Simon Willison announces the release of ChatGPT Images 2.5, noting that OpenAI’s image generation models have processed over 3 billion images across ChatGPT Images and the GPT‑Image API. The new version improves instruction‑following across multiple turns, speeds up responses, and better preserves subjects from reference photos. Two new API model IDs—gpt‑image‑2.5‑sunburst and gpt‑image‑2.5‑flare—are available, with Sunburst recommended for precision editing and Flare for fast, high‑quality everyday generation. Willison has updated his openai_image.py CLI tool to accept reference images, demonstrating its use with a raccoon scientist prompt.
Simon Willison announces the release of the llm-openai-decisions 0.1a0 plugin, which interfaces with OpenAI’s new Jev-style Decisions API. The plugin, inspired by llm-typesafe, supports image and text input and offers the same three question types (yes/no, choices, scores) as Jev, with pricing at 10¢ per million input tokens. Installation is simple via `llm install llm-openai-decisions`, and an example query demonstrates image-based evaluation.
Release: llm-gemini 0. 33 It's been a while since the last llm-gemini release.
Quick Study is a tool that lets users open a photo and quickly generate a painted version of it using a canvas element and a simulated paintbrush. The process relies on Sobel edge detection and requires no external libraries beyond Google Fonts. It builds on previous experiments that used Claude Fable to create watercolor illustrations with P5.js.
The article describes a test of the new Python 3.15 profiler using GPT-6 Astra Extra High in Codex to run it against a web-edit application from PyPI. It includes a flame graph and source heatmap generated from this profiling effort. The author emphasizes the use of these visual tools to analyze the performance of the web-edit app.
The release of llm 0.37 introduces numerous new features and fixes accumulated over two months, including new GPT-6.1 models, an openai endpoint add command, and a cheaper default model (gpt-6-luna). It enhances embedding support for varied content types and incorporates many AI‑assisted pull requests from multiple authors. The update also highlights the project’s efficient handling of AI‑generated contributions, aided by triage tools like Opus 5.5 and GPT-6 Astra.
Simon Willison demonstrates how to use Blender with coding agents on macOS by installing the full Mac application from blender.org and issuing prompts to ChatGPT Codex. He shows a step-by-step example where the agent renders a scene of a pelican riding a bicycle, then adds background flair and improves the image. The resulting image is generated via Blender's Python API.
Python 3.15 introduces several new features, notably explicit lazy imports from PEP 810 that delay module loading until first use, and unpacking syntax in comprehensions (PEP 798) that simplifies flattening lists, sets, and dictionaries. The release also updates the profiling tools, renaming the tracing profiler to profiling.tracing and adding a new sampling profiler, profiling.sampling. These changes aim to improve performance and developer ergonomics in Python code.
Simon Willison introduces GPT‑6 Astra, a new model that offers improved attention to detail, better prompt comprehension, and the ability to generate more sophisticated outputs. The model excels at creating 3D renderings, producing detailed scenes such as gardens, shipyards, animals, cityscapes, and even Dyson spheres. Willison highlights its whimsical creativity, noting examples like a pelican wearing a red neckerchief riding a bicycle.
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.
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.
Simon Willison tested GPT‑6 Astra by generating SVG pelicans riding bicycles at various reasoning levels and compared the results to GPT‑5.6 Sol, Terra, and Luna. The Astra pelicans consistently outperformed the other models, especially at low and xhigh reasoning levels, and even the Astra max version produced high‑quality images. Astra also used fewer tokens and was roughly twice as expensive as Sol, yet its low‑level output was cheaper and superior to any Sol model.
The article announces the release of llm-meta-ai 0.2, featuring Meta AI’s new Muse Wonder and Muse Wonder Full models, described as the most capable models yet. These models are suggested to be Meta’s first Fable‑class models, with Artificial Analysis scoring Muse Wonder (Max) equivalent to GPT‑6.1 Sol. The author updated the llm-meta-ai plugin to support the new model and provides pelican‑drawing benchmarks across six reasoning levels, noting significant improvements over previous releases.