
_Post inspired by this morning's journal_

When you are deep into a workout, whether it is your final half mile of a run, last repetition of your squat, or last minute of your AMRAP, what pushes you to finish? What is it that you dig into to keep you going?

Late at night when everyone else is asleep, what keeps you working on your side project? You spend all day working a 9-5 and come home to work _more_. What pushes you to do that?

Lately I've been struggling with both these scenarios. The last round in the gym and I don't dig deep, I stay comfortable. The opportunity to work on [**Harvy**](https://kevinguebert.com/2018/07/11/www.harvy.app) but instead I stay comfortable, I go to bed early. In these times, I lose my "why." Why do I want to go to the gym? Why am I working out? Why do I want to build Harvy? Why am I working more?
![](../images/5f05c4109352a54b1903d173_photo-1484069560501-87d72b0c3669.jpeg)
Photo by [**Emily Morter**](https://unsplash.com/@emilymorter?utm_source=ghost&utm_medium=referral&utm_campaign=api-credit) / [**Unsplash**](https://unsplash.com/?utm_source=ghost&utm_medium=referral&utm_campaign=api-credit)

Finding your own "why" is tough - I am constantly searching, refining, rebuilding, and redefining what mine is seemingly week by week. But having your "why" is important - it is what keeps you going when you don't want to push anymore. It wakes you up early in the morning, it keeps you up late at night, it pushes you to hit that last rep, it helps you stay focused and disciplined.

The "why" gives you purpose, a goal, a reason to do it incessently.

The question though is _how do you find your why?_ I don't have an answer to that. I don't fully know mine so giving an all-in solution would be frivilous. It takes knowing yourself, knowing your aspirations (deep aspirations, not surface level), and knowing the kind of person you want to be. For some it may be as simple as being a better person that you were yesterday or a year ago - that definitely applies to me.

A good starting point would be to write about it. It doesn't have to be every day or every morning, but take some time this weekend, make a good cup of coffee or tea, sit on a porch outside and start writing. Don't have a time limit either, write until you feel like you've gotten everything out on the page. You may not have a full-blown "why", but I would bet that you have a place to start.


Find Your Why


> Mnemonic: a device such as a pattern of letters, ideas, or associations that assists in remembering something.

While following my Python for Everybody course, I learned about _mnemonic variable names_, what that means, and how it can enhance programming. If you have been programming for a while now, you were probably taught this at one point in your history. For me, it was instilled in me in my first co-op job. Mnemonic variable naming is simple creating variable names that are indicative of the data they are holding. For example, x = 40 is pretty basic, but if you change it to hours_per_week = 40, you can clearly understand that the value 4 relates to how many hours one may work in a week. If you are a new programmer and starting off, I would recommend trying to use mnemonic variable names as much as possible. It will help with debugging, sharing code, and help deliver better quality code in the end!


Coding Mnemonically


My list of "things I need to learn" in the world of programming is growing every day. Most recently, while working on a side project, I have fallen into the hole of poor application architecture and design. Why did this happen? Well, part of the reason why is because I oftentimes get too excited about starting to code that I don't take a step back to figure out what the actual underlying pieces are. Then, as I start going down one path I realize that maybe it isn't the best path and have to start over. Software architecture is on the list! (Included in architecture is also design patterns. I think of them as being hand-in-hand). Next up on the list is creating applications that are object-oriented. Most of my work is done for the Web and the web oftentimes doesn't have much object-oriented parts to it. But, when developing larger backend applications, object-oriented principles can make life a whole lot easier. Reduce code duplication, leaner and faster, and easier to update! Lastly, implementing test driven development. This goes a little with object-oriented programming, but how nice would it be if I knew my code worked before it was tested? If I was able to run tests to make sure everything operated as I expected? That sounds ideal.

My action items:

- Find courses in each of those 3 areas. If they are good courses that cost a little bit amount of money, invest in them!
- Have patience when building applications - starting off with a good design will help in the long run!

Time to learn more!

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Things I Wish I Knew (Coding Edition)


### Fac, si facis: "Do it, if you're going to do it"

What is the difference between working for a company and owning a company with employees? What is the difference between a snowboarding going to the Olympics and an amateur riding in the terrain park?

**Work**

When looking at the two people - the professionals vs amateurs - the difference between the two is the amount of work put into the craft. Sitting down and saying "I'm going to be a professional athlete" is **not enough.** Thinking and accepting this idea that you may have is not enough to get you to that level. You **must work.**

Along the way though, our ego is going to get in the way. Our ego wants our ideas, wants us to aspire to something, it wants that attention and glory of saying you are going to do something (echoing a previous post, The Dope Addiction). But in saying something, we are not progressing - there is no work being done.

"When you are not practicing, someone out there is, and when you meet him, he will win"

Bill Bradley

Practice. Work. Improve. Every time you sit down to work on that side project, to study, to prepare for an exam, to do your homework, remind yourself that the work you are doing is an investment in yourself. Do the work.


