning a new language
• Productivity
• Mastery of a complex skill

Assuming straight-line growth means overconfidence in long-term progress. As a result, it is easy to hit plateaus if the difficulty isn’t deliberately tuned to break your comfortable rhythm.

Logarithmic growth also implies it is easier to slide back down the hill. Since it is so steep in the beginning, carelessness can mean those immediate gains are often easily lost. Losing weight quickly may be more desirable than losing it slowly, but it also risks putting it back on again quickly if you stop your efforts.

Logarithmic mistakes are common, but so to are mistakes when reality is in the other type of growth curve.

The Exponential Growth Mistake

Once again, people view progress linearly when it is, in fact, exponential. Some examples which usually follow exponential curves for at least part of their lifecycle are:

• Technological improvement (e.g. Moore’s Law)
• Business growth
• Wealth
• Rewards to talent/career

Unlike logarithmic curves, almost nothing is consistently exponential. Most are only exponential over some range of values, outside of which they are logarithmic again.

No business reaches near-infinite values, even though this would be implied by an exponential curve. Eventually market share is saturated or competition stabilizes growth. However, for many types of businesses, exponential growth can persist for much of the business lifecycle.

Exponential curves are somewhat rarer than logarithmic ones, however the mistakes here can be even more costly. Expecting linear growth when it is actually exponential causes many people to give up way before they should.

Several years ago, I remember being disheartened when drawing straight-line projections of my business income. At the going rates, it was often a dozen years away or more before I could make a full-time living at it. However, my income turned out to be exponential. Despite spending more time below my ideal projections, I ended up eventually surpassing them.

Exponential areas of life are full of quitters. People making linear assessments of viability and giving up before the exponential curve can take hold. Not all of this is irrational, many exponential areas are high-variance as well. However, the problem with exponential domains is that the feedback can often look bad, even when it is good.

Is Your Growth Exponential or Logarithmic?

Both types of mistakes, failing to recognize exponential domains and logarithmic ones, are costly. It’s not always clear which is which, but by reasoning about the features of a domain you can get a better sense which one it is likely to be.

The easiest way to tell is to look at how other people have progressed in that field. Don’t pay attention to their rates, just pay attention to the shape of their growth trajectory. Is it the kind that slows down with mastery or speeds up?

Exponential growth stories tend to be the ones where a person struggled long and hard with little to show for it, then started quickly gaining success. These stories often seem to be overnight successes, since they ignore the years of obscure toiling.

Logarithmic growth stories involve a continuous dedication to remaining at your peak. A fluent speaker of a second language will comment on the regular practice required, not the sudden moment where it all “happened”.

Other features of the environment can tell you whether something is exponential or logarithmic. Exponential environments often seem to be based on a catch-22 or circular causality. Having money makes it much easier to make more money, not only because of interest rates but because people give rich people opportunities they wouldn’t to those without success.

Logarithmic domains usually have diminishing returns once the “obvious” solutions are taken. The more you work at them, the harder you have to look for insight to generate new gains. Occasionally you can discover overlooked opportunities and regain the steep part of the curve, but this is inherently difficult.

Growth Mindsets

In logarithmic domains, two mindsets are important. In the beginning, high-growth phase, the emphasis needs to be on maintaining long-term habits. Since growth is fast initially, care needs to be taken so that it won’t slide back down once effort is removed.

In the later, low-growth phase, the emphasis needs to be on habit breaking. Since low-growth is often caused by calcifying routines, deliberate effort needs to be taken to break out of that comfort zone.

In exponential domains, the mindset of resilience and endurance are critical. Since feedback is sparse and generally negative during the initial part of the curve, it takes dedication to persist. Part of the reason, entrepreneurs are often consumed by their own vision is that it helps block out the negative feedback until they can reach the exponential part of their growth.

Now it’s your turn. What are you working on right now? Is it more exponential or logarithmic? What features of your environment do you believe make it that way? Share your thoughts in the comments.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Flow is the mental state of complete engagement. It happens when you are fully immersed in an activity that is neither too difficult to be frustrating, but not so easy as to be boring. First described by Dr. Mihály Csíkszentmihályi, the concept became popular because it was seen as a key to both performance and happiness.

Flow is great. I’ve previously written about it before, and I believe that if your regular work doesn’t offer you at least some opportunities for flow, it’s unlikely you’ll be passionate about it enough to succeed at it.

But there’s a myth that flow also maximizes learning. That, to get the fastest learning rate, you should be in a state of flow-like engagement with the learning material. This is likely false.

Deliberate Practice and Frustrating Difficulty

The idea that flow would also maximize learning is an optimistic one. After all, wouldn’t it be great if the state of being which produces pleasure and happiness also causes the fastest growth?

From Anders Ericsson’s research on deliberate practice, however, I have serious doubts about this being true. Deliberate practice, the activity tightly coupled with skill development, is also a grind. Far from being a flow-like state of optimal difficulty, deliberate practice is strenuous and often frustrating.

Flow comes from a balancing point of difficulty. It occurs when you have enough mastery to perform well, but the task is not so trivial as to allow distractions to enter the mind.

