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Transitioning to using AI tools Despite working in the applied AI field for the last couple of years, I’ll admit that for a while I was resisting using it in my daily life, or my coding.

This post is about things I wish all eval tools provided (but many don’t).

Recently, I’ve been surprised to note how many companies aren’t just returning to offices (ugh, open offices are the worst!)

Some folks love doing demo days, but I find it sometimes gets in the way of actual work, creates more work in the long run, and/or can be downright demoralizing (especially to backend teams).

Some observations on vibe coding I get a lot of questions about working with LLMs.

I’ve been doing a lot of work around grief lately.

Life is growth.

This is one of those posts that I’m writing mostly because I’m still frequently hearing people say they’re struggling with the question of how to interview data people.

I decided to embark on this comparison mostly out of curiosity.

Coming from a background in bench science, or what we affectionately referred to as “wetlab”, I like to test everything I do, and I like my tests to be fast and representative of what I expect to find when I run things “for real”.

Recently, a coworker asked me what the difference is between data science and data engineering.

Question: How come we don’t have more diversity at our company?

Recently, someone asked for help with setting up postgres in docker and connecting to it with python.

I was chatting with some friends recently, and this question came up: What does a good working relationship with your manager look like?

I frequently hear Python referred to as a ‘scripting’ language, because it’s not compiled.

If you do a lot of data pipelining, you’ve probably heard a lot about Airflow by now.

To make a long, mostly whiny story short, as part of my current role, I’ve been doing a lot of fighting with AWS to help support my team.

The scene: I needed to process data from an s3 bucket using pyspark.

Why Spark?

Airflow for hands-off ETL Almost exactly a year ago, I joined Yahoo, which more recently became Oath.

Over the years, I’ve done a few data science coding challenges for job interviews.

Things I learned while following a tutorial on how to build reusable models with scikit-learn.

Here is a story about an interesting interview question and how I approached it.

I don’t believe truth is a finite value.

When to test, and why: • Write a test for every method. • Write a test any time you find a bug!

For better or worse, when you’re dealing with data pipelines of varying shapes and sizes, sometimes you need to combine objects that don’t match up evenly.

Am I getting slower every day?

In continuing my series on investigating bike data, I ran into some interesting aspects of working with device data.

One of the main reasons this project turned out to be interesting is that time series data has all kinds of gotchas.

One thing I wanted to do with this data set was experiment with plotting methods.

So I have some bike data that I parsed out of XML and put into a pandas dataframe.

In an effort to advance my python skills, I spent some time slowly pecking away at the puzzles on pythonchallenge.

I know that from the outside, ‘science’ seems like The Place Where Scientists Live.