ai
Ten days ago, I started building a diagram library from scratch. Today I'm releasing Flora – a fault-tolerant, Mermaid-compatible diagram library that renders polished, interactive SVGs from imperfect input. I want to talk about Flora, but I also want to talk about how it got built, because
ai
A few months ago, I participated in a company-wide hackathon with a group of data engineers. The project was straightforward enough: build tooling to automate some of our data modeling conventions – tasks like auto-generating documentation, scaffolding schema files, and enforcing naming patterns. The kind of stuff that everyone
ai
I've been spending a lot of time lately figuring out which AI tools to invest in. New models, new frameworks, new CLI tools, new IDE integrations (or no IDE at all?!). The ecosystem is moving so fast that just orienting yourself feels like a project in its own
ai
I've been using Claude Code almost every day for over six months now, and the thing that surprises me most isn't how fast it is. It's how quickly it exposes the gaps in my own thinking. I'll prompt something like "clean
ai
If you've been following AI news lately, you've probably heard about the viral rise of Moltbook. The concept is wild: a Reddit-style social network exclusively for AI agents. Bots post, comment, and interact with each other while humans can only watch. Within 48 hours of
new tools
I’ve been back at work for three weeks after seven-and-a-half months of maternity leave, and it genuinely feels like I've returned to a completely different industry. The fundamentals haven't changed: data pipelines still break in unexpected ways, and tech debt still needs
visualization
Last week, I followed the 2025 New York City mayoral election and was so impressed by how clearly the New York Times visualized the election results. Their choropleth map did a great job of capturing the city's political mood at a glance. You can see entire boroughs tilt
A weekly newsletter about all things data.
In a previous post, Is Marimo the New Jupyter Notebook?, I wrote about all of the great things that Marimo has to offer. While I still think Marimo is an exciting new tool, I tried using it for a few analyses and found myself wishing I was using Jupyter instead.
This past summer, I started using Strava as a kind of diary to record my walks and runs while on maternity leave. Between nap schedules and coffee runs, I ended up exploring several new coffee shops and walking routes around the neighbourhood. By the end of summer, I had logged
When I meet other data scientists in the wild, my favorite question to ask is this: If you could get your hands on any dataset in the world, what would it be? For me, the answer is easy — dating app data. I've always been fascinated by how our
I've been using Jupyter notebooks for almost a decade to power everything from quick exploratory data analyses to end-to-end machine learning prototypes. While these notebooks have served their purpose, I'm sure that the majority of data scientists will agree that they also come with
BigQuery is powerful — but it can also be expensive and slow if you don’t know what you’re doing. It's easy to accidentally scan terabytes of data when you only meant to query a few gigabytes. Mistakes like these can end up costing hundreds (and sometimes thousands)
Let's dig into what DuckDB is and why there's so much hype around it.
After working with BigQuery for almost five years, here are five features that I wish I knew about earlier in my career.
I'm back from a one year hiatus and I'm excited to bring this blog back to life.
Let's learn how to handle large data with Pandas, Polars, and DuckDB.
PyCon US is the largest Python conference in the world. This year, over two thousand people from around the world gathered at the David L. Lawrence Convention Centre in Pittsburgh, Pennsylvania to meet and talk about all things Python. PyCon runs over the course of one week - two days of
A comprehensive overview of data cleaning and feature engineering techniques for clinical data
In 2006, Netflix, which was then a DVD rental service, announced a data science competition for movie rating predictions. The company would offer a $1 million grand prize to the team that could improve their existing recommender system's prediction accuracy by 10%. The competition garnered much interest from