Deliberate practice is a recalibration of that balancing point. Personal experience tells me that ideal deliberate practice sits much closer to frustration than to flow. Learning something new, in the most efficient way possible, is exhausting.

Flow is Great for Work, Bad for Learning

Flow is an equilibrium, deliberate practice is a disequilibrium. This isn’t the only area of human psychology or biology where we see growth coming from disequilibrium. If you want to gain strength, most of the gain comes from the final, agonizing repetition, not the first ones of mild exertion.

Another hint to the nonoptimality of flow for learning is in Dr. Ericsson’s own distinction between practice and work. Work is the performance of your skill to useful ends—a violinist concerto or a basketball game. Practice is pushing your performance to a new level—repeating the hardest few bars of a solo or perfecting your layup. Games and concerts are fun, drills aren’t.

Flow is an ideal state for working—it shows you’ve achieved enough mastery to perform well, but that the task is still at your level. As such, I think it’s worth striving for in your regular day-to-day tasks.

Learning is not optimized by playing at your level. It’s optimized by playing above it, where you feel you can barely cope.

Achieving Fluency

Learning a new language is a great example of this. When practicing, there are two points worth noting: flow and mastery.

The first is the point of flow—this when the language feels challenging, but not overwhelming. In a beginner, that could mean parroting premade sentences. In an intermediate to advanced speaker, that could mean having conversations along the lines you normally follow.

The second is the point of mastery. This point is where you are learning the most new things. It is painful and often awkward, but it results in the fastest growth. For a beginner that means trying to speak in the language, before one feels comfortable. For more advanced speakers, it is putting deliberate effort into improving your accent, grammar or adding new elements of vocabulary.

Benny Lewis, who speaks nearly a dozen languages fluently, is often able to achieve conversational fluency in only a couple months with a new language. His method is dedicated to this uncomfortable point of speaking before you’re ready in the early phases, and an insistence on corrections in the later phases.

I met Benny when he was living in Berlin. Despite only having learned German for two months at the point when I met him, he was already speaking better German than other people we met who had lived there for years.

No Pain, No Gain

This entire article may sound overly discouraging. After all, if optimal learning is frustrating or painful, isn’t it unrealistic to expect people to do it?

Learning may have some short-term intensity, but the rewards are great too. Immersion in a language can have some short-term pain, but every gain you make in speaking ability is satisfying.

I remember some hard moments with near non-existent French when I first moved to France, but being able to have whole conversations in French just a few months later was rewarding enough that I’m convinced I’ll learn more languages in the future.

Working through mathematical problem sets during the MIT Challenge was often overwhelming. But being able to cover the material so quickly, hooked me onto learning new technical concepts in the future.

Treating learning like exercise is a big shift for many students. Because they’ve largely used passive methods which are considerably less intense, they’re also used to spending a lot more time studying. Studying is better seen as a series of sprints than as an endless jog.

I’ll admit, pushing hard to get to the next level of ability isn’t always fun. But because you achieve growth faster, you don’t need to spend as much time. I’d rather have some moderately intense bursts with more downtime than endless library study sessions.

What Does This Mean for You?

I’ve found that maximum learning occurs at an intensity that is uncomfortable to most people. Possibly too uncomfortable for many students who are used to learning at much closer to a flow-like state of effortless engagement.

As a result, full intensity might not work for all tasks if you’re unable to withstand the discomfort. Running until you puke probably isn’t the best way to start a habit of regular exercise.

Instead, I’d suggest dialing up the intensity slowly. Here’s some things you can do:

1. More active recall. Active recall is problem sets, flashcards or anything where you are forced to produce an answer from memory. Passive recall is re-reading notes or watching videos.
2. More problem solving. Solve new problem types to test your knowledge in new areas. Pretend you’re teaching the solution method for old problem types.
3. Ratchet up the environmental difficulty. New to a language? Try speaking it with a native, even if only for a sentence or two. Advanced in a language? Try delivering a speech, writing an article or training yourself to reduce your accent.

Because optimal learning isn’t necessarily optimal enjoyment, this also means there will often be a tradeoff between the two. You want progress to be intense enough to reach your goals in a timely fashion, but not so intense you lose your desire to learn the subject at all. Where you strike that balance will probably depend on what your goal is and how much motivation you have.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

When I wrote Learn More, Study Less in 2008, one of the big pieces of advice was to “learn it once”. The main idea being that, while review is still necessary, you shouldn’t procrastinate on what you’re learning—if you don’t understand something, the pre-exam cram session isn’t the time to learn it.

I stand by that original intention and I still believe that, given the classroom environment where ideas are presented in a structured way, it’s an appropriate maxim.

However, another interpretation I hadn’t really intended was the idea that you should only require one pass of an idea to deeply understand it. “Learn it once” has meant, for some, “never learn it again.”

This second interpretation bothers me. First, it’s almost certainly false. Deep understandings almost always take a couple passes. That doesn’t mean you should postpone developing insight—repetition without understanding is just memorization. But it does mean that “learn it once”, taken in this context, is bad advice.

Is Repetition a Good Thing?

