Simon Willison discusses using Claude Fable to assist in the development of sqlite-utils 4.0rc2. The AI helped identify critical bugs, specifically a transaction error in delete_where that could cause data loss, and facilitated significant code changes across dozens of files. Willison also employed GPT-5.5 to review the AI's work, discovering that cross-model verification is a highly effective technique for catching edge cases. The post includes a cost analysis of the AI development session and details the significant changes to the library's transaction model and error handling.
- Using coding agents for bug detection and large-scale refactoring
- The effectiveness of cross-model review using Claude and GPT
- Key breaking changes in sqlite-utils 4.0rc2 regarding transactions and error handling
- Cost breakdown of agentic AI development sessions
- Using coding agents for bug detection and large-scale refactoring
- The effectiveness of cross-model review using Claude and GPT
- Key breaking changes in sqlite-utils 4.0rc2 regarding transactions and error handling
- Cost breakdown of agentic AI development sessions
This article details how to build a 100% local MCP (Model Context Protocol) client using LlamaIndex, Ollama, and LightningAI. It provides a code walkthrough and explanation of the process, including setting up an SQLite MCP server and a locally served LLM.
Python virtual filesystem for SQLite to read from and write to S3.
