Avatar The Last Airbender Language Model and Text Generation

2020-08-15

This week’s TidyTuesday data set is basically the script for all of the Avatar: The Last Airbender episodes. I thought it’d be neat to build a language model from the script and use it to generate new sentences that plausibly sound like they belong in the Avatar universe. In this post, I’ll quickly present some of the sentences generated from the model, and then I’ll dive into how I obtained those results.…

A Tidy Analysis of Astronauts and Their Missions

2020-07-21

Introduction I live in Orlando Florida, which is less than an hour away from Cape Canaveral. As you might imagine, visiting the space center and watching launches is a “thing” we Orlando folks do fairly often. I’ve also been getting into R and data science recently via Garrett Grolemund and Hadley Wickham’s excellent R for Data Science. To apply the things I’m learning, I thought it’d be fun to analyze this week’s Tidy Tuesday astronauts dataset.…

Analyzing Traffic Fatalities by Vehicle Weight and Size with Tidyverse

2020-07-03

I bought a car recently. I’m cheap and practical and I’m going to be a father soon, so the only things I really cared about were: will the car keep my wife and child safe if they’re in an accident will the car keep running reliably how do I get 👆 those two things on the cheap I’ve also been getting into machine learning and data science recently, so I thought it’d be useful to answer them using some data science tools.…

Should we refactor files we're working on?

2020-05-09

When programming, always follow the camping rule: Always leave the code base healthier than when you found it. — Martin Fowler, Refactoring The Boy Scouts of America have a simple rule that we can apply to our profession. Leave the campground cleaner than you found it. — Robert Martin, Clean Code Many of us share the attitude expressed by the above Fowler and Martin quotes. The attitude presumes that code we’re working on now will change again soon, and we’ll reap the benefits of a refactor when that happens.…

Why and How we Should Calculate Expected Utility of Refactorings

2020-04-29

In my experience, most applications are a mess…Changes are commonly made under urgent time pressure, which drives applications towards disorder…Velocity gradually slows, and everyone comes to hate the application, their job, and their life. —Sandi Metz, “The Half-Life of Code” Why Many of us work in codebases that are not easy to work with, codebases that we want to make better. The way that we typically choose what parts of the codebase get made better, however, is sub-optimal.…

My Mid-Career Job-Hunt: A Data Point for Job-Seeking Devs

2020-01-26

I wrapped up my job search recently, and I’m happy to say that I’ll be joining a YC-backed startup called “Heap.” I thought I’d share a little bit about my job search in case the information may benefit other job-seeking devs. I’ll go over the pipeline of places I applied to and the result of each application. I’ll also talk about things like salary, resume formatting, coding challenge prep, and interviewing.…

Maybe Don't Inject That

2019-10-03

I’m actually always suspicious when I’m writing up a pattern or a technique if I can’t talk about reasons when you shouldn’t use and trade-offs against it. If I can’t find arguments against it, then I’m worrying that I’m not really analyzing things properly. –Martin Fowler, “Is TDD Dead?”, 35:00 DI is useful. It’s great. It makes your code more flexible, testable, etc. Dagger? Also useful. Great. My favorite DI library.…

Maybe Don't Write That Test

2019-09-19

Testing seems to be like going to gym. Everyone feels like “yeah. I should be testing. I should be going to the gym everyday.” Koushik Goupal, Fragmented, “Episode 13,” 12:01 Remember those gimmicky fitness products that made you think you could “get fit” without actually going to the gym/dieting/etc? Because I live in Orlando and have seen the Carousel of Progress at the Magic Kingdom a bunch of times, the first example of this kind of gimmicky product that comes to mind is a thing called an “exercise belt.…

An Intro to Gradient Descent for Kotlin Programmers

2019-09-01

Introduction Gradient descent is an algorithm that’s used to solve supervised learning and deep learning problems. Here I’m going to try to give you an idea of why the algorithm works and how you’d implement it in Kotlin. I’ll also show the algorithm working with a simple kaggle dataset involving video game sales and ratings. Everything I cover here is covered in Andrew Ng’s excellent Coursera machine learning course with the exception of the Kotlin implementation of gradient descent.…

Dagger 2, 2 Years Later

2019-08-27

…in software, feedback cycles tend to be on the order of months, if not years…It’s during the full lifetime of a project that a developer gains experience writing code, source controlling it, modifying it, testing it, and living with previous design and architecture decisions during maintenance phases. With everything I’ve just described, a developer is lucky to have a first try of less than six months… –Erik Dietrich, “How Developers Stop Learning: Rise of the Expert Beginner”…