Projects

My side projects span machine learning, programming languages, personal analytics, browser extensions, code editors, and personal tools to support my workflows. I work on projects for fun, to learn, or to solve a specific problem I have. More recent projects are listed first.

Table of contents

Plum dispatch for fastcore (experimental)

An experimental PR to fastcore (the core library powering the fastai deep learning framework), that replaces its custom type dispatch system with plum-dispatch. Both bring Julia's multiple dispatch into Python using type annotations and decorators.

EasyEquities browser extension

A browser extension for the EasyEquities investment platform built with TypeScript, React, React Router, and Mock Service Worker (to develop against a mocked version of their API).

This started with frustration that EasyEquities didn't provide a single view of my holdings across all of my investment accounts, nor of my time-weighted returns. I made good progress but stopped building this since there's no public EasyEquities API, and sending handcrafted requests to the internal API of my investment platform seems like a bad idea!

A tiny command-line tool to quickly open URLs related to your repos. I made this for a smoother experience while working on a manyrepo codebase.

MyFitnessPal to SQLite

Save your personal data from MyFitnessPal to a SQLite database. I occasionally use MyFitnessPal to track my weight and calories. Inspired by the dogsheep movement, I built this to afford personal analytics on my own data.

CircleCI to SQLite

Save your personal data from CircleCI to a SQLite database. I threw this together for a quick analysis on build times at a former workplace, which we could then follow up with easy build pipeline optimisations.

Reverse engineering ncode

An attempt at reverse engineering ncode paper technology. I got all the way down from what looked like dots on paper to a matrix which I needed to decode. Perhaps I'll get back to it some day!

Romulus

An experimental framework for creating structure-aware editors. The idea was that actions (e.g. move left, insert character) would be aware of the context surrounding the cursor location within the (tree-)structured document, thus would have slightly more intelligent behaviour.

For example, given a document ((foo)|, where | represents the cursor, and where ((foo)) is a known symbol represented some BlockRef object, inserting ) would know to create a BlockRef object in the internal tree structure. Like a hackier version of tree sitter. The vision was to use this framework to create a Roam clone using an enhanced markdown-like syntax for personal use.

Zip

Functional hierarchical zipper (tree cursor), with navigation, editing, and enumeration. A port of clojure.zip to JavaScript that I intended to use in Romulus.

Roam tools

Tiny command-line tools for working with Roam graphs.

Roam parser

A tiny Roam parser built with Clojure and instaparse. I also live tweeted the entire development process.

Image alignment

Keypoint-based alignment of two grayscale images using ORB and RANSAC via skimage. This was completed as a take-home assignment for a job application, so I limited the implementation to a max of 8 hours.

Editor

Minimal terminal text editor written in Python and curses. I wrote a corresponding step-by-step tutorial as well.

Advent of Code

I enjoyed taking part in AoC for years 2017, 2018, 2019, and 2020.

Alfred Github local

An Alfred workflow to open GitHub repo URLs via a local workspace directory. No GitHub API access needed!

pdlog

Seamless logging for pandas dataframe operations, inspired by tidylog. We used pandas in production extensively at a former workplace, and our code often ended up overwhelmed with logging logic. With pdlog it's simple: instead of calling, say, df.dropna(), call df.log.dropna() and it'll log <pdlog> dropna: dropped 1 row (17%), 5 rows remaining.

Neural networks from scratch

A basic implementation of neural networks from scratch. Shortly after my encounter with reinforcement learning (see below), I realised that deep learning was an important precursor and shifted my studies there.

Reinforcement learning from scratch

Re-implementing sections of Sutton and Barto's Reinforcement Learning: An Introduction. My first inspiration in AI was the possibility that a computer could play games better than the best humans! I was determined to build one of these AIs myself.