Fac, si facis


"To-do lists are great if you use them correctly, but when people say things like 'I sometimes write down easy items I can cross off right away, because it makes me feel good,' that's exactly the _wrong_ way to create a to-do list" - Timothy Pychcyl (129)

We've all heard of SMART Goals - **S**pecific **M**easureable **A**chievable **R**ealistic and **T**imeline. I think I recall first hearing about them when I was in school. I don't recall what age, but it may have been in middle or high school. In talking about SMART goals, we lay out a plan for a goal we want to achieve and break it down into those 5 pieces - Specific, Measurable, Achievable, Realistic, and Timeline. With these 5 parts created, we then have a plan laid out in front of us that helps us tackle any goal. Charles Duhigg in _Smarter Faster Better_ argues that SMART goals are not enough. With SMART goals, one can get in this mindset of only focusing on the short term achievable goals. Like the quote above, if someone is just creating SMART goals and wanting to check off the items in the list, that person then loses sight of other aspects that can be improved. Setting a smart goal to improve sales by 20% this quarter is a good goal, but if in doing that goal one loses sight of a bigger picture, maybe building a network that will last many years to continue selling to, then the long term progress of SMART goals is missed.

Duhigg then crafts a better way to create goals by combining two different systems - creating a Stretch goal and then combining that with SMART goals. He wants you to think big, to dream big - "if you do know how to get there - it's not a stretch target" (125). Stretch goals should be goals that seem _almost_ impossible. If you try and lay out the steps right now, you wouldn't quite know the path to get there. But, at the same time, they also need to be a little in reach too. There is a fine line between a Stretch goal and an impossible goal. When a Stretch goal is created, then you can start crafting the SMART goals that would go along with it. You can start creating "proximal goals" - smaller ambitions and tasks that help one build up to succeeding. So if your stretch goal is to run a marathon, a very good proximal goal would be to 5 miles without stopping. Then when that is completed you run 10 miles without stopping, so on and so forth. These proximal goals build upon each other to help reach the Stretch goal.

What does this all mean? Next time you do a yearly review with your boss or you are trying to accomplish something, set a Stretch goal first. Something that seems almost impossible. Then break that down into smaller SMART goals that will build up and help you achieve that Stretch. You've got to Stretch and be SMART about it.

Post inspired from: **Smarter Faster Better. The Secrets of Being Productive in Life and Business** by Charles Duhigg.


How to Stretch and Be SMART


**_Job Perks:_**

- **_Unlimited Vacation Days_**

Is that not something all of us want to see when applying for jobs? _Unlimited Vacation Days_ - it sure does sound perfect. But what if I told you that oftentimes that in the companies that implement the unlimited vacation day policy, employees actually take **less** vacation time. Sounds impossible right? How could that be?

Blame it on Game Theory.

Now I can't say that all people are like this, many people I know are so I think it applies well, but when we are working at a job what is one of the main goals? Besides delivering good work and pleasing your boss, _most people_ I know have a larger goal in mind: getting a promotion and moving up. We all want to move up, right? More pay, more benefits, more responsibility.

As we, individually, are trying to move up in our positions and work towards that next promotion, well, so are your coworkers. Your coworkers also want that promotion, they also want more responsibility.

Well that's where the problem with Unlimited Vacation Days comes into view. When you have unlimited vacation days, what do you want to do? Well, if you are gunning for that promotion, you are trying to seem "better" than your coworkers. You want your boss to know you are working harder, spending more time, contributing more, and encompassing yourself more with work _more than your coworkers._ Not a lot more, just a little more to have that edge. Say your coworker takes a 14 day vacation - well, to prove you are "harder working", you may one up it a little and just take a 12 day vacation. "I'm working harder and giving more to the company" you may think. You have that edge.

...But then flip it for your coworkers. Your coworker also wants that edge - so when they see you taking a 12 day vacation, well they just might only take a 10 day vacation. And then you see your coworker taking a 10 day vacation and think "shoot, they are doing better than me, I need to work harder", so you take less vacation. When you take less vacation that means that your coworkers take less vacation...

In the end, there is the problem. No one is taking enough vacation. Since everyone is vying to get that promotion, to get better, to "climb the ladder", the Unlimited Vacation Days are not being used. You don't want to come off as a slacker or not putting in your time, and, in doing so, everyone ends up taking less time off.

What's the solution? I don't have it - maybe a required minimum to start. Otherwise, those 2-week policies aren't looking too bad anymore.

Post inspired from: **Algorithms to Live By** by Brian Christian and Tom Griffiths.


The Myth of Unlimited Vacation Days


**Explore/Exploit:** In the explore phase, we _gather_ all the information, and the exploit phase is _using_ that information you have to make the best decision.

### Decisions

If we look at the Explore/Exploit algorithm, we can see that an old phrase is actually has some truth behind it:

> The grass is always greener on the other side.

We have all heard that saying before, but, did you know that there is some mathematically proof that supports it? If you do not have much data, then the unknown has a higher chance of success, even if we expect it to be no different, or "if it's just as likely to be worse" (41). Brian Christian and Tom Griffiths state it as the "untested rookie is worth more than the veteran of seemingly equal ability, precisely because we know less about him." Therein lies the kicker though - once we have enough data, then we make a decision based with the provided data that could possibly change the scenario around. So next draft you see - whether it is football, baseball, basketball, or any sport - remember that the rookies getting the huge contracts are getting the huge contracts because of the chance that they are the best (a higher chance of success).

