Quickly bootstrapping a new Node.js project

A problem that often happens to me: I get the inspiration to whip up something in Node.js  (for fun, for experimentation, for a side project, etc) but then I realize that I need to go through the process of actually setting things up before I can even start writing some code.

Usually, I have to dig through previous projects and copy over my eslint and prettier config files, read through some documentation and remember how to setup TypeScript again, install the correct dependencies for running tests. Before I know it, I’m bored and tired and no longer interested in doing whatever I was going to do.

I decided to experiment with some command line tools and created a Node.js script that can help me quickly bootstrap a new project with common configuration parameters that I use. It’s available on GitHub: Bootstrap Node Project.

The GIF above shows this tool in action. I’m able to get the scaffolding for a new project up and running within about 20 seconds! After running, the project structure looks like this (with associated npm start and test scripts, all ready to go). That is pretty awesome.

my-cool-project/
├─ .husky/
├─ node_modules/
├─ src/
│ ├─ index.js (.ts)
│ ├─ index.test.js (.ts)
├─ .eslintrc.json
├─ .gitignore
├─ .prettierrc
├─ package-lock.json
├─ package.json
├─ README.md
├─ tsconfig.json (optional)

Obviously, it’s highly opinionated and caters to configuration options that I personally like to use. But I figure it’s a great resource for anyone who wishes to roll their own utility to quickly bootstrap projects as well.

 

Fun with GPT-3

Earlier this evening, I was playing around with OpenAI’s GPT-3 language model for generating “human-like text.”

I gave it the following prompt:

Write a plot about a movie that involves rogue unicorns who want to
conquer Earth and the planet’s only hope is an Australian Cattle dog named Benson who sleeps a lot.

The response?

The unicorns, led by their king, Uni, have been planning their conquest of Earth for years. They have been observing the humans and have concluded that they are weak and vulnerable. The unicorns have developed a powerful magic that will allow them to take over the planet.


Benson, an Australian Cattle dog, is the only one who can stop them. Benson is a lazy dog who loves to sleep. He is content to spend his days lounging around and eating food. However, when the unicorns begin their attack, Benson is the only one who can stop them. He rallies the other dogs and leads them in a fight against the unicorns.

The unicorns are powerful, but they are no match for Benson and the other dogs. Benson is able to defeat Uni and the other unicorns, and the Earth is saved.

Summer blockbuster coming soon to a theater near you!

Creating an automated Twitter bot about gun violence

The school shooting in Uvalde last week was horrible. As a parent, I feel so powerless to protect my kids from something like that. Taking them to school the next day was extremely emotional.

It’s clear that we, as a country, are going to continue to do nothing about guns and gun violence. I channeled some of my emotion into building an automated bot for Twitter. I call it SABSStochastic Analysis for Ballistics Superfans (alternative title is “Second Amendment Bullshit”).

If you’re so technically inclined, you can download and run it yourself. Powered by Node and a fun little experiment into Twitter’s API.

It automatically replies to any congressional member who tweets.

Which of course includes unhinged Republicans.

Debugging problems as of late

I’ve been trying to fix a particularly tricky bug lately that only ever manifests itself in our production environments. So, I’m unable to reproduce it on my local machine. It’s been fun. Let me tell you…

I’ve been stuck somewhere between stage 4 and stage 6 for weeks now.

Book Review: Deep Work by Cal Newport

The pandemic forced a change in the way many knowledge workers work. Many of us have shifted to working from home — some roles are permanent.

I’m fortunate to be in such a position, but it’s been both a blessing and difficult to adjust to.

Distractions are frequent. From regular Zoom meetings, Slack messages and various alert notifications, to email. I think a number of people (myself included) are over compensating in our communication styles.

For software engineers, this causes a lot of context switching. And that’s generally a bad thing.

Context switching can lower productivity, increase fatigue, and, ultimately, lead to developer burnout. Switching tasks requires energy and each switch depletes mental focus needed for high cognitive performance. Over an entire workday, too many context switches can leave developers feeling exhausted and drained.

The impact of context switching lingers even after switching tasks. Cognitive function declines when the mind remains fixated on previous tasks, a phenomenon known as attention residue.

I’ve recently felt myself feeling drained and less productive that usual. While browsing a thread on Hacker News, a comment on Hacker News suggested that someone should read Deep Work by Cal Newport for ideas on how to regain focus and minimize distractions. It was the first I’d heard of that book.

