Simon Willison — 2026-06-22#
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Simon’s success in porting a PyTorch machine learning model to a browser-based WebGPU application entirely through “vibe coding” highlights a fascinating shift in developer workflows. It demonstrates how autonomous agents like Claude Code can now bridge significant gaps in domain knowledge, allowing developers to orchestrate the deployment of complex client-side AI tools while actively writing code for entirely different primary projects.
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Porting the Moebius 0.2B image inpainting model to run in the browser with Claude Code · Source Simon successfully ported the Moebius 0.2B lightweight image inpainting framework to run locally in the browser, relying purely on “vibe coding” with Claude Code. While waiting for Codex Desktop to complete mid-sized refactors for a new Datasette table UI, he instructed Claude in a terminal window to convert the original PyTorch model to ONNX, publish the 1.24GB converted weights to Hugging Face, and build a user interface hosted on GitHub Pages. To prevent the application from downloading the massive 1.3GB model on every single page load, he pointed a Claude subagent at a Whisper Web demo to successfully implement browser caching via the CacheStorage API. The core takeaway is the impressive capability of Opus 4.8 to act as a full-stack ML engineer—handling format conversion, model deployment, and front-end development—proving that heavy client-side AI is feasible today if users tolerate the initial download. After completing the project without writing a single line of code himself, Simon used Claude.ai to study his newly generated repository and gain a deeper technical understanding of how ONNX files bundle computation graphs and model weights together.