Ultralearning

See also Learn to Code Anything - Why Books Crush Tutorials - Universal, Timeless Knowledge.

Chapter 1. Can you get an MIT education without going to MIT?

The author went to the University of Manitoba, a middle-ranked school he could afford. He felt a Bachelor of Commerce was the wrong major and wanted to be an entrepreneur. He went back to study business, but realized that was geared toward big corporations, not starting your own thing. This led him to question whether going back to school was worth it at all. That’s how he started what he called the MIT Challenge: a twelve-month intense dive into the MIT computer science curriculum using their free OpenCourseWare materials. He focused on passing the final exams and completing the programming projects, figuring those two criteria would give him all the skills without the frills. He found that by moving through materials at high speed, he could get through a class in about a week.

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My problem isn’t with the French. Just Parisians.

_{(p. 11) Benny Lewis}

The author was frustrated that after living in France for a year on an exchange program, he didn’t learn as much French as he wanted. Then he learned of Benny Lewis, who learned languages in three months. Lewis’ strategy was to skip formal study and start speaking right away. This led him to become fluent in Spanish, Italian, Gaelic, French, Portuguese, Esperanto, and English, and later Czech and German. The author realized that this phenomenon of aggressive self-education wasn’t restricted to languages.

Roger Craig, at the time, was the highest-earning winner of Jeopardy! He had to solve the problem of how to study for a test that can ask any question.

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Everybody that wants to succeed at a game is going to practice the game. You can practice haphazardly or you can practice efficiently.

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Craig downloaded tens of thousands of questions from every Jeopardy! game and tested himself on those. He used data visualization to map his strengths and weaknesses, then focused on his weakest areas. He also employed spaced repetition software, designed to optimally time when you need to review material so you don’t forget without over-studying.

Another example is Eric Barone, who developed Stardew Valley over five years, declining computer science jobs to put full focus into it while working a minimum wage job instead.

The author’s strategy for the MIT classes changed over time. He went from completing a single class in a few days to spending a month doing three to four. He finished the entire curriculum in less than 12 months. Afterward, he started what he called the “year without English,” where he went to four countries and spent three months in each, not allowed to speak English from the first day. At the end of the year, he could confidently speak four languages.

He also spent a month improving his ability to draw faces, overcoming his biases by sketching from photographs hundreds of times with the same rapid feedback strategies. He got significantly better.

The ultra-learners he studied usually worked alone for months and years. Their interest tended toward obsession, but most of all they cared about learning.

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Ultra-learning isn’t easy. It’s hard and frustrating and requires stretching outside the limits of where you feel comfortable. However, the things you can accomplish make it worth the effort.

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Chapter 2. Why Ultralearning Matters

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Ultra learning is a strategy for acquiring skills and knowledge that is both self-directed and intense.

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The core idea is that you are in the driver’s seat. It’s not meant to be fun, but effective. The economist Tyler Cowen argues that with universal access to information, there’s going to be a deep skill divide and polarization. The economy is constantly changing, and we can engineer a response by aggressively learning hard skills.

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Average is over.

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Three cases where ultralearning pays off:

1. Accelerating the career you have.
2. Transitioning to a new career.
3. Cultivating an advantage in a competitive world. The ability to learn hard things quickly is going to be increasingly valuable.

Natural talent exists and influences outcomes (Terence Tao taught himself to read at two), but the author argues strategy and method matter just as much, if not more. You can also do ultralearning part-time or during gaps in work and school. It doesn’t require quitting your job, as the long process of becoming skilled suggests.

Chapter 3. How to Become an Ultralearner

One concern is that ultralearning success stories might be like finding a few flecks of gold through a ton of pebbles. The author tested this by bringing together a group of people to try it.

One participant wanted to do public speaking. After a month, he won his area competition. After less than seven months, he went to the world championships.

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I knew this project was going to be big for me when I started it, but it was definitely life-changing. I didn’t expect it to actually change my life.

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What differentiated him was his obsessive work ethic.

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Principles allow you to solve problems even those you may have never encountered before in a way that a recipe or a mechanical procedure cannot. If you really understand the principles of physics, for instance, you can solve a new problem simply by working backward.

