Simon Willison

commit-rewriter 0.1

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Simon Willison released commit-rewriter 0.1, a web app that lets users edit commit messages for a repository. The tool is useful for cleaning up commits that contain internal references or unwanted code before publication. After editing, it creates a timestamped branch to preserve the original state and rewrites the selected commits.

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Simon Willison
Aug 9

GitHub Models is now retired

GitHub Models is now retired I missed this news until today, when the GitHub Actions run for my simonw/research repository failed with this error message: GitHub Models is temporarily unavailable as part of a scheduled retirement brownout. That message is already stale, because the retirement has been completed.

Simon Willison
Aug 22

Quoting Linus Torvalds

The article recounts a challenging debug session that was significantly aided by an AI assistant. Despite the AI initially claiming the problem was unsolvable and suggesting a report be written instead, it persisted, adding debug code and analyzing it as the author pushed forward. Ultimately, the author credits the AI with writing the commit message for the fix.

Simon Willison
10h ago

llm 0.36

The release of llm 0.36 introduces new OpenAI models gpt-6-sol and gpt-6-luna, and adds support for model plugins to declare that they do not support conversations via supports_conversation = False. When such models receive assistant or tool history, llm raises a ConversationNotSupported error and the chat interface rejects them before starting a session. Additional changes include wrapping reasoning traces in Markdown output with <details> tags and bug fixes from five contributors.

arXiv AI
Sep 2

Don't Let the Model Write the YAML: Deterministic, Minimal-Diff GitOps Remediation from LLM-Proposed Field Changes

The paper investigates how large language models (LLMs) can propose changes to Kubernetes configuration files in a GitOps workflow. It shows that having the model directly generate edited files or diffs is unsafe for unattended automation, as current text‑generation strategies either fail to apply patches or silently misapply them. The authors propose a deterministic, minimal‑diff approach where the model emits only a structured intent for a field change, and a pipeline locates and replaces the exact character span in the raw YAML, preserving formatting and comments while guaranteeing correctness and O(1) generation cost.

By Pruthvi Davineni