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A review of the things I have learned from building in the open over the past year. Thoughts and reflections on what it takes to grow a project and the difficulty translating open-source success to commerical success.

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A review of the things I have learned from building in the open over the past year. Thoughts and reflections on what it takes to grow a project and the difficulty translating open-source success to commerical success.- Published on

An exploration of markdown and HTML syntax trees. Documenting my experience creating rehype-prism-plus, a syntax highlighting plugin that creates pretty code blocks.- Published on

A good project is only one part of the puzzle. Getting stars is really all about marketing and promoting it. A guide on growth hacking a Github project.- Published on

Learn Julia by implementing Schelling's famous segregation model. You will see many similarities to Python - no types need to be specified (it's a dynamic language) and pick up some nice syntactical properties of Julia.- Published on

A revised benchmark of graphs / network computation packages featuring an updated methodology and more comprehensive testing. Find out how Networkx, igraph, graph-tool, Networkit, SNAP and lightgraphs perform- Published on

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The serverless way - using Google Cloud Platform to deploy simple machine learning models via Cloud Run. A fun weekend project that analyses the twitter-verse- Published on

Tips and tricks to speed up R and plotly based web apps- Published on

Benchmark of 5 popular graph/network packages - Networkx, igraph, graph-tool, Networkit and SNAP- Published on

Technical overview of our 2nd place solution and my experience at the Binance hackathon- Published on

In this post, I explore the problem of simplifying route intersections and document some Python code that can be used to clean and visualize Open Street Maps as a network representation- Published on

Part II in the network exploration of the Game of Thrones series. In this post, we combine the plots together and use gganimate to visualise relationships across all 5 books- Published on

A network exploration on the links between characters in the Game of Thrones series with the help of igraph and tidygraph- Published on

Chains, Forks, Colliders, paths and d-seperation - how DAGs can contribute to better causal inference- Published on

Deriving the OLS formula as a means of approximating the conditional expectation function- Published on

A reference cheatsheet on adjacency matrix, incidence matrix, laplacian matrix and the basics of algebraic graph theory- Published on

How should we choose the control group in a situation where we have multiple treatments and time periods? A simple statistical simulation exercise- Published on

Applying the SVD to the regression framework- Published on

To what extent do the coefficients obtained from a regression carried out at the group level correspond to the estimates at the individual level?- Published on

Deriving the OLS estimator via the maximum likelihood approach- Published on

A tutorial on using Leaflet in R for geospatial visualisation- Published on

Establishing the OLS formula via the method of moments approach- Published on

Deriving the OLS estimator - projection method- Published on

This post is the first in a series of my study notes on regression techniques. It covers regression as a solution to the least squares minimisation problem