I’ve had mixed feelings about repetition being used as a learning tool. I’ve seen too many students memorize things that need to be understood. Even if you did need only memorized information and zero understanding, creating connections and using vivid mnemonics makes the same process faster.

But, provided blindly repeating information isn’t your first action, repetition isn’t bad. Most of the research shows that repeated viewings of the same idea strengthen memory. Taking the spacing effect into account, learning something multiple times might even be better than trying to cluster it in one session.

A better, albeit more wordy, maxim might be “Don’t procrastinate on learning ideas as you’re exposed to them. Build insights and connections before memorizing anything.” Doesn’t have the same ring as “learn it once” but it’s a little more nuanced and accurate.

If I were to implement this advice as a student, I would still do all the things I mentioned in Learn More, Study Less. Create metaphors, explain ideas as if you were teaching them to someone else, use mnemonics for fickle details. But I’d also add active recall, as an important method for strengthening long-term memory.

Peeling the Onion

Most learning tasks inevitably break down to one of two groups:

1. Things to be understood.
2. Things to be remembered.

Most items fit somewhat in both categories, but rarely equally. Physics has some memorization, but answering problems depends crucially on having insight. Languages have some insight, but they’re mostly memory and practice.

A common mistake, and hence a theme of my writing, is to assume something requires mostly memory, when it actually requires mostly understanding. I die a little inside each time I see a student memorize a formula without trying at all to figure out how it works.

Repetition for things that need remembering is obviously useful. Holistic learning is still important, because memory is associative, so connections between ideas will improve recall. But even a well-tuned mnemonic system will still benefit from repetition for purely arbitrary information.

Repetition for concepts can be dangerous, if only because it can sometimes create the illusion that you understand it. When I was doing MIT classes, I had to watch myself that I wasn’t merely memorizing solution patterns, since they gave the feeling of progress but would collapse whenever a new type of question was introduced.

However, provided you are actively building insight, repetition can also be useful for understanding. I see understanding most concepts like peeling an onion. Each time you explore it, you have the opportunity to look at it in a new way. Repeated exposures allow you to burrow down to deeper layers of understanding.

I felt this way with learning many mathematical concepts. The depth of insight in something like Fourier analysis is so large it’s impossible to fully grasp it with a first pass. New exposures, particularly those from different vantage points, reveal new insights each time.

This is true even of ideas you may have already “learned”. A simple idea like addition can be peeled all the way to Zermelo-Fraenkel set theory. Having learned something once here is misleading, because learning is inevitably incomplete.

Practical Advice for Self-Learners and Students

What does this mean for you? For the most part, I don’t believe it changes the implications of what I’ve been advising all along. Don’t procrastinate on understanding. Never substitute memorization for insight. Use connections and mnemonics to remember ideas with less effort.

However, I will explain how this has manifested itself in my own learning so you can see how it might affect yours:

1. Almost all learning is incomplete. Circling back to gain new insights or refresh your memory of old ideas is a part of learning. I do feel sometimes self-learners are too conservative in their exploration of new knowledge, but this doesn’t mean learning something is a box you can tick and never return to.
2. Repetition, provided it supplements holistic learning, can be a good tool. In the past I’ve been hard on spaced repetition systems, but now I see that they can also be a way of automating the reminder process. Supplement, don’t substitute.
3. Use active recall. Active recall is where you try to answer a question, without seeing the solution. This is superior to passive recall where you see the question and answer at the same time.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

There’s a lot of ways to procrastinate. Extra pushes of the snooze button, the final cram session before an exam, waiting until midlife to pick a career. Maybe you’re procrastinating right now.

I used to believe most of this was just inertia. With a push, you could start rolling and finish the work with less effort. Procrastination was just temporary laziness.

Now I’m not so sure.

Consider chronic procrastination. These are the projects you drag your feet on regardless of how many pushes you get. Inertia can’t be the core problem here.

But if action isn’t the cure for procrastination, what is?

“Just Do It” Culture and Distaste for Planning

We live in a culture that has taken Nike’s famous slogan to heart. Action is celebrated, planning is obsolete. The world changes too quickly for stuffy things like research, what we need are brave pioneers and risk takers to do the bold things overly-cautious people cannot.

The slogan has some merit, doing things is how things get done (even if tautologically so). If you want change, there must be some action to cause it.

By putting all the emphasis on momentum, however, we might ignore other more fruitful approaches. Instead of pushing on a locked door, why not spend some time to find a key first?

Conversations with fellow blogger Cal Newport clued me in on this possibility. Cal’s philosophy contradicts much of the just-do-it approach. He researches exhaustively before starting. Despite this slowness, he’s hardly a non-starter—he is currently working on his fifth book.

Obsessive Research (Or How Not to Procrastinate on Hard Things)

Analysis paralysis was a buzzword of a self-help era with audio cassettes and three-day seminars. The idea was that spending too much time thinking about an idea would stall action. At some point, you just need to stop thinking and just do it.

Now I believe that this is almost entirely true: thinking excessively about something can lead to inaction. But I’m not being self-contradictory, I’m saying this because researching a goal isn’t the same as thinking about it.

