yuri.is

Note: Experimenting with quick writeups as I play around with LLMs and my homegrown agentic workflow system.

Note: Experimenting with quick writeups as I play around with LLMs and my homegrown agentic workflow system.

Note: Experimenting with quick writeups as I play around with LLMs and my homegrown agentic workflow system.

Note: Experimenting with quick writeups as I play around with LLMs using my homegrown agentic workflow system.

Sometimes the threads of the universe weave together in unexpected ways: one of my favorite books, English as She Is Spoke, turns out to have also been enjoyed by Abraham Lincoln.

I was thinking about the Morris counter algorithm and wondered if it would be possible to combine it with the H2 histogram encoding to have more control over the standard deviation of the approximation in exchange for spending more bits to represent the s…

Previously: Podcast Vibes Presentation.

In redesigning this site I wanted to prevent widows in headlines, ensuring that individual overflowing words don’t wrap onto a new line.

Information is independent of its representation, and any particular data representation makes some things easy and other things hard.

Years ago I read a book where the author interviewed 40 famous opera singers, including Pavarotti, on their technique.

As a long-time fan of names and nominative determinism, I wanted to know how well language models could assess the fit between a person’s name and their chosen profession.

I’m winding down my podcast project and wanted to write a note about some of the things it taught me.

I gave a talk today about my podcast analysis project (previously: Note: Podcast Vibes, Podcast Vibes Prototyping), connecting it to some visualization design work from over a decade ago which explored ways to visualize and organize text corpora with vect…

A neat idea I learned today: You can make a doubly-linked list that only needs to store a single pointer – the xor of the prev and next pointers – to allow traversal of the list from either direction.

I’m considering turning my little research note on podcast vibes into a bit more of a product.

Here’s a research note on my recent explorations into using LLMs to analyze the emotional content of podcasts.

This is a follow-up to Wavelet Matrix Construction.

This is a follow-up post to Enter the Wavelet Matrix.

Imagine you have an array of integers and want to preprocess it so that you can answer quantile queries, such as computing the median or the 99th percentile, in constant time.

The Hilbert Curve is space-filling fractal that, in the limit, will fill any power-of-two-sized square with an increasingly dense squiggle that fills the plane.

Reader-side LLMs enable information filtering and synthesis to be done on behalf of the reader.

Splash is a color format that reduces the space of choices in a playful way and lends itself to interesting UI design possibilities.

I like thinking through ideas on a walk and sometimes record voice notes to myself as I go.

I came across a nice dependency-free library today, hyparquet, for parsing Parquet files in JavaScript.

In the 1970s, decades before computers began to compete at the highest levels of chess, a new variant of the game was invented, called Advanced Chess.

If you use an editor with Rust’s LSP integration, you can put your cursor on a particular derive macro, such as Clone in #[derive(Clone)], or an the name of a macro application, such as matches!

Here’s a git pre-commit hook to auto-format code on commit.

I use Netlify to host small static sites like this one.

Status: This was written in a hurry.

A great JavaScript library has recently reached 1.0: Cheerio.

The National Electronic Injury Surveillance System in the US collects data on product-related injuries.

Status: First draft; written in a hurry.

Imagine there are three teams of runners who are about to compete against each other in a team-vs-team race.

Sublime Text This is my text editor of choice for many years and I’m a happy paying customer.

A quick note on how I’m syncing Bear with this site, since I haven’t done this before.

This is my place for rough ideas and half-baked thoughts.

I recently learned there’s a useful connection between Euclidean distance and Pearson correlation: the squared Euclidean distance between two z-normalized vectors equals their Pearson distance, up to a scale factor.