What I did:
I completed The Web Developer Bootcamp, “the most comprehensive” online course on web development. I did this as a part of my self directed software engineering course for my Graduate Unschool project. I added this course (and a few others I will not be writing about at length: Angular 4, React from the Ground Up, The Full Stack Web Developer) because I realized it’s important to learn relevant technologies when pursuing a career in software. Web development is also a practical skill that I can combine with what I learn in the MIT courses or personal interests to build projects to show and test my knowledge.
This course has had a serious impact on my life. Since finishing it I have: started working as a full stack web developer and developed and launched my own website.
I strongly recommend this course to anyone, with any background, who wants to learn to develop web applications.
What I learned:
I made a custom project based to showcase everything I learned in this course: FindSkateparks.com
Users can easily search the database of skateparks, find the closest or top rated parks, or add their own skatepark to the website.
The website uses geolocation to find the user’s location and show the parks closest to them.
The most useful information is at the top: location, hours, and required equipment. This is so users can easily find how and when to visit a skatepark without having to search through inconsistently structured pages. Users can also add comments and ratings, which will change the park’s overall rating over time.
Simply put, I learned everything you need to know to build a website or web-based application from scratch.
This includes experience and understanding using the following technologies:
- DOM Manipulation
- Database Associations
Here are some of the many projects I built as a part of the course:
The capstone project for the course. I based my skatepark site off of this.
A simple web application for making editable lists.
- Patatap Clone:
A visual beat pad. This is actually pretty fun to use. Click the link and use your keyboard to make beats and interesting patterns. This was the first project that made me feel like I could build something cool and useful.
How I did it:
Although I typically approach my Software Engineering Graduate Unschool courses using a modified deliberate practice method, this course had a different format, so I took an approach that was more in line with the natural layout of the course, and added a section to experiment with a new learning strategy. The following sections were completed sequentially.
- Coverage & Practice (50 %)
- The course is structured as a series of video lectures, most of which were code-alongs, and exercises. I have always been critical of the wastefulness of passive learning, and the code-alongs take the passiveness of a lecture and turn it into an engaging process where you are guided though building something that demonstrates an underlying idea, helps you gain experience with a certain concept, or will be used as a useful reference when you’re developing something outside of the course. A code-along is where the instructor has you solve a problem together while he guides you through the steps he takes and the reasons for his approach. You’ve probably experienced this lecture style in science classes in the past. It’s important to note that when taking this approach the problems the instructor chooses to solve ultimately make or break the course. Ideally an instructor would carefully select each problem to demonstrate the core concepts and skills in the course and order them in a sequence such that every problem is challenging enough to be interesting and engaging (so guidance is welcomed), but not so challenging that it is overwhelming and disorienting (so students shut down). Colt Steele, the instructor of this course, choose the problems perfectly. Each code-along builds on the last, it’s always engaging, and I walked away every single day feeling as though I was a better developer than the day before. This was the best lecture/coverage section I’ve ever seen in an online course, and I think this course can be used an example for how anything should be taught.
- Creative Project (45%)
- This section was an experiment for me, and was not part of the course as it was presented. Typically I’d end the bulk of the skill development section after completing difficult practice exercises and be left with the synthesis section to demonstrate my knowledge (in a way that I have been dissatisfied with so far). However, I realized that we all have personal reasons for learning something, so I took everything I learned in this course and spent almost as much time personalizing the skills I had developed to create something I was interested in. I ended up learning as much in this phase as I did in the previous phase, building on top of what I’d already covered in the course. My creative project for this course is: findskateparks.com
- Creating a personal project really helped me connect with the usefulness of the skills I developed in the course, and forced me to practice them in a realistic way, as opposed to the rote way they are typically practiced though textbook problems or assigned exercises. Of the courses I’ve completed so far, I can most easily apply the skills I’ve learned from this one. I believe that is a direct result of investing my time building something I care about using the course knowledge. I will be adding creative projects as a potentially crucial part of the learning method.
- Synthesis (5%)
- I procrastinated for months then painfully wrote this article outlining what I did and what I learned in the course.
What I learned about learning:
- Web development is an awesome connector skill that can be combined with any other skill to further one’s pursuit of both. Examples: my creative project (skateboarding), this blog (learning and writing), your favorite website, your favorite web application.