Research isn’t pondering. Researching is an activity to gather more information. Here are some research activities which are certainly a lot more than day-dreaming:

• Reading at least a dozen books on the subject.
• Creating an experimental prototype.
• Interviewing several experts.
• Doing a pilot project, as a proof of concept.

It’s important to note that none of these are the same as starting in full force. A pilot or prototype is purposely reduced in scope or quality. The purpose isn’t to build something to get started, but to learn what works in the project itself.

Research like this has a major advantage over actually doing the project. Because its entire purpose is as an information probe, there is no possibility of failure. As much as we try to laud failure as progress, nobody acts that way—failures still sting. Research allows us to gather information before we’ve put any of our ego or commitment on the line.

Why Research Kills Chronic Procrastination

A big cause of chronic procrastination is uncertainty. You don’t start that ambitious project, not because you lack momentum, but because you don’t know enough. Research fixes that in an obvious way, replacing ignorance with impetus.

Uncertainty also hits as a general malaise. Students I work with often have chronic procrastination issues. Research can help here too: more information can either give them more reasons to be excited to move forward, or it can give them the acute reasons they need to make a change immediately.

Obsessive research helps beyond minimizing uncertainty however. Even with projects where uncertainty can’t be reduced, the power of obsessive research is that it builds commitment. The more you research, the more invested you become in starting, so that when you do, you’re far less likely to back out on a whim.

I researched obsessively before doing my MIT Challenge, and I think it was a big part of the reason I didn’t give up, even when the subjects were often difficult. Now I’m preparing for another mega project, and I already see how doing preparation and research is escalating my commitment.

Where Does This Method Work?

Like all advice, I don’t believe obsessive research has universal applicability, but I do think there are a range of situations it is well suited for, and still more where it might work.

First, obsessive research is helpful where you feel a lot of uncertainty or doubt. Human psychology is peculiar in that, even if a project appears more difficult than you had assumed, this is still more motivating than lacking any information.

Second, research can help with projects which fail due to lack of commitment. Regular people like to poke fun at those who set resolutions to get in shape each January, but I feel sorry for the people who have given up on setting any goals because they know they can’t commit to them.

Finally, I think research can also be useful in some cases where chronic procrastination is due to unexciting prospects. Many students or employees are stuck in this demotivating trap. Research can help either to find exciting opportunities, or at least to get the pressure to pull the kill switch and make a change.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

Most academic concepts have fairly narrow usage. You can draw analogies between fields, but these connections usually rest on you understanding both sides of the metaphor sufficiently well.

Consider the Fresnel equation in physics. With some effort you might be able to draw an analogy between this equation and another domain. But I’d doubt you could say that understanding the equation led to overflowing insights in history or art.

However, hidden within all the ideas with tenuous crossover implications, there are rare ideas which seem to illuminate far beyond what they were originally designed to explain.

The problem, of course, is that academic subjects are generally pursued in isolation. Each profession learns the paradigms and tools of their trade and only borrows from other schools of thought when those ideas are directly relevant to research. Specialization, not general purpose thinking, is the norm.

With that, I’d like to pose the following question: what are the intellectual ideas you’ve mastered, which have broad scope in understanding the world?

My Picks for Powerhouse Ideas

I’ll kick off the discussion with my picks for ideas which everybody should know.

1. Evolution and Natural Selection

This is an idea everyone has an opinion about, but few people really understand. Once understood, however, the analogy is powerful for explaining how complex systems develop and change over time. Languages, businesses, technology, social customs and diets are just a few of the areas which borrow similarities to biological evolution.

Some resources I recommend which show the breadth of the idea:

• The Selfish Gene
• The Red Queen
• The Language Instinct
• Why Do Beautiful People Have More Daughters?

 

2. Bayes’ Rule

Bayes’ Rule has been described as the secret of the universe. It is a simple mathematical formula which helps you calculate the probability of an event. On the surface, just a formula you would memorize and apply on an exam and immediately forget. But going deeper, you can see how it may even be the basis of all rational thought.

The best introduction to the rule is Eliezer’s guide: An Intuitive Explanation of Bayes’ Theorem, although the implications of this snippet of mathematics may take you years to unravel.

3. Economic Efficiency

In 1776 Adam Smith wrote On the Wealth of Nations, which would later become the foundation for modern economic theory. He laid out the basics of how automatic forces guide and improve our material existence.

The idea of efficient markets is a controversial one, if only because there are many instances when the forces Smith recognized can break down. But that doesn’t make the idea any less powerful as an explanatory concept. Just because we rarely see ideal spheres on frictionless planes, doesn’t mean classical mechanics isn’t useful for explaining motion.

Some resources:

• The Wealth of Nations
• MIT’s Microeconomics Course
• Economics in One Lesson

 

4. Signalling and Game Theory

Along with evolutionary psychology, signalling is perhaps the best single theory for explaining human behavior. The basic idea is that we take actions not only for their direct consequences, but to communicate and deceive others who have imperfect information.

Game theory is a useful intro topic since understanding the basics of static and dynamic games, and getting the mathematical intuition behind them, makes it easier to fully see signalling play out in everyday life.