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These are the nine principles that underlie ultralearning:

1. Meta-learning. Draw a map of the skill you want to tackle.
2. Focus. Gain the ability to concentrate for large chunks of time.
3. Directness. Learn by doing the thing you want to be good at.
4. Drill. Constantly improve your weakest points, because those are the most important.
5. Retrieval. Push yourself to recall information rather than passively review it.
6. Feedback. Know how to use it without letting your ego get in the way.
7. Retention. Understand what you forget and why.
8. Intuition. Develop it through play and exploration of concepts and skills.
9. Experimentation. Explore outside your comfort zone. Maybe you’ll find a method that works better.

Beyond these principles, there’s a broader ethos: take responsibility for your own learning. Decide what to learn, how to do it, and build the plan. You’re in charge.

Chapter 4. Principle 1: Meta-learning. First Draw a Map.

The prefix meta comes from Greek, meaning “beyond.” Meta-learning means learning about learning. Dan Everett uses meta-learning to not just learn a language but draw a map with theories about how the language works.

There was a study showing that a third language is easier to learn after knowing a second, backed by meta-linguistic awareness. This generalizes beyond languages.

To draw your map, break down the project into three questions:

• Why? Your reasoning. Make the motivation intrinsic, because if you don’t want to do something, you won’t learn it effectively.
• What? The knowledge and abilities you’ll need. Categorize into concepts (things to understand), facts (things to memorize), and procedures (things to practice).
• How? The resources, environments, and methods you’ll use.

One tactic is to interview people who are already successful at what you’re trying to do.

Once you have the what, figure out how to benchmark your progress. Find a curriculum and modify it: emphasize areas of weakness, exclude things you already know.

Most people under-research rather than over-research. But research can also be procrastination. A good rule of thumb: invest 10% of expected learning time into research. Compare the marginal benefit of more research versus more direct learning. If meta-learning is contributing more, keep researching. If not, get started.

The long-term benefits are even greater. Each project improves your general meta-learning ability, building a toolkit over time that only comes from putting in the work.

Chapter 5. Principle 2: Focus

Mary Somerville, born poor and discouraged from intellectual pursuits, still achieved greatness in math, languages, and piano. The ability to focus deeply is ubiquitous in intellectual accomplishments.

Problem 1: Failing to Start (Procrastination)

We procrastinate because we crave doing other things and have an aversion to the task. The first step is to recognize when it’s happening.

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Most of what is unpleasant in a task or what is pleasant about an alternate task is an impulse that doesn’t actually last that long.

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A useful crutch: tell yourself to spend only five minutes on the task. Once you start, momentum often carries you forward. You can also use a calendar to block out project time, but only if you actually follow it.

Problem 2: Failing to Sustain Focus

Flow is the state where you’re completely absorbed in a task with no self-consciousness, as discussed in AI Coding Killed My Flow State. But in ultralearning, flow isn’t always possible or even desirable. You constantly need to adjust your approach, which requires the self-awareness that flow suppresses. Don’t feel guilty when you can’t find flow.

Researchers find that people retain more when practice is broken into different studying periods (interleaving). If you have several hours, cover a few topics rather than just one.

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50 minutes to an hour is a good length of time for many learning tasks. If your schedule permits only more concentrated chunks of time, say once per week for several hours, you may want to take several minutes as a break at the end of each hour and split your time over different aspects of the subject you want to learn.

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Three sources of distraction:

1. Your environment. Phone off? Internet blocked? Multitasking feels fun but doesn’t work.
2. Your task. Some activities are harder to focus on than others. If you have a choice of tools, pick the one easier to concentrate on, as long as it still allows for deep learning.
3. Your mind. Negative emotions, restlessness, daydreaming. The solution is to acknowledge the feeling and let it pass without reacting.

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If you learn to let it arise, note it, and release it or let it go, this can diminish the behavior you’re trying to avoid.

