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
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 launch,
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 managing. But how
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
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.
data
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
data
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
A weekly newsletter about all things data.
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 some headaches:
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
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
Walks through the implementation of Gale-Shapely's algorithm in Python.