5. Biases and Heuristics

The field of biases and heuristics in psychology is a popular one nowadays, with websites like LessWrong dedicated to the art of human rationality. Even if it is a popular field, that doesn’t downplay its importance. By understanding the errors humans make in reasoning, we can at least understand our frailties, even if we cannot fix them.

As an aside, I considered myself well-versed on this topic before reading Daniel Kahneman’s book, Thinking, Fast and Slow, however even I found dozens of new insights, so I strongly recommend reading the book even if you’ve been exposed to this concept previously.

6. Gödel’s Incompleteness Theorem

I picked this one as a last concept, not because it is universally useful, but because of how profound the result is. Basically, Gödel proved logically that there exist true things which can never be proven, or alternatively, that there are truths which can never be known.

If you’re feeling like going down the rabbit’s hole, I suggest this book: Gödel’s Proof.

Now It’s Your Turn

I’ve given my shortlist, now I want yours. What’s an intellectual idea that you feel everybody should know? Bonus points for any ideas that are largely removed from pop psych or self-help. Please give your idea, along with why it is so broadly useful, in the comments.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

The titular question was posed on Quora, and one rich person, who claims to have made $15M after selling a tech startup offers a surprisingly nuanced and insightful answer:

“Being rich is better than not being rich, but it’s not nearly as good as you imagine it is.”

Other rich responders were less enthusiastic. Another writes:

“Made $20M on second start-up. Finally, real f’you money. I feel no better. Yes, I bought a better house. I didn’t even bother to buy nicer cars. Who cares. I just bought some more jeans. Look, I am intellectually proud and gratified to have this money. But it didn’t buy my freedom, which I had from before. It didn’t improve the quality of my life.”

Informally, I’ve also had the chance to meet rich people. My sense from those encounters is that being rich is nice, but it’s hardly the panacea people make it to be. I doubt many of those rich people are significantly happier than they would be with only a moderate income. Some may even be less happy.

The research also sides with this intuition. Kahneman shares in this talk that the relationship between money and happiness flattens at around $60,000 per year.

However, when I ask people who aren’t earning a lot of money about this, the responses are nearly universal. They would be ecstatic to have such wealth, and can’t comprehend why those spoiled, ungrateful rich people aren’t living in utopia.

I imagine even now many of you are rolling your eyes at even the mention of rich people complaining about being rich. But that’s exactly my point. Why are our intuitions so different from reality? If being fabulously rich is only a moderate boost to happiness, why don’t we see it that way, in advance?

Location Independence, Freedom and Money

Being rich is just one fantasy. You could replace the entire preceding introduction with “location independence”, “fame” or “a relationship”. People who don’t have them feel they would change everything. People who do have them find they don’t change nearly as much as they had thought.

In that way, I suppose I too live a life which is only a fantasy to some. I’m a full-time blogger. I can live wherever I want, work on whatever I want and how much I want. While I’m not super rich, I’m earning much more than I had expected when I got started nearly seven years ago.

I say this not to brag, but to provide contrast. For the first eighteen years of my life I lived in an isolated tiny town in northern Manitoba. My parents are middle class, but I definitely had my poor moments in college. I washed my clothes in a bathtub for a year because the laundromat was too expensive.

I’ve been enormously lucky, and I’m both grateful and happy. Success has made me happier, but like the previous respondents, the change is less dramatic than you would think. I worry about money less, I travel more, and I certainly don’t have the entrepreneurial angst that came with starting an unproven business.

I feel my success may have even been buffered from the disillusionment of the previous examples. I never really strove for location independence, fame or money. All I wanted to do was to be able to run a business without needing a job. My dream was to do something, not to have something. This might explain why having a lot of money and nothing to do feels empty. Life is more about doing, than having.

I was fortunate enough to anticipate that money and location independence wouldn’t make me happy. So when they entered my life, I saw them as being nice perks, not disappointments because my day-to-day life remained mostly unchanged.

The one thing I didn’t predict in advance was the orienting power of my goal itself. Having devoted myself for seven years towards a goal that I not only achieved, but surpassed my expectations was wonderful. But it also led inevitably to ask what could possibly fill the gap?

The Hungers of Life

Imagine, for a moment, the last time you were extremely hungry. So hungry that all you could think about was food (if you can’t think of that, then imagine a time you were in another physical pain, such as exhaustion, heat or cold). In those moments, that pain constrains your life—it focuses your attention and defines how you see the world.

Now remember what it was like to eat food again after that moment of hunger. It probably felt good, for a few minutes, and then there was nothing. When you’re starving, food feels like it will fill you forever and make all your worries disappear. When you’re satiated, the pleasure lasts only for a moment before your mind orients itself to something different.

I argue being rich is like being full. It’s not a bad feeling, and certainly better than being hungry. But as long as you’re well fed, food just isn’t something you think much about.

Most of us have had enough experience with both hunger and fullness to realize that being well-fed doesn’t mean life becomes perfect. But few people have had the same sense of ‘fullness’ with money, to have had the same experience.

If you currently have some hunger in your life for something, be it money, fame, freedom or a relationship, realizing that these hungers are a lot like the physical hungers can help you avoid the disappointment you might feel when you realize that satiation didn’t fix all your problems.