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Problem 3: Failing to Create the Right Kind of Focus

There’s interesting research surrounding arousal and task complexity:

• Low arousal (sleepy) produces poor performance overall
• High arousal (too much coffee, exercising) creates narrow, brittle focus
• Complex tasks benefit from a more relaxed kind of focus

Start small. If you can’t sit still, try sitting still. Over time, frustration may become transmuted into interest.

Chapter 6. Principle 3: Directness

An architecture graduate found he wasn’t useful in the real world despite going to architecture school. He’d focused on design and theory, but the job required building codes, costs, and software. He got a print shop job and prepared a portfolio with direct exposure to real blueprints.

This illustrates the importance of directness. Most routes to self-education aren’t direct. We watch videos instead of doing, because the real thing feels uncomfortable. But directness is what differentiates ultra-learners. This is the same idea behind shadow coding over passively consuming AI output.

The opposite of directness is the classroom approach: studying facts, concepts, and skills without reasoning for how they’ll be applied. During the MIT challenge, the author realized the most important resource was the problem sets. Sometimes you can’t be completely direct, but you simulate to get as close as possible.

Transfer is when you learn something in one context and use it in another. Unfortunately, formal education has largely failed at producing transfer.

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Despite the importance of transfer of learning, research findings over the past nine decades clearly show that as individuals and as educational institutions, we have failed to achieve transfer of learning on any significant level. Without exaggeration, it’s an education scandal.

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Transfer is paradoxical. When we want it, we do not get it. Yet it occurs all the time.

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Directness helps in two ways. First, learning with a direct connection to the target domain reduces the need for transfer entirely. Second, learning in a real context means you absorb the hidden details that make knowledge flexible and transferable.

Tactics for directness:

1. Project-based learning. Learning to program by building a game, or competing in a pokerbots competition.
2. Immersion. Going to a country where your target language is spoken.
3. Flight simulator method. Simulating as many cognitive features of the real task as possible.
4. The overkill approach. Make it harder than it needs to be. Tristan de Montebello practiced public speaking at middle schools because the feedback at adult clubs was too soft.

Chapter 7. Principle 4: Drill

Benjamin Franklin was above all else a writer. As a child, his father noted his writing lacked persuasion. Franklin would take notes on articles in his favorite magazines and try to reconstruct the original argument from memory.

There’s a concept in chemistry called the rate-determining step: the slowest part of a process that dominates the reaction. In learning it’s similar. There’s always a bottleneck. If you’re weak at algebra, you’ll get wrong answers even if you understand calculus. Drills target the rate-determining step to speed it up.

This might sound like a paradox compared to directness. The tension resolves when you see them as alternating stages: the direct-then-drill cycle.

1. Practice the skill directly (write software, speak the language).
2. Analyze and isolate the rate-determining step or hardest component.
3. Drill that component in isolation until you improve.
4. Go back to direct practice and integrate what you’ve learned.

Early on, the cycle should be fast. Later, you can take longer detours into drills.

Drill tactics:

1. Time slicing. Dedicate a block of time to a specific drill.
2. Cognitive components. Isolate one cognitive aspect (e.g., pronunciation vs. grammar vs. vocabulary when learning a language).
3. Copycat. Copy the parts of the skill you don’t want to drill, freeing you to focus on the target component.
4. Magnifying glass method. Spend disproportionately more time on one component (e.g., 10x longer on research when writing an article).
5. Prerequisite chaining. Jump into something with many prerequisites, discover what you’re missing, and build back.

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Something mentally strenuous provides a greater benefit to learning than something easy.

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Chapter 8. Principle 5: Retrieval

The mathematician Ramanujan was poor but loved math. He flunked out of university because he didn’t like other subjects. Without access to proofs or explanations, he spent his time trying to figure out theorems for himself. Instead of consuming math, he had to reconstruct it.

There are three ways to prepare for an exam: review the material, test yourself, or create a concept map. Testing yourself outperforms all other conditions. The act of trying to summon knowledge from memory is itself a powerful learning tool.

