Listen to this article instead Your browser does not support the audio element Once upon a time I wrote a post about building a toy TF-IDF keyword search engine. It has been one of the more popular posts I’ve written, and in this age of AI I felt a sequel has been long overdue. It’s pretty fast (even though it’s written in pure Python), it ranks results with TF-IDF, and it can rank 6.4 million Wikipedia articles for a given query in milliseconds. But it has absolutely no context of what words mean. ...

A few years ago I wrote a full-text search engine in 150 lines of Python. The Wikipedia data source it relied on has since been discontinued, and the tooling around it was showing its age. I wanted to (finally) write a follow-up about semantic search, but I realized that I had to get the old repository in a working state first. It’s now using Hugging Face (🤗) datasets, uv, ruff, pytest, and GitHub Actions, without touching the core search logic. ...

I haven’t really touched my blog since 2019. The theme was ancient, jQuery was everywhere, and I kept putting off the migration. Then I decided to let an LLM do it. Here’s what happened when Claude Code spent an evening trying to modernize my setup. Listen to this article instead Your browser does not support the audio element The situation It’s October 2025, and my blog is running on a Hugo theme from 2018 (the inimitable hyde-x) that was last updated when people still thought cryptocurrency was going to revolutionize everything1. The site worked fine (Google Analytics tells me there were at least three people who stumbled on my posts every month), but every time I thought about writing something new, I’d open the repo and would immediately run head-first into approximately all of the tech debt. ...

Full-text search is everywhere. From finding a book on Scribd, a movie on Netflix, toilet paper on Amazon, or anything else on the web through Google (like how to do your job as a software engineer), you’ve searched vast amounts of unstructured data multiple times today. What’s even more amazing, is that you’ve even though you searched millions (or billions) of records, you got a response in milliseconds. In this post, we are going to explore the basic components of a full-text search engine, and use them to build one that can search across millions of documents and rank them according to their relevance in milliseconds, in less than 150 lines of Python code! ...

Audio is big. Like, really big, and growing fast, to the tune of “two-thirds of the population listens to online audio” and “weekly online listeners reporting an average nearly 17 hours of listening in the last week”1. These numbers include all kinds of audio, from online radio stations, audiobooks, streaming services and podcasts (hi Spotify!). It makes sense too. Consuming audio content is easier to consume and more engaging than written content while you’re on the go, exercising, commuting or doing household chores2. But what do you do if you’re like me and don’t have the time or recording equipment to ride this podcasting wave, and just write the occasional blog post? ...

I’ve been using Lunr.js to enable some basic site search on this blog. Lunr.js requires an index file that contains all the content you want to make available for search. In order to generate that file, I had a kind of hacky setup, depending on running a Grunt script on every deploy, which introduces a dependency on node, and nobody really wants any of that for just a static HTML website. ...

Almost all modern browsers enable websites to customize the built-in search feature to let the user access their search features directly, without going to your website first and finding the search input box. If your website has search functionality accessible through a basic GET request, it’s surprisingly simple to enable this for your website too. ...

GitHub Pages has just become even more awesome. Since yesterday1, GitHub Pages supports HTTPS for custom domains. And yes, it is still free! ...

GitHub Pages is pretty awesome. It lets you push a bunch of static HTML (and/or CSS and Javascript) to a GitHub repository, and they’ll host and serve it for you. For free! ...

Bloom filters are cool. In my experience, it’s a somewhat underestimated data structure that sounds more complex than it actually is. In this post I’ll go over what they are, how they work (I’ve hacked together an interactive example to help visualise what happens behind the scenes) and go over some of their usecases in the wild. ...