Life Needs Constraints

Great designs always have constraints. In many ways, design is defined by constraints and using them elegantly. Often these constraints are from the environment, but good designers also self-impose constraints.

Life, in this sense, is like a process of design. Without constraints, you have a mess, not blissful freedom. People who live well either take constraints from the environment, or impose others on themselves to live in a more meaningful way.

Some constraints aren’t particularly inspiring of great works. The lowest possible budget hasn’t been the constraint that has produced the best architecture and art. But, it can also be argued, neither has having an unlimited budget with no other limiting scope.

Having more money, location independence, or success in any other dimension, often lifts environmental constraints from our lives. This is usually a positive thing, as I believe self-selected constraints probably result in better art than the random assortment of constraints given to us at birth.

But with more freedom, there is more discipline required to constrain your art.

Maybe you’ll never be rich or location independent. Maybe you even scoff at the idea that these represent real problems, and aren’t just narcissistic whining.

However we live in a rapidly changing world where many of the old constraints may no longer apply. Location independence becomes more common, as GDP rises, more people will live far above subsistence. As old constraints become less relevant, it will be up to us to decide what the new ones should be.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

After a year spent learning MIT’s computer science program independently, I’ve gotten a number of emails from people who want to do the same thing. People who want a computer science education but don’t want to wait four years and pay thousands of dollars to get it.

I’m very happy with how I conducted the challenge, but I wouldn’t recommend most people follow my plan identically.

My goal was to publicly tackle a hard academic program. I wanted to learn practical skills, but because I’m a full-time blogger, not a full-time programmer, I didn’t mind taking classes which were more math or science.

Many of the people writing me want to repeat the MIT Challenge so that they can become great programmers, possibly landing jobs in the industry. Given that’s a more popular goal than investing a year full-time just for intellectual satisfaction, I would definitely make some modifications to my challenge for those interested in repeating it.

Downsides to an MIT Education

Whenever you design a curriculum, you’re making tradeoffs. You choose what to emphasize and what to downplay. MIT does this. Every school does.

There are many advantages of an MIT education. The material is excellent and the teachers are world class. The expectations are high, so passing an exam is a good sign you really understand the ideas. The material, especially for graduate classes, is focused on cutting-edge innovations in the field.

But there are also tradeoffs MIT makes when teaching it’s students which I feel are important to take into account if you’re wanting to self-study the program and not receive a degree for your effort.

MIT is Math and Theory Heavy

Doing the MIT Challenge I learned a lot of math. Now don’t get me wrong, I enjoyed those classes and I wouldn’t have changed it if I had gone back. But the amount of math was probably unnecessary for being a decent programmer.

MIT’s bias towards theory and math-heavy classes, and the relative dearth of programming, was a contentious point during my challenge. Many people claimed it was impossible to do the programming work of a 4-year challenge in one year. Except that MIT doesn’t do nearly as much programming as other schools.

In the four classes of computer science I took from my alma mater, the primary emphasis was programming. Even the exams were more than half handwritten computer programs we had to make.

MIT does have programming assignments, and I did a lot of them (and there were also lab classes which had more programming which I had to substitute out during my challenge). The difference is only relative to my experience with other schools which did tons of programming but probably wouldn’t have gone into the full correctness proof of the SVM algorithm using Lagrangian multipliers.

My impression of MIT is that it assumes learning to program is the easy part, so it puts more emphasis on the big theoretical ideas of computer science. This is an understandable position, because they want to prepare their graduates to do innovative work, not just prepare them for their first entry-level job.

MIT Teaches a Rounded Program

Another laudable goal of MIT instruction is that they expect all their graduates to be well-versed in the science and arts. This roundedness to the curriculum is noble, but perhaps impractical for someone who mainly wants employable skills.

During the program I studied biology, chemistry, physics and economics. These were great classes, and I enjoyed them, but they could probably be omitted if you only wanted to learn about computer science.

MIT is Highly Extracurricular

Another reason I feel MIT’s course curriculum downplays the programming aspect is that they expect you’ll learn that on your own. Most MIT students will do summer internships or extracurricular projects which will supplement the theory-heavy courses.

Since my challenge, and any self-education attempt to replicate MIT’s curriculum would omit these side projects, it’s important to take that into account.

MIT Merges Electrical Engineering and Computer Science

Another quirk of the MIT program is that it also covers electrical engineering. As a result, I learned quite a bit about electrical engineering which is arguably only distantly related to computer science in the day-to-day work of a programmer.

I think there is some value of learning the basics of electrical engineering, particularly computation structures which shows how computers are made of wires and semiconductors. However if my main goal had been practical, not intellectual, I would have skipped most the EE courses to put extra emphasis on the CS.

I’m glad I followed MIT’s curriculum, but I’m aware that many people aiming to follow me have different goals and are unaware of these emphases in MIT.

What I Would Change for a Programmer

You can see the curriculum I followed here, and MIT’s actual 4-year computer science curriculum. It’s not a perfect replica, but my main goal was to get as close as humanly possible, both in volume and in subject matter.

However, if your goal is to become an excellent programmer or have the foundation of a computer science degree to serve as the foundation of a professional skill, I don’t think perfect imitation is the ideal benchmark.