Humans rely on judgments of learning (JOLs) to estimate how well they’ve learned something, and these are often wrong. If something feels easy, we believe we learned it. If you test people a few minutes after passive review, passive review wins. But days later, retrieval practice beats it by a mile. Whether you’re ready or not, testing still works better, especially combined with the ability to look up answers afterward. This is like speaking a new language from the very first day, or starting a project instead of watching tutorials.

Retrieval methods:

1. Flashcards (or spaced repetition systems). Work best for cue-response pairings like foreign vocabulary.
2. Free recall. After reading a section, write down everything you remember. Hard but effective.
3. Question book method. Rephrase notes as questions to answer later.
4. Self-generated challenges. Write problems for yourself to solve later.
5. Closed-book learning. Cut off the ability to look things up during practice.

Ramanujan was smart, but his genius came from obsessive intensity and retrieval practice.

Chapter 9. Principle 6: Feedback

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Everybody has a plan until they get punched in the mouth. Mike Tyson.

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Chris Rock went to small clubs to test new material because he valued immediate, honest feedback. What separates ultralearning is the immediacy, accuracy, and intensity of feedback. No feedback often results in stagnation.

However, feedback can have a negative impact in about 38% of cases. This happens when it’s aimed at ego or ability rather than at the work itself. Ultra-learners don’t act on every piece of feedback, and they recognize that fear of feedback is usually more uncomfortable than the feedback itself.

Three types of feedback, from least to most granular:

1. Outcome feedback. Tells you how you’re doing overall, but not what specifically. Like a letter grade without seeing which questions you missed. Entrepreneurs get this: you either sell well or poorly.
2. Informational feedback. Tells you what you’re doing wrong, but not how to fix it. Like getting your test back with wrong answers marked but no answer key. Programmers get this from compiler errors.
3. Corrective feedback. Shows you what’s wrong and how to fix it. Usually requires a coach, teacher, or answer key. This is the most valuable but hardest to find.

Immediate feedback is generally superior, though delaying the reveal of correct answers can sometimes help by providing spaced exposure.

Ways to improve your feedback:

1. Noise cancellation. Remove random factors you shouldn’t overreact to.
2. Hitting the difficulty sweet spot. Avoid situations where you always feel good or always feel bad. The edge of your ability is where feedback is most informative.
3. Meta-feedback. Evaluate whether your learning strategy is working, not just your performance. Track your learning rate over time to decide if you should try other methods. This is similar to tracking loss curves in gradient descent: if you’re not converging, change the approach.
4. High-intensity rapid feedback. Put yourself in situations where your work will be evaluated. The motivational pressure to perform can outweigh the informational benefit.

Don’t process feedback as a message about your ego. Get in and take the punches early so they don’t put you down for the count.

Chapter 10. Principle 7: Retention

Nigel Richards is a world champion Scrabble player and an obsessive cyclist. While cycling, he goes through word lists in his mind. His strategy focuses on memory over anagramming. Retention depends on employing strategies so the things you learn don’t leave your mind.

After learning something, we forget at exponential decay: most of what we forget, we forget almost instantly. Three theories explain why:

1. Decay. Memories fade with time. But this can’t be the only factor, since we can vividly recall events from early childhood.
2. Interference. Memories overlap in storage. Proactive interference: old knowledge makes acquiring new knowledge harder (you already associate a word with a different meaning). Retroactive interference: new knowledge erases old memories (learning while loops makes you confuse for loops).
3. Forgotten cues. The memory is still stored but inaccessible. This explains why relearning is faster than learning from scratch.

Four mechanisms to combat forgetting:

1. Spacing. Spreading learning across sessions hurts short-term performance but helps long-term retention. Space too closely and you lose efficiency; too far and you forget. Spaced repetition systems automate this trade-off.
2. Proceduralization. Procedural skills (riding a bike, touch typing) are stored differently from declarative knowledge (knowing theorems) and last far longer. You can lean into this by practicing skills until they become automatic.
3. Overlearning. Practicing beyond perfection. A single extra session produces a week or two of extra recall, and combined with spacing and proceduralization, the effects compound.
4. Mnemonics. Designed for remembering specific patterns. The keyword method converts a foreign word into something that sounds like a word in your native language, then creates a mental image as a bridge. The Guinness record holder for memorizing Pi knows 70,000 digits. But mnemonics require upfront investment, and recall from them is never as automatic as direct memory. They’re more like useful tricks than a foundation.

