Simon Willison

Video compressor

Simon Willison created a video compressor tool that uses the WebAssembly build of FFMPEG to optimize a demo video of his Equal Earth animation recorded on his phone. He employed Claude Fable 5.1 in Claude Code for web to generate the tool, enabling him to publish the optimized video on his blog. The project showcases how modern web technologies can streamline video processing workflows.

Google AI Blog
Feb 23, 2024

VideoPrism: A foundational visual encoder for video understanding

Posted by Long Zhao, Senior Research Scientist, and Ting Liu, Senior Staff Software Engineer, Google Research An astounding number of videos are available on the Web, covering a variety of content from everyday moments people share to historical moments to scientific observations, each of which contains a unique record of the world. The right tools could help researchers analyze these videos, transforming how we understand the world around us.

By Google AI
Simon Willison
Sep 13

shot-scraper 1.12

Shot‑scraper 1.12 adds WebP support, allowing users to capture web page screenshots in WebP format with an optional quality setting. The new --quality flag controls compression, while omitting it produces lossless images. WebP screenshots are reported to be significantly smaller than JPEG or PNG equivalents.

Simon Willison
Sep 7

Mercator ↔ Equal Earth

Simon Willison created an animated transition tool that morphs a map from the Mercator projection to the Equal Earth projection using D3. The project was inspired by the recent UN vote on the Equal Earth map projection and was built with GPT‑6 Astra (medium) in ChatGPT Work. It showcases how modern AI and web technologies can visualize complex geospatial transformations.

Google AI Blog
Jan 31, 2024

MobileDiffusion: Rapid text-to-image generation on-device

Posted by Yang Zhao, Senior Software Engineer, and Tingbo Hou, Senior Staff Software Engineer, Core ML Text-to-image diffusion models have shown exceptional capabilities in generating high-quality images from text prompts. However, leading models feature billions of parameters and are consequently expensive to run, requiring powerful desktops or servers (e.

By Google AI
Simon Willison
Sep 2

llm 0.34

The release of llm 0.34 introduces a new feature that enhances log output by including response duration in both milliseconds and a human‑readable format. The short log view now contains a dedicated duration_ms field. Additionally, the update incorporates multiple bug fixes and a notable performance boost to llm logs, attributed to waveplate integration.

Simon Willison
Sep 2

llm-gemini 0.34

The release of llm-gemini 0.34 introduces the new Gemini 3.8‑Flash model, available in low, medium, and high thinking levels, and fixes an issue where async responses failed to record the resolved model version. The update also notes that Google has released Gemini 3.8‑Flash (and a restricted 3.8 Flash Cyber version) today, with example outputs (pelicans) demonstrating the model’s performance across the different thinking levels. The author highlights Gemini Flash’s speed, low cost, and competence in generating HTML, JavaScript, and Markdown‑SVG content, citing a 13‑second, 1.8‑cent example of an HTML output.

Simon Willison
Aug 16

Markdown SVG upgrades

I started building my markdown-svg-renderer tool in May , but I've since added enough features to it that it's worth talking about here again. It's evolved into my ideal tool for sharing Markdown transcripts that include SVG documents.

Simon Willison
Aug 24

llm-anthropic 0.27

The release of llm‑anthropic 0.27 updates the Anthropic plugin for LLM to be compatible with the newly released anthropic v1.0.0 Python library, which has switched from httpx to httpx2. This mirrors a similar change made by OpenAI in their v3.0.0 release two weeks prior. The update includes a migration guide and a pull request that ensures tests pass after upgrading to anthropic>=1.

arXiv AI
Sep 2

Can LLMs Design Video Coding Tools? A Case Study on Planar Mode

The paper investigates whether large language models (LLMs) can design video coding tools, focusing on the Planar mode used in video coding standards. Using a generation-and-evaluation loop, the LLM generates new Planar predictors, which are then tested in the Fraunhofer Versatile Video Encoder (VVenC) and the Enhanced Compression Model (ECM). Results show that the LLM-generated mode can outperform the conventional Planar mode, achieving a 0.18% bitrate saving with a 0.4% complexity increase, and that similar gains are possible when integrating the new predictor into ECM under low‑resolution settings.

By Yingwen Zhang, Meng Wang, Liqiang He, Shiqi Wang