Here’s how I would change the curriculum I used to better suit those aims.

What I Would Keep

Here are the courses I thought were particularly useful to know, as a programmer. The list is not exhaustive, and there are useful classes I didn’t take. Many of these are more theory oriented, but they teach useful theory for someone who wants to build things in code:

18.01: Single Variable Calculus
6.01: Introduction to EE and CS I*
6.02: Introduction to EE and CS II
6.042J: Mathematics for Computer Science
6.006: Introduction to Algorithms
18.06: Linear Algebra
6.046J: Design and Analysis of Algorithms
6.034: Artificial Intelligence
6.004: Computation Structures**
6.033: Computer Systems Engineering
6.005: Elements of Software Construction

*Technically, this class requires having done the two intro physics classes, but considering the only reason for this is to understand voltages and simple circuits, you could just learn those from KhanAcademy videos and skip the two physics classes (although they are very well taught).
**This class requires Circuits as a prerequisite, but I believe you’d have sufficient coverage from 6.01 and 6.02 to cover it and taking 6.002 would be overkill.

These are the core CS courses I took. I would also consider taking additional courses based on your particular interests in computer science. I did machine vision, computer graphics and theory of computation as well, but the above 11 classes would be the most beneficial, even though it’s only a third of the classes I took.

What I Would Add

In terms of more classes, I would first consider adding an intro programming class if you’re starting from scratch. 6.01 is technically MIT’s first class, but it races through the beginnings of Python which could be daunting for someone who is new. I’d also include Khan Academy to make sure you have the appropriate math background to start taking calculus (not strictly necessary, but useful for understanding AI, graphics and some more advanced topics).

Some other academic subjects I would like to cover better, but weren’t explored deeply for the MIT Challenge would be:

• Operating Systems – This is covered in 6.004, but only as a section.
• Programming Paradigms – I did this separately from the challenge, but this set of lectures from Stanford is really good.
• C and C++ – Most of MIT’s classes are done in Python. One used Scheme and the main software development class used Java. I didn’t use any C during the MIT Challenge, and I only used C++ in the computer graphics course which didn’t teach using the language beyond the basics.

More Projects, Fewer Courses

Given the stripped down courses I listed plus the few additions I mentioned, you would have roughly 15 courses, less than half of the ones I took in the challenge. Adding to that, I would put the rest of my emphasis on working on interesting projects, not more academic classes.

The MIT Challenge did have projects, but school projects are very different from self-started ones. With an academic project the constraints and aims of the project are provided. With a self-started project, you must discover and create the constraints for yourself.

I’m in this phase myself, doing self-started projects to learn more about computer science and get to a higher level of skill as a programmer. I don’t have a perfect guide, but here are some rough categories of projects I want to work on to test my skills in different areas:

• Create a project that involves some machine learning
• A web application
• A project involving interesting uses of graphics or sound
• Build something using robotics

I’m not sure I’ll get to all of these projects, but they are interesting areas and allow me to expand on the theoretical foundations I’ve picked up in classes. Were I doing the MIT Challenge again with the sole aim of becoming a programmer, I would have spent at least half my time doing these kind of unstructured projects.

Since the MIT Challenge, I’ve finished one project on that end. I built an AI which plays Scrabble, so you can play against a computer that always plays the highest scoring word. For my next mini project I think I’m going to redevelop it so that it can be a JavaScript application I can put online for free.

Self-Education and Impractical Knowledge

I learn because I’m interested in many things and I’d love to be educated on many topics even if they don’t have any real practical necessity in my life.

One of my favorite classes was Theory of Computation, a graduate class exploring what computation is and the inherent powers and limitations of algorithms. It was fascinating, but the material was so esoteric I doubt I’ll ever need it when writing a computer program.

I’m aware that most people don’t share my love for knowledge, even impractical, so I feel my above modifications are better suited for someone who wanted to follow the MIT Challenge, but strip away the superfluous.

To me, the power of self-education is that the world is a fascinating place that I’d like to know more about. Not needing to pay tuition or deal with labyrinthine bureaucracy to get at it is a huge advantage. I know many people who paid thousands to study a useless major because they didn’t realize that learning doesn’t need to be expensive.

But self-education is also a tool you can apply to get better jobs, clients or business opportunities. If that is your primary aim, then the power of self-education is that you can tailor it specifically to your goal.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.

I love people who say they “know” what they need to do, they just don’t do it. These are the same people who claim that without credentials, connections or whatever they happen to lack, success is impossible.

The truth is, most people put almost zero effort into figuring out how success actually works in their field, so you can outclass the majority, just by doing a little research.

“All Employers Care About is a Degree”

A few days after my MIT Challenge completed, it was picked up by the social media website, reddit. While many of the comments were supportive of the challenge, a number disparaged that the concept was nice but that, without a real degree, the knowledge was almost useless.

Interestingly, HR recruiters also responded to the thread and they told a different story. Many of them claimed that, contrary to the students’ expectations, they were very interested in hiring someone who was aggressively self-educated. One from a large firm even offered to set me up with a job interview.