Forgetting is normal. There’s no way to avoid it entirely. We just employ these four strategies to minimize its effect.

Chapter 11. Principle 8: Intuition

Richard Feynman was known not just for solving problems but for seeming to see through them, arriving at answers that felt almost magical to others. His secret wasn’t raw talent alone but a deep, play-driven engagement with ideas. He would build physical intuitions for abstract math by imagining concrete scenarios.

Intuition is what lets experts skip steps that novices must work through laboriously. It comes from having so many examples and patterns internalized that recognition happens before conscious reasoning. This connects to the idea of intuition as a moat, something that becomes more valuable precisely because it can’t be easily automated or transferred.

The Dunning-Kruger effect means beginners often overestimate their understanding. Real intuition requires pushing past the feeling of understanding to the ability to actually use and apply knowledge. Feynman’s rule: if you can’t explain it simply, you don’t really understand it.

How to develop intuition:

1. Don’t give up on hard problems easily. Set a timer (10 minutes) and struggle before looking at the answer. The struggle itself builds connections.
2. Prove things to understand them. Don’t just read proofs; try to reconstruct them. This is Ramanujan’s method from the retrieval chapter.
3. Always start with a concrete example. When facing something abstract, generate specific cases first. Feynman always asked “what’s an example of this?”
4. Don’t fool yourself. Ask “do I really understand this, or does it just feel familiar?” Use the Feynman technique: try to explain the concept as if teaching it to someone with no background. Where your explanation breaks down is where your understanding is weakest.

Intuition isn’t mystical. It’s the result of deep practice and many encounters with a subject, built through the kind of exploration discussed in solving hard problems through pattern recognition.

Chapter 12. Principle 9: Experimentation

Van Gogh was not a prodigy. He started painting seriously at 27 and spent years copying other artists’ work before developing a style. His breakthrough came from relentless experimentation: trying different techniques, subjects, color palettes, and influences until he found something uniquely his. The Impressionist masters he admired all did the same.

Experimentation becomes more important as you advance. Early on, an established curriculum works fine. But once you’ve exhausted the basics, there’s no pre-set path. You have to try things and see what works.

Three types of experimentation:

1. Experimenting with learning resources. Try books, videos, projects, tutors, different curricula. What works for someone else may not work for you.
2. Experimenting with technique. Change how you practice. Vary the method, not just the content. If you’re always drilling one way, try another.
3. Experimenting with style. This is the creative frontier. Once competent, explore what makes your approach unique. Van Gogh’s thick brushstrokes weren’t a deficiency; they became his signature.

How to experiment:

• Copy then create. Start by imitating a master, then deliberately diverge.
• Compare methods side by side. Run A/B tests on your own learning strategies.
• Introduce new constraints. Force yourself out of familiar patterns. Restrictions breed creativity.
• Find your superpower. Combine skills in unusual ways. The intersection of two domains is often where the most interesting work happens, like applying graph search strategies to non-CS problems or using data analysis skills in a poker competition.

Experimentation is the final principle because it encompasses all the others. It’s the engine that keeps ultralearning from becoming a rigid formula.

Chapter 13. Your First Ultralearning Project

The book closes with practical advice for getting started:

1. Do your research. Apply meta-learning: spend about 10% of your total expected time planning.
2. Schedule your time. Block it on the calendar. Part-time is fine; intensity matters more than total hours.
3. Execute your plan. Use the nine principles as a checklist, not a rigid sequence. Adjust as you go.
4. Review your results. After the project, evaluate what worked and what didn’t. This meta-feedback feeds into your next project.
5. Maintain or master what you’ve learned. Decide whether to continue deepening the skill or shift to maintenance mode with spaced review.

The point isn’t perfection. It’s taking responsibility for your own learning and pushing harder than you thought you could.

Chapter 11, Principle 8, Intuition

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Do not ask whether a statement is true until you know what it means. Eric Bishop, mathematician.