It was pretty enlightening and I was pretty hooked!

It has a number of self-help style steps (that are somewhat obvious, in hindsight) that you can take to improve your situation and increase productivity (e.g., carve out set times when no one can bother you, like early in the morning or late at night, keep consistent times, set reasonable expectations and have a plan, don’t wing it).

But it also had shared some interesting research on how our brains have been rewired to have shorter attention spans, thanks to all our fancy pants technology.

“Once your brain has become accustomed to on-demand distraction, Nass discovered, it’s hard to shake the addiction even when you want to concentrate. To put this more concretely: If every moment of potential boredom in your life—say, having to wait five minutes in line or sit alone in a restaurant until a friend arrives—is relieved with a quick glance at your smartphone, then your brain has likely been rewired to a point where, like the “mental wrecks” in Nass’s research, it’s not ready for deep work—even if you regularly schedule time to practice this concentration.”

Yeah… guilty.

Anyway, definitely want to put some of these ideas into practice. It was a quick read and had some concrete steps on how to improve attention and focus that I can start using immediately. Excited to try it!

Deep Work by Cal Newport

Experimenting with parallel computing using node worker_threads

I’ve wanted to play around with worker threads in Node JS, so I put together this little repository that demonstrates how it all works. Check it out here.

In order to simulate multiple threads that are each processing data, each worker thread uses a randomly generated timeout between 100 to 700 milliseconds. In addition, it has a random number of loops (between 10 and 1000) that must be completed before the worker is terminated.

It’s kind of fun to watch the tasks run and automatically complete inside the terminal (check out the screenshot of the output up top).

Advent of Code: The most wonderful time of year…

December is upon us! That means the latest edition of the Advent of Code is here.

The Advent of Code is essentially a daily programming challenge featuring a new problem each day through Christmas. The problems all relate to a certain theme. This year, it sounds like we’re going on an undersea adventure.

— Day 1: Sonar Sweep —

You’re minding your own business on a ship at sea when the overboard alarm goes off! You rush to see if you can help. Apparently, one of the Elves tripped and accidentally sent the sleigh keys flying into the ocean!

Before you know it, you’re inside a submarine the Elves keep ready for situations like this. It’s covered in Christmas lights (because of course it is), and it even has an experimental antenna that should be able to track the keys if you can boost its signal strength high enough; there’s a little meter that indicates the antenna’s signal strength by displaying 0-50 stars.

Your instincts tell you that in order to save Christmas, you’ll need to get all fifty stars by December 25th.

Collect stars by solving puzzles. Two puzzles will be made available on each day in the Advent calendar; the second puzzle is unlocked when you complete the first. Each puzzle grants one star. Good luck!

I’ll be partaking using my preferred language of choice: x86 assembly.

I kid, I kid. JavaScript.

Generating terrain maps

Recently, I went down a rabbit hole attempting to learn how to generate interesting looking maps for games (insert mind-blown-gif here). While I’m not going to be using Voronoi diagrams anytime soon,  I am still interested in attempting to generate Civilization-like maps.

Most map generation algorithms rely on perlin noise. I wanted to go a different route.

So far, these are the rules that I’ve come up with for my algorithm:

1. Create a grid of some given size (e.g., 100 x 50). Set all tiles to “ocean”.
2. Randomly pick 8 tiles (variable) across the map to seed as “land”.
3. Now iterate across all tiles on the map and build an array of tiles that are “ocean” tiles but have a land tile N/W/S/E of them.
4. Once you have this array, randomly pick a single value. Flip it from “ocean” to “land”
5. Repeat step 3 until a given number of tiles have been filled across the grid (e.g., for an “Earth-like planet”, 30% of tiles will be land).
6. Now, to add some additional randomness — iterate across all tiles and build an array containing all “land” tiles next to water (N/W/S/E).
7. Randomly pick 1, flip from land to ocean.
8. Repeat step 6 a given number of tries (e.g., 100).
9. Done! (Maybe)

Height maps, biomes and all that can come later. To quote Amit, whom I linked to earlier:

The most realistic approach would have been to define elevation first, and then define the coastline to be where the elevation reaches sea level. Instead, I’m starting with the goal, which is a good coastline, and working backwards from there.

Anyway, it’s been kind of neat to figure out.

Another example map, created using the above rules: