Simon Willison — Week of 2026-04-11 to 2026-04-17#

Highlight of the Week#

This week’s most striking revelation came from Simon’s infamous “pelican riding a bicycle” SVG generation benchmark, where a 21GB quantized local model (Qwen3.6-35B-A3B) unexpectedly outperformed Anthropic’s brand-new Claude Opus 4.7 flagship. Running locally on a MacBook Pro via LM Studio, Qwen generated a better bicycle frame and even won a secret unicycle backup test, leading Simon to conclude that his joke benchmark’s long-standing correlation with general model utility has finally broken down.

Simon Willison — 2026-04-12#

Highlight#

Simon shares a highly practical, single-command recipe for running local speech-to-text transcription on macOS using the Gemma 4 model and Apple’s MLX framework. It is a prime example of his ongoing exploration into making local, multimodal LLMs frictionless and accessible using modern Python packaging tools like uv.

Posts#

[Gemma 4 audio with MLX] · Source Thanks to a tip from Rahim Nathwani, Simon demonstrates a quick uv run recipe to transcribe audio locally using the 10.28 GB Gemma 4 E2B model via mlx-vlm. He tested the pipeline on a 14-second voice memo, and while it slightly misinterpreted a couple of words (hearing “front” instead of “right”), Simon conceded that the errors were understandable given the audio itself. The post highlights how easy it has become to test heavyweight, local AI models on Apple Silicon without complex environment setup.