Simon Willison's Weblog: mandelbrot

Mandelbrot in x86 assembly by Claude

2025-07-02T04:35:35+00:00

Mandelbrot in x86 assembly by Claude

Inspired by a tweet asking if Claude knew x86 assembly, I decided to run a bit of an experiment.

I prompted Claude Sonnet 4:

Write me an ascii art mandelbrot fractal generator in x86 assembly

And got back code that looked... like assembly code I guess?

So I copied some jargon out of that response and asked:

I have some code written for x86-64 assembly using NASM syntax, targeting Linux (using system calls for output).

How can I run that on my Mac?

That gave me a Dockerfile.

I tried running it on my Mac and... it failed to compile.

So I fired up Claude Code (with the --dangerously-skip-permissions option) in that directory and told it what to run:

Run this: docker build -t myasm .

It started crunching. It read the errors, inspected the assembly code, made changes, tried running it again in a loop, added more comments...

...and eventually produced an ASCII art image of a fractal! Claude Code proudly declared:

Success! The Docker container ran and generated a Mandelbrot set visualization using ASCII characters. The output shows all @ symbols, which indicates the algorithm is working but all points are inside the set (high iteration count). The program compiled and executed successfully on x86_64 architecture via emulation.

Yeah... I was not satisfied with an image that was entirely @ symbols! I said:

That is a bad result - it is just @ signs, I cannot see the fractal

Claude made a few more changes and gave me the result I wanted:

Here's the finished assembly code, the Dockerfile to run it on a Mac and the full transcript of the Claude Code session that got it there.

Tags: mandelbrot, ai, generative-ai, llms, ai-assisted-programming, anthropic, claude, vibe-coding, claude-code

Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac

2024-11-12T23:37:36+00:00

There's a whole lot of buzz around the new Qwen2.5-Coder Series of open source (Apache 2.0 licensed) LLM releases from Alibaba's Qwen research team. On first impression it looks like the buzz is well deserved.

Qwen claim:

Qwen2.5-Coder-32B-Instruct has become the current SOTA open-source code model, matching the coding capabilities of GPT-4o.

That's a big claim for a 32B model that's small enough that it can run on my 64GB MacBook Pro M2. The Qwen published scores look impressive, comparing favorably with GPT-4o and Claude 3.5 Sonnet (October 2024) edition across various code-related benchmarks:

How about benchmarks from other researchers? Paul Gauthier's Aider benchmarks have a great reputation and Paul reports:

The new Qwen 2.5 Coder models did very well on aider's code editing benchmark. The 32B Instruct model scored in between GPT-4o and 3.5 Haiku.

84% 3.5 Sonnet, 75% 3.5 Haiku, 74% Qwen2.5 Coder 32B, 71% GPT-4o, 69% Qwen2.5 Coder 14B, 58% Qwen2.5 Coder 7B

That was for the Aider "whole edit" benchmark. The "diff" benchmark scores well too, with Qwen2.5 Coder 32B tying with GPT-4o (but a little behind Claude 3.5 Haiku).

Given these scores (and the positive buzz on Reddit) I had to try it for myself.

My attempts to run the Qwen/Qwen2.5-Coder-32B-Instruct-GGUF Q8 using llm-gguf were a bit too slow, because I don't have that compiled to use my Mac's GPU at the moment.

But both the Ollama version and the MLX version worked great!

I installed the Ollama version using:

ollama pull qwen2.5-coder:32b

That fetched a 20GB quantized file. I ran a prompt through that using my LLM tool and Sergey Alexandrov's llm-ollama plugin like this:

llm install llm-ollama
llm models # Confirming the new model is present
llm -m qwen2.5-coder:32b 'python function that takes URL to a CSV file and path to a SQLite database, fetches the CSV with the standard library, creates a table with the right columns and inserts the data'

Here's the result. The code worked, but I had to work around a frustrating ssl bug first (which wouldn't have been an issue if I'd allowed the model to use requests or httpx instead of the standard library).

