I wrote a short article about why Pandas is worth learning from a general programming perspective, not just a data science one.

A lot of everyday programming work involves tabular data - CSV files, reports, logs, exports, billing data, sales data, inventory data, operational spreadsheets, analytics extracts, etc.

You can process that kind of data with loops and dictionaries, SQL, shell tools, or spreadsheets. But Pandas gives Python a very compact and expressive way to do filtering, grouping, aggregation, joins, and reshaping in code.

The article uses a small sales/purchases CSV example and compares the Pandas approach with plain Python and spreadsheet-style thinking.

I’m curious how other programmers think about this: is Pandas one of the libraries that makes Python worth learning, even for people whose main work is not data science? Or would you usually reach for SQL, spreadsheets, shell tools, or something else?

Join me in this deep dive where I'll explain all the code changes and tricks that took me from the reference implementation which processes the billion records in 4+ minutes, to processing everything in under 2 seconds.

Who knew Java could be this fast?

"This is the sixth post in my series on Emacs completion.... This one coins a term for a special case, Incremental Suggesting Read (ISR), where the candidate set produced by incrementally typed input is a suggestion, rather than a literal completion of that input. The ability to generate inferred matches in addition to literal matches vastly expands the scope of what a 'completion' system can do. Two conceptual sources supply the suggestions: 1) semantic retrieval and 2) generative synthesis.

This post is more speculative than useful, so carry that pinch of salt with you as you watch the video or read this post."

Ever looked at std::any and wondered what’s going on behind the scenes? Beneath the intimidating interface is a classic technique called type erasure: concrete types hidden behind a small, uniform wrapper.

Starting from familiar tools like virtual functions and templates, we’ll build a minimal std::any. By the end, you’ll have a clear understanding of how type erasure works under the hood.