This is my sixth term at university. For my first three terms I was in Management Engineering, which is a UWaterloo hybrid of information technology, business, and industrial engineering. My fourth term I switched out of Management Engineering into Mathematics, with the intent of concentrating in Computer Science. I quickly realized that Computer Science wasn’t for me, and my fifth term I took a motley array of mathematics courses that ended up loosely focused around Combinatorics and Optimization. I’m now in my sixth term, doing an exchange at HKUST, and still an undeclared Mathematics student, roughly equal parts towards a degree in Computer Science, Combinatorics and Optimization, Pure Mathematics, Computational Mathematics, and Statistics.

UWaterloo likes to streamline their students into specific concentrations very early on in their academic career. Talking to other exchange...

Technical debt, or tech debt, is a term coined by Ward Cunningham to describe “those internal things that you choose not to do now, but which will impede future development if left undone” in software projects (Sundaram). The term derives itself from financial debt, and is similar in many ways. Technical debt, like financial debt, “incurs interest payments, which come in the form of the extra effort that will have to be done in future development because of the quick and dirty design choice” (Ries). Tech debt can be accrued in two ways: intentionally and unintentionally (Chen). Intentional tech debt is often created when companies consciously choose to optimize for the present. For example, product managers may set a deadline that requires faster implementation at the expense of future flexibility. Unintentional tech debt is usually formed as an unavoidable by-product of time passing...

The Innovator’s Solution: Creating and Sustaining Successful Growth (2003) by Clayton M. Christensen and Michael E. Raynor

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NOTE: Bewarned, these notes are un-edited, un-revised, and un-styled. I plan on cleaning them up eventually, but until then, I apologize.

The Growth Imperative

basically doing an overview of the innovator’s dilemma

• companies need to grow
• stock based companies need to grow more than predicted (because their stock price takes into account the potential growth from new business)

theories

• start by categorizing observations

“The Innovator’s Dilemma summarized a theory that explains how, under certain circumstances, the mechanism of profit-maximizing resource allocation causes well-run companies to get killed”

How can we beat our most powerful competitors?

circumstance of innovation

• sustaining circumstance - improving an existing product -...

Ça va is a multiple choice based emotion logging application built on top of Blackbox. It focuses on asking and recording the answer to one simple question: “How are you feeling right now?” Here’s how it works:

The user downloads the Ça va app to their mobile phone. When they open the app they see a simple text header that asks, “How are you feeling right now?” Beneath this header are two buttons: one with a :) icon representing ‘happy’ and one with a :( icon representing ‘unhappy’. Clicking one of the buttons logs a change in emotional state to persistent data storage, and reveals a thank you message and a button to view a visualization of the aggregation of this data. Exiting the app without clicking the :) or :( button logs a :| representing an ‘in the middle’ state. Additionally, Ça va is configured to send notifications to the user three times a day prompting them to open the app...

The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses (2011) by Eric Ries

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These are my notes from reading The Lean Startup. I’ve re-arranged and summarized them where I thought doing so would aid clarity. In particular, I combined Ries’ introduction and epilogue into a single background section which I’ve placed first because I think it provides useful context for understanding the rest of the content. I’ve also included a commentary section at the end, which aggregates the questions and comments I had while reading. Quotes are taken directly from The Lean Startup unless otherwise attributed. Enjoy : )

Background

• The Lean Startup describes a management philosophy
• What are management philosophies?

Frederick Taylor’s The Principles of Scientific Management (1911)

• Frederick Taylor is known as “The Father of...

Part 1: I Promise This Is Going Somewhere

“The unexamined life is not worth living.”
– Socrates

The process of self-discovery is very similar to any process of scientific inquiry. We start by forming a hypothesis about ourself, which we use to make our lives more manageable and efficient. For example, I have a belief that I don’t like country music. I use this belief in my day to day life frequently: for example when I turn on the radio in my car I don’t re-check the country station to verify that I don’t want to listen to it. I assume that my belief still holds and I can safely tune to the indie rock station. But what happens when I start grooving to a country song at a party? My actions no longer agree with my beliefs. This hypocrisy creates dissonance which our brain resolves by either refining our belief or redefining ourself. Perhaps I decide I’ve been classifying country...

Résumébly (or resume résumé ably) is a service for managing the content of your résumé.

When you look at your résumé it may not seem obvious at first but it can be divided into two parts: content and presentation. The content part of a résumé is made up of sections, each which can recursively contain of subsections until eventually you reach the smallest subsection, which is generally represented as a bullet. The mathematical model for such a structure is a tree. Each node in this tree contains some arbitrary data which is used when visually displaying that section, and a list of children representing the subsections of that section. Each sibling in a list of children has a similar data structure, and is usually displayed in a similar way. For example, a simplified version of my résumé could be represented like:

var resume = {
    label: 'Résumé',
    children: [{
        label:

This wasn’t started with much foresight, so it’s kind of messy. It’s not ranked, it doesn’t include definitions, or reasoning. Some of these words probably don’t even belong on this list, and I think there might be a couple repeats. But I figure I might as well put it out there just in case someone finds it useful. And when I’ve got some time I’ll come back and tidy it up.

Words:

• disseminate
• ideation
• syncopation
• sensual
• sybaritic
• exotic
• thing that emmanuelle was all about
• tesselate
• fractal
• incept
• petrichor
• inglenook
• cavalier
• insouciance
• ghost
• dream
• serendipity
• flustered
• apposite - strikingly relevant
• heuristic
• paradigm
• curate
• naive
• queue
• scrobble
• serenity
• esoteric
• icarus (greek myth about boy that flew too close to the sun)
• sisyphus (king of ephyra punished to have to push a boulder up a hill repeatedly)
• cadence
• juxtaposed
• quixotic
• exotic
• enigmatic
• irrelevent
• proverbial
• gamine
• joi...

In my first interview with Noom we discussed the implementation of a support group feature (a la Weight Watchers or Alcoholics Anonymous but online). The problem: how do we divide Noom’s user base into manageably sized groups of similarly minded people? This isn’t a new problem, and the algorithm we created was essentially a k-means clustering variant, a well-known algorithm used in data mining for spotting trends.

First we vectorize the data; we think of each user as being a point in a multi-dimensional space of attributes, such as age, location, etc. For added flexibility we can even think of each user as being a weighted average of attribute vectors. This reflects the idea that some attributes are more influential in our definition of a user.

The algorithm starts by arbitrarily choosing k “means”, or points in the vector space, and then alternates between two steps:

1. Assign each...