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Simon was a genius, though he sucked at the humanities. He was in the bottom fifth of his class in college history. Even knowing his methods of magic doesn’t mean that you’d be able to do the same. Beginners tend to look at superficial features of problems, whereas experts focus on deeper principles. This approach is more successful as it gets to how the problems work. How then do you build intuition?

Rule number one, don’t give up on hard problems easily. Feynman loves solving problems. It works stubbornly on problems until they yield them. He often skipped shortcuts in order to understand things more. One way to introduce this is to give a struggle timer, where when you want to give up, set a timer for another 10 minutes. Even if you fail, you’ll be more likely to remember how you arrived at the solution when you encounter it.

Rule number two, prove things to understand them. Feynman didn’t master things by following along. He tried to recreate those results. Albert Einstein did the same.

Rule number three, always start with a concrete example. This forces a deeper level of processing the material. Developing instances of a problem forms deeper understanding which enhances later retention and intuitive understanding. This also gives you some feedback because if you can’t imagine an example that means you don’t understand something well enough.

Rule number four, don’t fool yourself. Be skeptical of your understanding. The Dunning-Kruger effect is a bitch. To avoid fooling yourself, ask lots of questions even with seemingly obvious answers because they’re often not so obvious.

Implications.

The Feynman technique is where you write down the concept you want to understand, and below explain the idea as if you had to teach it. When you get stuck, go back to your book to find the answer. This dispels an illusion of explanatory depth because it’s easy to think you understand something you don’t. There are a few possible applications of this. Number one, for things you don’t understand at all. Go back and forth between your explanation than the one in the book. Number two, for problems you can’t seem to solve. Go through the problem step by step along with the explanation and generate rather than just summarizing it. Number three, for expanding your intuition. Apply this to ideas that are so important that it would be good if you had a good intuition. Don’t focus on explaining every detail but generate illustrative examples, analogies or visualizations.

When people hear about geniuses there’s a tendency to focus on gifts rather than efforts.

Chapter 12, Principle 9, Experimentation

Vincent van Gogh started painting very late at 26. Most people start early. He also wasn’t good at drawing. Everyone also hated him because of his manic enthusiasm. He failed at everything in life. Since he didn’t go to school for art, he self-educated himself. He devoured books on painting exercises. He also studied from other artists. Since he struggled so much, he experimented with tons of styles which eventually led to his own. The second important note is his intensity as he was tenacious in his efforts.

When starting to learn it’s often sufficient to follow someone who’s further along than you. However, it’s no longer enough after some time. Abilities are likely to stagnate after mastering basics because after the first stage learning is no longer about accumulation.

There are three types of experimenting. Number one, experimenting with learning resources. This is where experiment with methods or resources you use. Try picking a resource and applying it rigorously for some period of time. Then evaluate how well it’s working. Number two, experimenting with technique. This is the question of what should I learn next. Pick some subtopic you’re trying to cultivate and evaluate your progress. Number three, experimenting with style. There’s no correct way of doing this but you need to be aware that different styles exist and what they are.

There are parallels between the mindset for experimentation and the growth mindset. In a fixed mindset people believe their traits are fixed or innate and there’s no point trying to improve them. In a growth mindset people see their capacity for learning as something that can be improved. Similarly, experimenting is based on the belief that improvements are possible in how you approach work.

So how do you experiment? Tactic 1. Copy then create. Copying simplifies the problem somewhat because you have a starting point to make decisions. You also build an understanding of why things you’re copying work. Tactic 2. Compare methods side by side. Apply something like the scientific method. Try to apply two approaches side by side and compare them. Tactic 3. Introduce new constraints. Make old habits impossible by adding constraints. Tactic 4. Find your superpower in the hybrid of unrelated skills. Figure out something, even if it’s niche that you’re really good at and hone in on. Tactic 5. Explore the extremes. Learning is experimenting in two ways. First as a kind of trial and error. Second as the process of trying out your learning methods.

Chapter 13, your first ultralaning project

How do you start your project? They’re not easy, they require planning time and effort.