- Directed problem solving, where the students are physically solving problems along with the instructor, is an excellent way to turn the passive and mostly useless lecture phases of a course into a more active, engaging, useful phase. Especially so when the problems are selected in a way such that they are challenging, but not intimidating, for students, and demonstrate the core concepts of the course in a way in which they’ll be realistically used.
- Creative projects turn new skills into long term skills by forcing you apply them in a realistic way.
- We want to learn new skills so we can do something with them. Every directed learning resource you encounter will fall short of the specific reason you wanted to learn your new skill. Reconnect with your “why” and use the skills acquired in the course in a more direct way to reach your personal goals before moving on.
What I did:
I successfully completed all the readings, lectures, homework assignments, and projects for MIT’s Introduction to Algorithms (the fall 2011 version of 6.006) course. I’ve included examples and brief explanations of my work below to give an idea of the types of things I’ve learned. All of my programming work and my final solutions to the homework assignments can be found on my GitHub.
I’ve included some examples of my work below:
Code I wrote for content aware resizing of images. The basic idea here is you can resize images without cropping or losing content. The code shows my bottom-up dynamic programing implementation of deciding which seam (vertical path from the bottom to the top, example shown in red), would be the least noticeable if removed.
image being resized via my algorithm
This program uses data from the National Highway Planning Network to find the shortest path from a source to a destination in the form of driving directions. This is similar to how Google Maps and other navigation software products work. Here is the code I wrote to implement Dijkstra’s algorithm, which performs the search and returns the results very quickly, as one must sort though a large amount of data to find the shortest path for a cross country trip.
Here I implemented interval subsequence hashing using a modified python dictionary I wrote to quickly compare DNA sequences (represented as series of letters). Included are my results, comparing the DNA sequences of two humans, a human and a chimp, and a human and a dog.
I dramatically improved the speed of image decryption software written for a theoretical extreme multi-core chip that is limited to only performing arithmetic operations on 8-bit or 16-bit unsigned integers. I’ve shown my straight forward arithmetic operations which perform better than that prewritten asymptotically efficient algorithms when inputs are 64 digits or less. I’ve also included an example of the type of extremely sensitive data that can be decrypted using this technology.
This is my implementation of a bidirectional search which provides the fastest (smallest number of moves) solution to an arbitrarily arranged 2×2 Rubik’s Cube (pocket cube) in <5 seconds.
This is a selected example of the written portion of a problem set, and my (neatly typed up) solutions. Although it is much easier to show and understand the value of some of the software projects I worked on in this course, it’s important to demonstrate that 6.006 is a very theory heavy course, and foundational understanding is emphasized much more than practical programming skills. I enjoy theory and figuring out creative proofs to (seemingly) difficult problems was one of my favorite parts of this course.
Here is a theory heavy problem that I found to be particularly difficult. The problem assumes that you are a newly hired employee at a competitor of Facebook and asks for an algorithm for suggesting potential friends. More specifically it requests that the algorithm find the strength (computed as the product of all edge-ranks, within users that are less than k degrees of separation apart), between a given user s and every other user v to whom s is connected, in O(kE+V) time (this is a graph theory problem). This solution required the creativity to recognize that maximizing a sum (the strength) of a product (the edge ranks) is equivalent to minimizing the sum over the log of 1/(the product). This converts the problem to be a shortest path problem for all paths shorter than k in length.
What I just described is technical, but the reason I chose this problem is to demonstrate that this MIT course teaches students to think creatively and challenges students to think outside of the box. A cookie cutter application of an algorithm from our algorithms textbook doesn’t suffice. The course teaches more about how to reframe real world problems so we can map them to previously solved problems and apply the solutions, which I believe is the most valuable skill one can learn in almost any domain.
This is another example of a proof that I found to be particularly valuable, but also built on the most important skill of this course, reframing problems and modifying solutions so you can use resources of previously solved problems (in this field these are called algorithms) and apply them to novel problems. In this particular problem I am writing pseudocode to develop more basic arithmetic operations for the theoretical chip from the image decryption problem.
How I did it:
Before I analyze what I did, I’ll give a raw explanation of what I actually did. I completed this MIT Course in my free time by using an evolving version of the learning method I developed and found success with in college. The method breaks learning a college course into three phases, which are listed below, along with the actions I took to complete those phases.
