I built an Astro component called CodeToggle.astro for my experimental site. The idea was to create a simple wrapper around a React (or other interactive component) in an MDX file so that the source of that rendered component could be nicely displayed as a highlighted code block on the click of a toggle. Usage looks like this:

import { default as TailwindCalendarV1 } from "./components/TailwindCalendar.v1";
import TailwindCalendarV1Source from "./components/TailwindCalendar.v1?raw";

<CodeToggle source={TailwindCalendarV1Source}>
  <TailwindCalendarV1 client:load />
</CodeToggle>

The implementation of CodeToggle.astro looked like this

Deepseek is getting a lot of attention with the releases of V3 and recently R1. Yesterday, they also released “Pro 7B” version of Janus, a “Unified Multimodal” model that can generate images from text and text from images. Most models I’ve experimented with can only do one of the two.

The 7B model requires about 15GB of hard disk space. It also seemed to almost max out the 64GB of memory my machine has. I’m not deeply familiar with the hardware requirements for this model so your mileage may vary.

The llm package uses a plugin architecture to support numerous different language model API providers and frameworks. Per the documentation, these plugins are installed using a version of pip, the popular Python package manager

Use the llm install command (a thin wrapper around pip install) to install plugins in the correct environment: llm install llm-gpt4all

Because this approach makes use of pip occasionally we run into familiar issues like pip being out of date and complaining about it on every use

Today, Anthropic released Citations for Claude.

In the release, Anthropic disclosed the following customer case study:

“With Anthropic’s Citations, we reduced source hallucinations and formatting issues from 10% to 0% and saw a 20% increase in references per response. This removed the need for elaborate prompt engineering around references and improved our accuracy when conducting complex, multi-stage financial research,” said Tarun Amasa, CEO, Endex.

I decided to kick the tires on this feature as I thought it could slot in very nicely with a project I am actively working on. Also, I couldn’t quickly find Python code I could copy and run so I conjured some.

Today, I needed to turn SVGs into PNGs. I decided to use Deno to do it. Some cursory searching showed Puppeteer should be up to the task. I also found deno-puppeteer which seemed like it would provide a reasonable way to make this work.

To start, let’s set up a deno project

deno init deno-browser-screenshots
deno-browser-screenshots

Using puppeteer

Now, add some code to render an SVG with Chrome via puppeteer.

import puppeteer from "https://deno.land/x/[email protected]/mod.ts";

const svgString = `
<svg width="512" height="512" xmlns="http://www.w3.org/2000/svg">
  <rect width="100%" height="100%" fill="#87CEEB"/>
  <circle cx="256" cy="256" r="100" fill="#FFD700"/>
  <path d="M 100 400 Q 256 300 412 400" stroke="#1E90FF" stroke-width="20" fill="none"/>
</svg>`;

if (import.meta.main) {
  try {
    const browser = await puppeteer.launch({
      headless: true,
      args: ["--no-sandbox"],
    });

    const page = await browser.newPage();

    await page.setViewport({ width: 512, height: 512 });
    await page.setContent(svgString);

    await page.screenshot({
      path: "output.png",
      clip: {
        x: 0,
        y: 0,
        width: 512,
        height: 512,
      },
    });

    await browser.close();
  } catch (error) {
    console.error("Error occurred:", error);
    console.error("Make sure Chrome is installed and the path is correct");
    throw error;
  }
}

When we run this code, we get the following error

About 6 months ago, I experimented with running a few different multi-modal (vision) language models on my Macbook. At the time, the results weren’t so great.

An experiment

With a slight modification to the script from that post, I tested out llama3.2-vision 11B (~8GB in size between the model and the projector). Using uv and inline script dependencies, the full script looks like this

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "ollama",
# ]
# ///

import os
import sys
import ollama

PROMPT = "Describe the provided image in a few sentences"


def run_inference(model: str, image_path: str):
    stream = ollama.chat(
        model=model,
        messages=[{"role": "user", "content": PROMPT, "images": [image_path]}],
        stream=True,
    )

    for chunk in stream:
        print(chunk["message"]["content"], end="", flush=True)


def main():
    if len(sys.argv) != 3:
        print("Usage: python run.py <model_name> <image_path>")
        sys.exit(1)

    model_name = sys.argv[1]
    image_path = sys.argv[2]

    if not os.path.exists(image_path):
        print(f"Error: Image file '{image_path}' does not exist.")
        sys.exit(1)

    run_inference(model_name, image_path)


if __name__ == "__main__":
    main()

We run it with

Deepseek V3 was recently released: a cheap, reliable, supposedly GPT-4 class model.

Quick note upfront, according to the docs, there will be non-trivial price increases in February 2025:

• Input price (cache miss) is going up to $0.27 / 1M tokens from $0.14 / 1M tokens (~2x)
• Output price is going up to $1.10 / 1M tokens from $0.28 /1M tokens (~4x)

From now until 2025-02-08 16:00 (UTC), all users can enjoy the discounted prices of DeepSeek API

Read More…

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.