Simon Willison on setting custom model prices in AgentsView
Simon Willison documents a workflow for configuring custom pricing for models within AgentsView, a tool for tracking AI agent costs. The post addresses a practical need for practitioners who use models not yet priced in the tool's default database. It is a short how-to from a tier-2 commentary source with minimal body content available.
Related guides (3)
Related events (8)
Simon Willison on Microsoft's new MAI models
Simon Willison covers Microsoft's release of new MAI (Microsoft AI) models. The post is commentary from a tier-2 source on a Microsoft model announcement, likely summarizing capabilities and context. Microsoft's MAI model line represents the company's continued push to develop proprietary frontier models alongside its OpenAI partnership.
sqlite AGENTS.md
Simon Willison publishes an AGENTS.md file for the SQLite project, a convention for providing AI coding agents with project-specific instructions and context. This follows the emerging practice of including agent-readable documentation files in codebases to guide LLM-based tools. The post reflects the growing ecosystem of conventions around agentic coding workflows.
Anthropic Releases Claude Sonnet 4.5: Top Coding and Computer-Use Model with Agent SDK
Anthropic has released Claude Sonnet 4.5, claiming it is the best coding model and strongest model for building complex agents, with a 61.4% score on OSWorld (up from 42.2% for Sonnet 4) and state-of-the-art performance on SWE-bench Verified. The release is accompanied by major product upgrades including checkpoints in Claude Code, a native VS Code extension, a Claude Agent SDK giving developers access to the same infrastructure powering Claude Code, and new context editing and memory tools in the Claude API. Pricing is unchanged from Sonnet 4 at $3/$15 per million input/output tokens. Early enterprise customers including Cursor, GitHub Copilot, Devin, Canva, and Figma report significant gains in coding, agentic, and long-context tasks.
Datasette Agent
Simon Willison describes a Datasette Agent, an AI agent built on top of the Datasette data exploration tool. The post appears to demonstrate an agent capable of querying and reasoning over SQLite databases via natural language. This represents a practical deployment of LLM-powered tooling for data analysis workflows.
AINews: Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo
A Latent Space AINews digest covers open model developments, the emerging distinction between model labs and agent labs, and a featured essay by Sarah Guo on what capabilities remain untrainable. The piece appears to be a reflective commentary day with a focus on strategic framing of the AI ecosystem. The 'model labs vs agent labs' framing and 'what's untrainable' angle suggest substantive industry analysis worth indexing.
Real AI Agents and Real Work
A commentary piece from One Useful Thing examining the practical deployment of AI agents in real work contexts, framing the tension between human-centered work and AI-generated productivity outputs. The piece appears to analyze how autonomous AI agents are changing knowledge work workflows. Published by a Tier 2 source known for applied AI analysis aimed at practitioners and researchers.
Quoting Armin Ronacher
Simon Willison quotes Armin Ronacher in a brief commentary post. The body content is empty, so the specific substance of the quote is unavailable, but given the source and subjects involved—both prominent figures in Python/developer tooling communities who have written extensively about AI coding tools and agents—the post likely touches on AI-assisted development or related tooling themes.
Simon Willison releases datasette-agent 0.3a0
Simon Willison published datasette-agent 0.3a0, an alpha release of an agent tooling package for Datasette. The release appears to be a tooling update in the agent/data exploration space. Datasette is an open-source tool for exploring and publishing data, and this agent layer extends it with AI-driven capabilities.