I’m aiming to setup a space for more interactive UX experiments. My current Hugo blog has held up well with my scale of content but doesn’t play nicely with modern Javascript frameworks, where most of the open source energy is currently invested.

Astro seemed like a promising option because it supports Markdown content along with plug-and-play approach to many different frameworks like React, Svelte and Vue. More importantly, there is a precedent for flexibility when the Next Big Thing emerges which makes Astro a plausible test bed for new concepts without requiring a brand new site or a rewrite. At least, this was my thought process when I decided to try it out.

In this notebook, we’ll use the MovieLens 10M dataset and collaborative filtering to create a movie recommendation model. We’ll use the data from movies.dat and ratings.dat to create embeddings that will help us predict ratings for movies I haven’t watched yet.

Create some personal data

Before I wrote any code to train models, I code-generated a quick UI to rate movies to generate my_ratings.dat, to append to ratings.dat. There is a bit of code needed to do that. The nice part is using inline script metadata and uv, we can write (generate) and run the whole tool in a single file.

I’ve started posting more on Bluesky and I noticed that articles from my site didn’t have social image previews 😔

I looked into Poison’s code (the theme this site is based on) and found that it supports social image previews at the site level or in the site’s assets folder.

This approach didn’t quite work for me. I recently switched to using page bundles which group markdown and content in the same folder and make linking to images from markdown straightforward. With a few modifications, I was able to make the code work to use images in the page bundles for social previews as well.

I explored how embeddings cluster by visualizing LLM-generated words across different categories. The visualizations helped build intuition about how these embeddings relate to each other in vector space. Most of the code was generated using Sonnet.

!pip install --upgrade pip
!pip install openai
!pip install matplotlib
!pip install scikit-learn
!pip install pandas
!pip install plotly
!pip install "nbformat>=4.2.0"

We start by setting up functions to call ollama locally to generate embeddings and words for several categories. The generate_words function occasionally doesn’t adhere to instructions, but the end results are largely unaffected.

Using Cursor, we can easily get a first pass at creating alt text for an image using a language model. It’s quite straightforward using a multi-modal model/prompt. For this example, we’ll use claude-3-5-sonnet-20241022.

Here’s what it generates.

Having completed lesson 5 of the FastAI course, I prompted Claude to give me some good datasets upon which to train a random forest model. This housing dataset from Kaggle seemed like a nice option, so I decided to give it a try. I am also going to try something that Jeremy Howard recommended for this notebook/post, which is to not refine or edit my process very much. I am mostly going to try things out and if they don’t work, I’ll try and write up why and continue, rather than finding a working path and adding commentary at the end.

In this notebook/post, we’re going to be using the markdown content from my blog to try a language model. From this, we’ll attempt to prompt the model to generate a post for a topic I might write about.

Let’s import fastai and disable warnings since these pollute the notebook a lot when I’m trying to convert these notebooks into posts (I am writing this as a notebook and converting it to a markdown file with this script).

In this notebook, we train two similar neural nets on the classic Titanic dataset using techniques from fastbook chapter 1 and chapter 4.

The first, we train using mostly PyTorch APIs. The second, with FastAI APIs. There are a few cells that output warnings. I kept those because I wanted to preserve print outs of the models’ accuracy.

The Titanic data set can be downloaded from the link above or with:

!kaggle competitions download -c titanic

To start, we install and import the dependencies we’ll need:

I use direnv to manage my shell environment for projects. When using a Jupyter notebook within a project, I realized that the environment variables in my .envrc file were not being made available to my notebooks. The following worked for me as a low-effort way to load my environment into the notebook in a way that wouldn’t risk secrets being committed to source control, since I gitignore the .envrc file.

The code below assumes an .envrc file exists in the project root, containing

I upgraded to macOS Sequoia a few weeks ago. I had a feeling this update wasn’t going to be trivial with my Nix setup, but after trying to upgrade to a newer package version on unstable, I got a message that seemed to imply I needed to upgrade the OS, so I went for it. Also, I was at least confident I wouldn’t lose too much about my setup given it’s all committed to version control in my nix-config repo.