- Coverage (5%)
- I listened to all the lectures at 3x speed, speed read the assigned reading (chapters of the textbook and additional reading assignments) for the course (in <5 hours) (here is how I did it). The purpose of this section is to see the forest before I start investigating individual trees. The entire course material is covered up front for multiple reasons. First, because textbooks are used primarily as (incredibly valuable) reference tools, and it is much easier to find what you’re searching for if you’ve seen it before, even if you didn’t realize what you were initially looking at. Covering the course in full without any deep dives emphasizes the scope of the course more than the details. When learning anything it’s important to understand what’s actually important so it can receive the appropriate attention. If I stopped when I struggled to understand each algorithm (the majority of the readings and lectures), I may have wasted hours memorizing them, but after completing my coverage phase and standing back from the course I realized that the purpose was not to teach us specific algorithms, but to teach us how to reframe problems so that we can use algorithms that others before us have developed. This clarity helps me focus on the important details of the course instead of all of them, ensuring that my time is spent more effectively. Also, reading and listening are forms of passive learning, which can only take you so far. My experiences have convinced me that real skill building, deep understanding, and internalization comes from active learning (phases 2 and 3). Since I find passive learning to be somewhat shallow, but still helpful in the initial phases, I feel that it is important to give it a sense of closure early so I don’t waste extra hours rereading paragraphs and re-listening to lectures because I don’t feel I fully “understand” them. The truth is I can only gain a false sense of understanding, (which is more dangerous than knowing you don’t know) from anything other than the actual application of the knowledge. Instead I quickly move through this section, capping the time, with the expectation that I will understand almost nothing, but that the exercise will give me a sense of what’s out there, what I haven’t learned yet, and what I can expect to gain a deeper understanding of through my active, intensive, next phase.
- Practice (80%)
- I completed every problem set and project sequentially using my adaptive set method. I wrote down every problem set number on a different line of a piece of paper (my mega adaptive set), and beside each problem set number I wrote down the number of each problem on the associated problem set.
my mega adaptive set
I started on the first problem set, attempting each problem and subproblem individually and discretely to the best of my ability, and providing a falsifiable answer, which I then immediately compared to the official course solutions. If my solution was correct I crossed the problem number off my mega adaptive set, and neatly wrote up my solution to be used as an “official solution”. If my solution was incorrect I circled the problem on my mega adaptive set, compared my attempted solution with the official solution line-by-line, identifying where exactly I went wrong, correcting my mistakes, and completing my incorrect solution using the official solution as a guide. Then I threw away my corrected, attempted solution, and moved on to the next problem on my set. Once I reach the end of the problem set (I call each of these a run-through) I circle the problem set number if there are circled problems remaining on the problem set. I then attempt another run-though of the problem set, where I repeat the process described above, but only for the circled problems. It is important to note that I do not attempt a circled problem within 24 hours of circling (unsuccessfully attempting) it. This is to ensure that solutions are not memorized and that I’ve actually learned the important concepts and tools necessary for solving the problem. Once all the problems have been crossed off a certain problem set, I cross of the problem set number and move on to the next one. Since this is an algorithms course, I thought it would be fun to rewrite my practice set method in algorithm form.
- Synthesis (15%)
- I identified (in this case singular) the most important core principle underlying the content of the course. This principle is as follows; To solve a non-trivial problem, reframe it such that it resembles a previously solved or trivial problem (although this reframing is typically non-trivial), and apply the known solution. I believe that statement, an algorithm resource (the course textbook or the internet), and practice solving problems using the stated method forms a basis for this course.
- I went through my coursework and selected the solutions and projects I felt were most representative of what I accomplished and what I learned throughout the course. I paired these solutions with brief descriptions, so readers of all technical backgrounds can very quickly gain an insight into what I actually did. I believe it is important to show your work in a way such that it is beneficial to you. An A on a transcript can only communicate one point of information. It is important to take ownership of your work so that it takes it from something you did for a course, to something you made and can be proud of. This creates a virtuous cycle, because knowing you want something to display, whether as a selected project on your resumé, an item in your portfolio, or as a part of an article on your learning blog ;), motivates you to make something special, something you can be proud of. This nicely presented work helps you feel better about your work, and the experience with your learning project, which motivates you to do more, build more, and make more presentable and interesting projects in the future. I am in the process of learning this first hand, despite preaching about the importance of showing your work so that you can take credit and benefit from it for the past few years. The selected projects on this page are more visual and understandable than on my last course write-up, which was a major step up from me completely skipping this phase on my last two MIT courses. During a recent job search I had official offers for Software Development positions extended to me because of my last synthesis article, and how it showed my knowledge of the field, my learning methods, and my ambition. I did not expect my last article to help me get a job. If I did, I would have sent it to potential employers. Instead I just left a link to my blog on my resumé because I believe it is an important part of my personality, and hiring managers later told me how my Graduate Unschool articles strongly influenced their decisions to extend offers to me. I’m not writing these to get a special job, or prove anything to anyone else, I’m writing these synthesis articles to complete my learning process. Everything else is a free bonus for showing my work in public.