Step 1, do your research. What topic do you want to learn? What resources are you going to use? This can be books, videos, classes, tutorials, guides or even people. This is where you decide where you’ll start. What benchmark will you use for how others have successfully learned? Identify what other people who have learned this skill have done to done it. You don’t need to follow them but it can be a good benchmark. What practice activities will you do? You need to do some direct practice, as we discussed before. Direct practice is impossible. Find opportunities for practice that mimic the skill. What backup materials do you have? You want to look at possible drills or backup materials you may want to use, if the ones you use might be overwhelming at first.

Step two, schedule your time. Prioritize your project by setting it down on your calendar ahead of other things. You want to also decide when you’re going to learn. whether in the morning and afternoon on Sunday or every day. Optimize flexibility in your schedule. You also need to decide the length of time. Shorter commitments are easier to stick with, especially with interruptions in your life.

Step 3. Execute your plan. Here are some questions to ask yourself to see if you’re slipping from the ideal. Meta-learning. Have I done research into the best way to do this? Focused. Am I focused when I spend time learning or am I distracted? Directness. Am I learning the skill in the way I’ll eventually be using it? Drill. Am I spending time focusing on the weakest points? What’s the rate-limiting step? Retrieval. Am I mostly reading and reviewing, or am I solving problems and recalling things without looking at my notes? Feedback. Am I getting honest feedback on my performance early on, or am I dodging the punches? Retention. Do I have a plan to remember what I’m learning long term? Intuition. Do I deeply understand what I’m learning, or am I just memorizing? Could I teach the ideas to someone else? Experimentation. Am I getting stuck? Do I need to branch out? Together, these principles are directions but not destinations. Look at how you’re progressing and see if you need to adjust.

Step four, review your results. After you’re done or if you put it on pause, analyze what went right, what went wrong, and what you could do next to avoid making the same mistakes. You won’t always be successful, but even successful projects are worth analyzing to tell you they can tell you more than your failures because the reasons a project succeeded are the elements you want to retain.

Step five, choose to maintain or master what you learned. You have the skill now. What do you do with it? If you don’t have a plan, it will eventually decay. The first option is to try to maintain this skill through practice or try to integrate it into your life. The other option is relearning. Forgetting isn’t ideal but sometimes the cost of relearning are smaller than the cost of keeping it sharp. Re-learning tends to be easier than learning the first time. The third option is to keep diving deeper. Think of it as endless learning you want to master.

We established ultriling as a strategy, but there are some alternatives you can do that are more low intensity. Alternative one, low intensity habits. This leaves your frustration level low and learning is automatically rewarding. You don’t have a fancy project or effort. Alternative 2, formal structured education. College is still a great way to learn. Ultra learning isn’t a rejection of those opportunities. But finally most of all the goal of ultra learning is to expand the opportunities available to you, to create new avenues for learning and push yourself rather than timidly waiting by the sidelines.

Chapter 14, An Unconventional Education

Before researching for the book, all the ultra-learners the author met were ambitious self-starters. He initially was skeptical that ultra-learning could have implications for the educational system.

Psychologists recognize a large difference between goals people pursue intrinsically and those they pursue extrinsically. The latter type are the cause of misery.

The Polgar sisters, for example, didn’t have tiger parents. Instead, their parents encouraged play and positive feedback, not authority and punishment.

To raise an ultra-learner, start early. You should start the child’s education no later than three, with specialization no later than six.

Then specialize.

Afterward, make practice into play. Laszlo Polgar insisted that play is not the opposite of work.

Fourth, create positive reinforcement to make the activity pleasant rather than frustrating.

Finally, don’t coerce learning. Self-discipline, motivation and commitment must come from the children.

Here are some suggestions to foster ultra-learning at your home.

One, create an inspiring goal. Inspiration is an essential starting point. You need to have something compelling for the person to summon up the energy and self-discipline needed.

Number two, be careful with competition. You don’t need to feel as if you’re good at something to invest energy into learning.

Number three, make learning a priority. Outside school, learning is seen as a byproduct of doing a job, not the goal. Ultra-learning provides the opportunity to accomplish real objectives but designed to teach something new.