Explores how to use the llm CLI tool within a script's shebang line by utilizing the env -S pattern. This approach allows plain text files to become executable via large language models, treating file contents as prompts. The article covers using flags like -f for fragments and -x for code extraction, integrating tools such as llm_time, and leveraging YAML templates with parameters or embedded Python tool functions to create highly functional scripts.
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
Simon Willison discusses why requesting HTML rather than Markdown as an LLM output format can significantly enhance technical explanations. While token constraints previously favored Markdown, modern models benefit from the ability of HTML to incorporate SVG diagrams, interactive widgets, and improved navigation. The article provides prompt examples for reviewing pull requests via HTML artifacts and showcases a GPT-5.5 generated explanation of a Linux security exploit that uses CSS and JavaScript to create a rich documentation experience.
An exploration of the new Qwen3.6-27B open weight model, which claims flagship-level agentic coding performance that surpasses previous larger MoE models while being significantly smaller in size. The author tests a quantized version using llama-server and demonstrates its impressive ability to generate complex SVG graphics locally.
Key points:
- Qwen3.6-27B outperforms the older Qwen3.5-397B-A17B on coding benchmarks.
- Dramatic reduction in model size from 807GB to approximately 55.6GB for the base version.
- Successful local execution using a 16.8GB quantized GGUF version via llama.cpp.
- High-quality SVG generation capabilities for complex prompts like a pelican riding a bicycle.
Key points:
- Qwen3.6-27B outperforms the older Qwen3.5-397B-A17B on coding benchmarks.
- Dramatic reduction in model size from 807GB to approximately 55.6GB for the base version.
- Successful local execution using a 16.8GB quantized GGUF version via llama.cpp.
- High-quality SVG generation capabilities for complex prompts like a pelican riding a bicycle.
Simon Willison tests OpenAI's newly released ChatGPT Images 2.0 model using a complex Where's Waldo style prompt involving a raccoon holding a ham radio. By comparing results against previous versions and competitors like Google's Nano Banana, the article evaluates the model's ability to handle high-detail illustrations and specific text elements.
Django co-creator Simon Willison predicts the emergence of a "dark factory" era in software development, where AI-driven automation handles the entire coding process without human intervention. Drawing an analogy from automated manufacturing, Willison suggests that if machines can operate reliably without human oversight, there is no need for "lights on" monitoring. As AI tools increasingly take over complex programming tasks, the role of human developers may shift from writing code to merely providing creative direction. This evolution raises significant questions about the future of the global workforce and the potential for widespread displacement in the tech industry.
Google DeepMind has released four new open-source, vision-capable LLMs under the Apache 2.0 license – Gemma 4, with sizes ranging from 2B to 31B parameters, and a 26B-A4B Mixture-of-Experts model. The models are notable for their intelligence-per-parameter ratio, with the smaller models (E2B and E4B) utilizing Per-Layer Embeddings to maximize efficiency.
The models support both vision and audio input, although audio functionality is not yet fully implemented in tools like LM Studio or Ollama. Testing with LM Studio showed varying results, with the 31B model experiencing output issues. The author also experimented with the models through the llm-gemini API, generating SVG images of a pelican riding a bicycle to assess their visual capabilities.
The models support both vision and audio input, although audio functionality is not yet fully implemented in tools like LM Studio or Ollama. Testing with LM Studio showed varying results, with the 31B model experiencing output issues. The author also experimented with the models through the llm-gemini API, generating SVG images of a pelican riding a bicycle to assess their visual capabilities.
Simon Willison details creating a custom macOS presentation app, "Present," in just 45 minutes using Swift and SwiftUI. Frustrated with the risk of browser crashes when presenting a series of web pages, he built an app that displays URLs as slides, offering features like full-screen mode, keyboard navigation, and automatic URL saving. He even added remote control functionality via a web server and Tailscale.
The project highlights the power of AI-assisted coding and expands his skillset, demonstrating how experienced software engineers can quickly learn new languages and tools to solve personal problems. The resulting app is a simple, effective solution tailored to his specific needs.
The project highlights the power of AI-assisted coding and expands his skillset, demonstrating how experienced software engineers can quickly learn new languages and tools to solve personal problems. The resulting app is a simple, effective solution tailored to his specific needs.
Simon Willison explores "vibe coding" - building macOS apps with SwiftUI using large language models like Claude Opus 4.6 and GPT-5.4, without extensive coding knowledge. He successfully created two apps, Bandwidther (network bandwidth monitor) and Gpuer (GPU usage monitor), demonstrating the potential of this approach. The process involved minimal prompting and iterative development, leveraging the LLMs' capabilities for both code generation and feature suggestions.
While acknowledging the need for caution regarding the apps' accuracy, Willison highlights the efficiency and accessibility of building macOS applications in this manner.
While acknowledging the need for caution regarding the apps' accuracy, Willison highlights the efficiency and accessibility of building macOS applications in this manner.
Starlette 1.0 has been released, and Simon Willison explores its new features by leveraging Claude’s skill‑building capabilities. He demonstrates how Claude can clone the Starlette repository, generate a comprehensive skill document with code examples, and even create a fully functional task‑management app complete with database, API endpoints, and Jinja2 templates—all generated and tested by Claude itself. The article highlights the practical benefits of integrating an LLM as a coding agent, showcases the new lifespan mechanism, and reflects on the growing popularity of Starlette as the foundation of FastAPI.
Create executable demo documents that show and prove an agent's work. Showboat helps agents build markdown documents that mix commentary, executable code blocks, and captured output. These documents serve as both readable documentation and reproducible proof of work. A verifier can re-execute all code blocks and confirm the outputs still match.