- The purpose of the synthesis phase is to teach the material of the course in a way that is helpful and more concise than the original course material. This is how knowledge progresses, somebody learns something, then represents it publicly in a way that is easier to understand. Imagine how long it would take to complete a simple high school math test if you had to derive everything from fundamental axioms. Imagine if every time you wanted to use a computer you had to invent it from scratch without any sort of guide. When Newton talks about standing on the shoulders of giants this is what he means. Our ancestors built the intellectual world around us though absorbing, mastering, and representing knowledge in a form that is easier/faster to learn. Your duty is to do the same. You don’t need to discover a useful math theorem or invent a piece of technology to be a part of this process, all you need to do is learn something, synthesize it, and make your synthesis publicly available. Read a book and share what you learned from it, write a how to article on something you can do whether it’s simple or complicated, help those that you know and those that you will never meet learn what you know faster than you did. That’s the real secret to accelerated learning, we can all accelerate each other’s learning.
What I learned about learning:
- If you have two goals, it is simpler and faster to complete them sequentially and discretely, rather than simultaneously
- This learning method applies to the application of this learning method.
- Right now I recognize that the synthesis phase is a major weakness for me, but re-covering my old synthesis section notes, practicing writing the last two articles, and showing others how to write a synthesis article is helping me evolve the synthesis phase, as well as improving my skills at it.
- It makes sense to pause one goal for the completion of another, if you think it improves the sequencing.
- I paused this course twice, once for 6.0042j (which I was closer to finishing) and once for the web developer bootcamp (whose completion was more urgent).
- Start with a naive solution, then iterate towards improvement/Ignore the temptation of the ideal solution
- Make your synthesis articles visually appealing
- You can feel more proud of your work
- Visually appealing articles are much more easily and quickly communicated. With a visual representation an outsider can gain a much better understanding of what you did in 30 seconds than if you just left a giant block of text with a few complicated math problems sprinkled in.
- One doesn’t count to 1000
- I had to make the transition from counting my hours to making this a major part of my lifestyle. At first I was tallying off every hour I completed towards my 1000 hour goal. I made mini goals to hit so many hours a day, week, month, but I was always behind. Once I decided that this was important to me and dedicated sacred hours to this, I stopped counting completely and realized that I would easily overshoot my goal. If you hope to make significant progress on a big project you need to stop counting away the minutes to completion and make it a part of your life. Take detours here and there, fall in love, but make it a part of your lifestyle, that way you won’t need to count or remember. Instead you use the habits, taught and synthesized from your past self, to propel you current self forward.
After a medium length hiatus the podcast returns! Ryan and I talk about my experiences at improv, struggling for enlightenment, who is The Ryan Doner(?), how we’re finding ourselves, the ultimate way to respond to criticism, acceptance, moving to new cities, the Lindy Effect of friendship, and some mistakes we made and how to make it easier to stop your negative patterns. Enjoy!
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What I did:
As a part of my software engineering course for my Graduate Unschool project, I completed the fall 2005 version of MIT’s 6.042j course, Mathematics for Computer Science. This means I read all of the course materials (textbook/lecture notes/solved problems and examples), successfully solved every problem of every homework assignment (equivalent to a perfect homework score), and synthesized the course into it’s essential elements, shown below. All of my completed solutions and my course synthesis can be found here. I’ve also included a few examples of my solutions so you can see what types of problems I solved, also below. If you want to read more about the learning process, scroll past the examples of my work.
The foundational knowledge and tools I learned/used while taking 6.042j.
Examples of my work
How I did it:
The learning method I’ve paired with the software engineering course is essentially an evolution of the learning method I developed and used in college. It is heavily based off of the core concept of Deliberate (or possibly Directed in my case) Practice, and I am evolving it by trying new techniques I found from Scott H. Young’s learning methods, more literature on Deliberate Practice, and other experimental tweaks. Below is an overview of the phases I broke the course into. These phases were completed in discrete chunks:
- Coverage (5%)
- I speed read all of the textbooks, and course notes (I try to do this in ~5 hours).