I’m certainly not saying degrees are useless, or even that employers would make no distinction between my challenge and a real degree. But what amazed me was that nobody had actually done any research. The assumption was that all employers cared about was a degree, even though that was being contradicted in the exact same discussion forum.

You Don’t Know What it Takes to Be Successful (and That’s Okay)

I’m picking on these commenters, but in reality I was brainwashed with many of the same lies. I thought that software companies, particularly big firms, would never make exceptions on degree requirements, and that they wanted new hires to have mastered the particular technologies they use.

The difference, in my case, is that realizing I didn’t understand what employers care about, I decided to ask someone who works for a big software firm. I was surprised to learn that he had several colleagues who worked as programmers without computer science degrees—one even studied music.

I was even more surprised to hear that he said particular mastery of a technology wasn’t their main hiring criteria. They expected to do a lot of training internally, so overall programming ability mattered more than skills with a particular language.

After just a single interview, I can’t say whether these trends are the exception or the norm. However, even after just an hour of talking to someone, I was already beginning to see that many of the assumptions I had were wrong.

From Job Hunting to Customer Insight

When I first started selling ebooks and courses, the popular wisdom was: more is better. People wanted more content, more features and more material. Believing this, I made sure when I built my course I included nearly eight hours of audio and video content.

Therefore I was in for a big surprise, when I interviewed actual customers. It turned out that most of them had watched less than a third of the videos, even when they said they loved the content.

I changed this when I re-released Learning on Steroids. I recorded short videos, sometimes only a couple minutes, that honed in on the key ideas.

Interviewing actual customers turned up dozens of little improvements that would have gone untested had I stuck with my assumptions.

Use The Interview Method to Kill Faulty Assumptions

The big problem both with the students who thought all employers cared about were degrees and my product, was that we both thought we “knew” how it worked. Our assumptions could have been easily fixed, but we never put the small effort into fixing them.

The interview method is a way around this problem. You can kill a lot of bad ideas, just by talking to people who have a better understanding of how the field works.

The steps are simple:

1. Identify at least five people who are moderately successful in your field.
2. Reach out to them and ask if you can buy them lunch.
3. Meet up with them and get them to share their thoughts.

Yes, some people might be too busy or not have an interest in helping you. For that reason I also suggest picking people who aren’t likely to be getting tons of similar requests. A mid-level manager will probably consider your request for advice more seriously than Bill Gates.

Even if you do get some polite declines, researching and contacting a dozen or so people in this way will likely yield at least a couple interviews. The reason most people fail at this is that they never ask, not because it’s difficult.

I also suggest not directly asking for advice. Hindsight bias clouds many direct advice-giving efforts, so useful information ends up buried in useless platitudes. Instead, ask the person to share their story, or if they’re in a position of influence, what they look for when evaluating potential employees or clients.

Repeating this process several times will give you a road map of how success works in your field. This doesn’t mean you should blindly follow their example, sometimes you may spot faster avenues that the majority of successful people missed. But at least understanding how success typically works gives you an enormous edge, even if you opt for more creative solutions.

Case Study: How Do You Become a Successful Architect?

Architecture is a notoriously difficult field, and when my good friend Vat graduated almost none of his peers were able to find work in the post-recession economy.

Even after finding a job, his prospects weren’t looking particularly good. Pay is typically low, even for people with decades of experience and graduate degrees. Adding to the difficulty, he was hoping to one day open his own firm, something few people succeed at.

Following a conversation with Maneesh Sethi, regarding his own use of the interview technique, he decided to use it in understanding the career path of successful architects. He started a blog interviewing local architects about their thoughts on the profession.

Not only did he get hours of valuable advice on managing the difficulties of the career, the interviews also led to dozens of high-value contacts, job offers and referrals.

This is all during a time when most of Vat’s peers have either given up looking for work in the field, or gone back to school to delay life for another few years.

Why This Method Allows for Easy Wins

The interview approach is incredibly valuable for the advice it yields. Knowing how success works in your field is essential, even if none of the people you interview ever offer any material help.

But the method is more powerful because in reaching out to people, in order to learn from them, you often form relationships that turn out to be valuable later. People like people who are eager to learn from them, and have the discipline to apply it.

When I look at my own email response habits, I see I’m much more willing to respond to a blogger politely asking for advice, than someone callously asking for me to link to them.

Why the Interview Method Often Beats Reading Books

The interview method is often better than just reading a lot of books or expert opinions on the subject. Pundits like me have different constraints for writing than just merely giving accurate advice—we’re also need to be interesting, entertaining and original. This means great career or business books may leave out the boring, but essential details to be successful at something.

The other advantage of the interview method is specificity. Because you’re picking people to interview that are nearer to your situation, you’ll be able to learn a lot more details that don’t generalize well.

By interviewing customers, I learned which specific modules should be highlighted, something I could never learn reading a business author. By speaking with an actual hiring manager at a big software firm, I could know exactly how they conduct their interviews, something I wouldn’t know just reading a newspaper article about employment trends.

Most people don’t know what they don’t know. Doing several interviews to figure out how a field works can save months of effort and be the difference between success and failure.

Learn Faster, Achieve More
Get the ideas I don't share on the blog. Join my private newsletter and I'll give you my free rapid-learning ebook.