I also tried running it using the Apple Silicon fast array framework MLX using the mlx-llm library directly, run via uv like this:

uv run --with mlx-lm \
  mlx_lm.generate \
  --model mlx-community/Qwen2.5-Coder-32B-Instruct-8bit \
  --max-tokens 4000 \
  --prompt 'write me a python function that renders a mandelbrot fractal as wide as the current terminal'

That gave me a very satisfying result - when I ran the code it generated in a terminal I got this:

MLX reported the following performance metrics:

Prompt: 49 tokens, 95.691 tokens-per-sec
Generation: 723 tokens, 10.016 tokens-per-sec
Peak memory: 32.685 GB

Let's see how it does on the Pelican on a bicycle benchmark.

llm -m qwen2.5-coder:32b 'Generate an SVG of a pelican riding a bicycle'

Here's what I got:

Questionable Pelican SVG drawings aside, this is a really promising development. 32GB is just small enough that I can run the model on my Mac without having to quit every other application I'm running, and both the speed and the quality of the results feel genuinely competitive with the current best of the hosted models.

Given that code assistance is probably around 80% of my LLM usage at the moment this is a meaningfully useful release for how I engage with this class of technology.

Tags: mandelbrot, open-source, ai-in-china, llm, llms, ollama, qwen, paul-gauthier, mlx, llm-release, ai, local-llms, uv, generative-ai, ai-assisted-programming, pelican-riding-a-bicycle

Claude 3.5 Sonnet

2024-06-20T18:01:26+00:00

Claude 3.5 Sonnet

Anthropic released a new model this morning, and I think it's likely now the single best available LLM. Claude 3 Opus was already mostly on-par with GPT-4o, and the new 3.5 Sonnet scores higher than Opus on almost all of Anthropic's internal evals.

It's also twice the speed and one fifth of the price of Opus (it's the same price as the previous Claude 3 Sonnet). To compare:

gpt-4o: $5/million input tokens and $15/million output
Claude 3.5 Sonnet: $3/million input, $15/million output
Claude 3 Opus: $15/million input, $75/million output

Similar to Claude 3 Haiku then, which both under-cuts and out-performs OpenAI's GPT-3.5 model.

In addition to the new model, Anthropic also added a "artifacts" feature to their Claude web interface. The most exciting part of this is that any of the Claude models can now build and then render web pages and SPAs, directly in the Claude interface.

This means you can prompt them to e.g. "Build me a web app that teaches me about mandelbrot fractals, with interactive widgets" and they'll do exactly that - I tried that prompt on Claude 3.5 Sonnet earlier and the results were spectacular (video demo).

An unsurprising note at the end of the post:

To complete the Claude 3.5 model family, we’ll be releasing Claude 3.5 Haiku and Claude 3.5 Opus later this year.

If the pricing stays consistent with Claude 3, Claude 3.5 Haiku is going to be a very exciting model indeed.

Tags: mandelbrot, ai, generative-ai, llms, anthropic, claude, vision-llms, claude-artifacts, claude-3-5-sonnet, llm-release, prompt-to-app

textual-mandelbrot

2023-04-01T19:23:26+00:00

textual-mandelbrot

I love this: run “pipx install textual-mandelbrot” and then “mandelexp” to get an interactive Mandelbrot fractal exploration interface right there in your terminal, built on top of Textual. The code for this is only 250 lines of Python and delightfully easy to follow.

Via @davepdotorg

Tags: mandelbrot, python, textual

SQLite Query Language: WITH clause

2017-11-26T07:23:12+00:00

SQLite Query Language: WITH clause

SQLite’s documentation on recursive CTEs starts out with some nice clear examples of tree traversal using a WITH statement, then gets into graphs, then goes way off the deep end with a Mandelbrot Set query and a query that can solve Soduku puzzles (“in less than 300 milliseconds on a modern workstation”).

Tags: mandelbrot, sql, sqlite

Mandelbrot set in PostgreSQL

2009-08-13T14:23:19+00:00

Mandelbrot set in PostgreSQL

Surprisingly short SQL statement that produces an ASCII art Mandelbrot set.

Tags: ascii-art, fractals, mandelbrot, postgresql, sql