- Practice (80%)
- I make each individual problem set into an adaptive set. I then attempt each problem as if it were a normal homework assignment (using the PDF version of the book and in-class examples heavily for references) and make my best attempt at a solution. After completing my solution attempt, I immediately compare it to the official course solutions. If my solution is correct, I cross the problem off my adaptive set and move to the next unsolved problem. If my solution is incorrect, I compare my attempt with the solution, identify exactly where I went wrong, circle the problem, and move forward to the next unsolved problem. When I work my way to the end of the problem set (I call this a run-through), I start again on the problem set, this time going through the circled problems in order (being careful to not attempt the same problem in the same 24 hour period to avoid memorization). I continue to run through the problem set until I have crossed off every problem, then I group my solutions together and move on to the next problem set.
- Synthesis (15%)
- I identify every major concept and problem solving tool I used while completing the problem sets and put them in a list. I prune the list so that it only contains essential information, but I also ensure that nothing essential is missing so that the list spans the course (think of it as a basis for the course). I then write this into a one-page synthesis sheet so that the entire course’s information is organized in one place (this is useful so others can learn the course faster in the future, so you can refresh yourself quickly, and it could be used as a crib sheet).
There’s a lot more that goes into finishing an MIT computer science course in your free time than a just a learning method, and how I finished this course is very different than how I started because I constantly experimented with my approach and integrated what worked into my habits. I started in September 2015 after successfully completing MIT courses 6.01 and 6.02 back to back in two weeks each, with a huge plan for how I would complete all of the MIT courses on my list in just six months. I decided to experiment with taking courses simultaneously because it was what I was familiar with (traditional school), and I thought the spacing and connections would help. I’m glad I tried that because I realized that I was very wrong.
Attempting two courses at once divided my focus, and pushed the finish line further back (it should take twice as long to finish two courses if completed simultaneously) which made it more difficult to make progress because I’d experience decision fatigue when deciding what task I was going to attempt each day, and it made what was once a bite-sized achievable short term goal, completing one course, into an overwhelming project, causing me to procrastinate, fall behind, feel guilty, and become avoidant of my work.
Eventually I stopped working on Graduate Unschool completely. I went from regularly spending 6 hours a day 6 days a week making serious progress to going months without programming, forgetting where I stood in each course, and asking myself if I was ever going to follow through on my original intentions. Sporadically I’d have short intense spurts where I’d stay up all night making a new plan and plugging away at problems, but that energy would wear off, and I’d go back to my intellectual drought of pretending Graduate Unschool either didn’t exist or was a relic of my naive past.
Then one day, somewhere between a financial rock bottom and a personal career renaissance I reconnected myself with the original intentions of the Software Engineering Course. I found myself easily slipping into flow states while programming, loving it again, and wanting to get better and pursue a career in developing software, and although I didn’t have a formal education in computer science, I believed that this would fill in the gaps in my knowledge and signal to employers that I truly did “know my stuff”. I started ramping up my skills again, challenging myself everyday, and I eventually found a job as a software developer where I have been working for around seven months now. Just as I’d hoped, my job had me programming everyday, and consistently learning all sides of software development. My secondary intentions for the software course had been fulfilled, and I hadn’t even made it very far through my personal plan. I realized that it was very likely that I could continue to have a career in software without ever completing another MIT problem set or practice interview question. I also realized that I cared deeply about developing hard skills, I loved programming, and more importantly pursuing excellence and improving my programming skills as I started developing software full time. Through some introspection I found that I truly enjoyed the MIT Computer Science courses, and that completing them was important to me even after I’d successfully completed the career transition I originally thought the courses would help me with. With purified intentions, and a serious break from any disciplined self-learning routine, I made a little bit of progress everyday, built some healthy habits, finished this course, and most importantly gained insights into the learning process. My synthesis sheet clearly shows what I learned about math while taking 6.042j. Here’s what I learned about the learning process itself.
What I learned about learning:
- Attempting two courses or topics at once divides your focus and slows progress and motivation for both. It is faster and better to start one thing, focus on it, finish it, and move forward.
- Setting up a feedback system where you can see your progress everyday is extremely helpful and motivating. Including a mechanism where your progress measures and acknowledges both efforts and results seriously accelerates your learning. You’ll know exactly what to do when starting everyday, where to go, and when something is difficult and takes multiple attempts before you make any tangible results progress, you’re rewarding the important part of learning a difficult concept, the unseen internal progress that can only be made from serious attempts (both successful and unsuccessful).