### Regret

> "To try and fail is at least to learn; to fail to try is to suffer the inestimable loss of what might have been" - Chester Barnard

Another way to look at Explore/Exploit is to frame it in the light of trying to live "**a life with minimal regret**" (43). The authors share a story about how Jeff Bezos, founder of Amazon.com, took the risk of moving from his well paying job to the online bookstore. They quote him as saying it was an easy decision. Why? Well Bezos used the idea of living a life with minimal regret and did an exercise with it: he imagined himself at 80 years old and then thought back on his life and knew he wanted to have minimal regrets. In this exercise, he knew that not taking the risk would lead to regret, thus, he took the risk.

Researchers have then spent years looking to find a way to guarantee minimal regret - the most famous algorithm is called the **Upper Confidence Bound.** This algorithm makes a decision not based on what has performed best in the past, but what _could_ perform the best in the future. To explain it, the authors share that "if you have never been to a restaurant before, then for all you know it could be great. Even if you have gone there once or twice, and tried a couple of dishes, you might not have enough information to rule out the possibility that it could yet prove better" (44).

The Upper Confidence Bound algorithm has a sense of optimism about it - the optimism that what you are trying could be the best. "By focusing on the best that an option could be, these algorithms give a boost to possibilities we know less about" (45). What does that mean for you and me? If we think about the world in the terms of these algorithms, then "you should be excited to meet new people and try new things - to assume the best about them, in the absence of evidence to the contrary" (45) because the chances of finding the best that way are better than not even trying at all.

Post inspired from: **Algorithms to Live By** by Brian Christian and Tom Griffiths.


Explore/Exploit Algorithm in Decisions & Regret


**Look-Then-Leap Rule:** "You set a predetermined amount of time for 'looking' - that is, exploring your options, gathering data - in which you categorically don't choose anyone, no matter how impressive. After that point, you enter the 'leap' phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase." (13)

## Explanation

Scenario: You are interviewing for a new secretary and want to pick the best applicant. Once you have passed on a candidate, you cannot go back and pick them. You are interviewing at random.

#### 1 Applicants

Hire them!

#### 2 Applicants

For 2 applicants, you have a 50/50 shot at hiring the best one. Either pick the first one, or, if you don't, you have to pick the second one.

#### 3 Applicants

Let's say you have 3 applicants, ranked as the best being 1, second best being 2, and worst being 3. With 3 applicants, there are 6 possible scenarios in which you can interview:

1-2-3 (that is: best, second best, worst)

1-3-2

2-1-3

2-3-1

3-1-2

3-2-1

With the 3 applicants, _we can actually do just as well as we did with just 2 applicants - a 50/50 shot._

How?

When we see the first applicant, we don't have any baseline information so we pass up on them to gather more data (the "look") phase. When we see the final applicant, we only have one person so we have to pick them.

With the second option, there is more room. With the "Look-Then-Leap" Rule - **if the second option is better than the first one, then you leap. If it is not, then you move to the next one.**

Let's visualize this - remember, you are on the second candidate because the first candidate was your "look" - or information gathering:

**1 - 2 - 3**

In this scenario, we have already passed up on the best candidate (1), so this would be a Failure.

**1 - 3 - 2**

Similar to the one above, we have already passed up on the best candidate (1), so also a Failure.

**2 - 1 - 3**

In this scenario, the second candidate is better than the first one (1 is better than 2), so you would leap on this candidate - Success!

**2 - 3 - 1**

The second candidate is _not_ better than the first one (3 is worse than 2), so you would then move on to the 3rd candidate. The 3rd candidate is better than the first one, so you would then leap - Success!

**3 - 1 - 2**

In this scenario, the second candidate is better than the first one (1 is better than 3), so you would leap on this candidate - Success!

**3 - 2 - 1**

In this last scenario, the second candidate is better than the first candidate (2 is better than 3), so that means we would leap by the rules. Unfortunately that means we miss out on the last candidate, the 1 - Failure.

Looking at the data above we have 3 successes and 3 failures - thus a 50/50 chance when we have 3 candidates - tada!

Successes: [2,1,3], [2,3,1], [3,1,2]

Failures: [1,2,3], [1,3,2], [3,2,1]

To continue on an enumerate more candidates - 4, 5, 6, etc. - the place where you end up drawing the line between looking and leaping is actually 37% (the 37% Rule).

#### 37% Rule

When choosing the best candidate, you only have a 37% chance of hiring the best possible applicant.

**How it works:** You look at the first 37% of the applicants, not choosing any of them, then after that, leap for anyone that is better than all those you have seen so far.

Next time you are making a decision over a large amount of data and you are trying to decide the "best", it may be worth it to consider this algorithm!

--

Post inspired from: **Algorithms to Live By** by Brian Christian and Tom Griffiths.

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productivity