Running RAG with a (small) language model in the browser
My blog does semantic search in your browser. Now it also runs a (small) language model in your browser.
Narrating your blog with Kokoro, a local and opensource text-to-speech model
Seven years ago I used Google's text-to-speech API to create audio versions of my posts. Now I can run an 82-million-parameter open weight model locally on my laptop, for free.
Client-side semantic search for your static site
This blog now has semantic search that runs entirely in your browser. No server, no API keys, no 23 MB transformer. The whole model is a 4 MB lookup table, and extends the existing keyword search with semantic capabilities.
Running a coding agent locally for when the budget runs out
The all-you-can-eat AI coding era is over. So what do you do on the 22nd of the month, when the token budget's gone and there's over a week of work left? Here's a local coding agent (Pi plus Qwen3-Coder on a MacBook) that's decent enough for the routine stuff and runs for free (if you have enough RAM).
Your OpenClaw can book flights. But can it survive a dungeon crawl?
Everyone's building AI agents that send emails and book flights. How about one that fights monsters in a dungeon? Here's how you can too.
Building a semantic search engine in ±250 lines of Python
Our keyword search engine can't find 'alcoholic beverage disaster in England' even though the London Beer Flood is right there. In this post, we add semantic search using sentence-transformers embeddings and cosine similarity to find documents by meaning, not just matching words.
Modernizing my 150-line Python search engine: Yahoo! dumps -> Hugging Face 🤗
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.
Migrating my hopelessly outdated Hugo blog with Claude Code
I haven't really touched my blog since 2019. The theme was ancient, jQuery was everywhere, and I kept putting off the inevitable 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.
Building a full-text search engine in 150 lines of Python code
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](https://localghost.dev/2019/09/everything-i-googled-in-a-week-as-a-professional-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](https://www.worldwidewebsize.com/)) of records, you got a response in milliseconds. In this post, we are going to build a basic full-text search engine that can search across millions of documents and rank them according to their relevance to the query in milliseconds, in less than 150 lines of code!
Use Google Cloud Text-to-Speech to create an audio version of your blog posts
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". 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 chores. 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?