- Avoid large gaps away from your work. Taking a ~ 1 year gap in the middle of this course seriously set me back. It took tens of hours to figure out exactly where I had left off, what work I had already completed, and what remained. Even after straightening all of that out I had to complete a serious amount of redundant work, either because I’d lost a problem, or because I didn’t recognize a solution until I had already redone it.
- Make frequent progress, however small. My recommendation is to improve your desired skill 1% everyday.
- Increment the intensity of your approach to avoid overwhelming yourself and creating a system you’re likely to quit or burn out in. My recommendation is to start by completing one pomodoro (25 minute chunk of uninterrupted work) each day at the same time (since consistency matters), so it becomes a part of your lifestyle. At first you will feel resistance, but once it becomes a habit (meaning it no longer takes willpower to complete your pomodoro), you can consider adding another pomodoro, and repeating the habit incrementing process. If, after completing your regular pomodoros, you want to continue, feel free to keep working, but it’s important to realize that 25 minutes today and 25 minutes tomorrow will serve you better in the long term than 5 hours today and feeling too exhausted to start tomorrow.
- Stream of consciousness work journaling helps your progress feel more meaningful, keeps you focused, and gives you a catalogue of your actions for you to analyze and learn from in the future. At the beginning of every session I write the date, every action I’m taking as I take it, and often what’s going through my head and what I’m feeling as I work. I keep my journaling short, personal, honest, and quick (~20 words per hour, I spend about a minute an hour total on the journal, so it’s not distracting. Instead I note my distractions in the journal so that I can move forward with my work.)
Here’s an example of my stream of consciousness journal.
- Document your work and keep it well organized.
- Close your loops. I’ve already recognized the importance of keeping the phases of each course discreet, and in the process of completing this course I’ve realized it’s important to keep the courses discrete as well. This means finish one task before moving on to the next one. The only reason I’m typing out this synthesis (and not avoiding it like I did for the first two software engineering courses), is because I forced myself to complete all phases of 6.042j before continuing on 6.006. Once you’re finished with one task it clears up space in your mind that you can use to fully focus on the next task.
- Give yourself credit for the work that you do. If someone want to visualize what I learned while completing 6.042j I can send them this page. Find a way to demonstrate your skills so you can benefit from your work.
- Build your skills through iteration. You’ll be intimidated when approaching new things. Throw perfectionism out the window, finish something quickly, stop avoiding it, don’t fall behind on any of the pieces, get it out there, get feedback on how you can improve a few specific pieces of it (but again, just upgrade it, make it just a little better than your last one, don’t try to accomplish too much in a single iteration), and make it a little better next time. This is how you get better at anything. It takes patience and humility.
- Break very difficult problems into digestible chunks. Sometimes concepts are so complicated you won’t be able to successfully complete it in your first attempt, and you won’t be able to even completely understand or internalize the solution on your first attempt (meaning you’ll get your second attempt wrong as well). Find your saturation point, find a piece of the solution that you can easily understand today, maybe one concept, maybe one problem solving mechanism, and briefly focus on that while analyzing the solution. Quickly write how it works, then when you reattempt the problem later, start by focusing on what you’ve internalized in this process. Now that you’ve chunked out a piece of the solution, the remainder will be less intimidating. Try the now truncated problem again, and you’ll be surprised how quickly you progress. Learn the problem, solution, and problem solving mechanisms in smaller pieces so you spend less time feeling stuck.
- How to attempt problems you know you are unlikely to succeed at: Give it your best attempt. Don’t take too long (pomodoros come in handy for this). Make sure it’s a complete solution that can be turned in and be graded (you want to know where you’re going wrong, and why your assumptions were wrong). Don’t leave anything out that was asked for. Set yourself up for success, even if it is unlikely, because then you can close the gap between a successful solution and what you did more quickly/easily. It’s easy to fall into one of two unproductive traps: wasting multiple days attempting to figure something out that is too far outside of your current skill level, and giving up whenever you are uncertain and relying on the solutions to guide you. Instead of focusing on solving problems, instead focus on using problems to measure and push the limits of your skills. If you can’t solve it today, see how far you can get today, then focus on solving it tomorrow, and move forward.
I asked the four people I admired the most in my 23rd year, “what were you doing when you were 23?” and “what are you doing now?“. Then I shared the emotional journey of my 23rd year.
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