Hello folks,

I'm pleased to announce the release of AUTOSEL, a complete rewrite of the stable kernel patch selection tool that Julia Lawall and I presented back in 2018[1]. Unlike the previous version that relied on word statistics and older neural network techniques, AUTOSEL leverages modern large language models and embedding technology to provide significantly more accurate recommendations.

## What is AUTOSEL?

AUTOSEL automatically analyzes Linux kernel commits to determine whether they should be backported to stable kernel trees. It examines commit messages, code changes, and historical backporting patterns to make intelligent recommendations.

This is a complete rewrite of the original tool[1], with several major improvements:

1. Uses large language models (Claude, OpenAI, NVIDIA models) for semantic understanding 2. Implements embeddings-based similar commit retrieval for better context 3. Provides detailed explanations for each recommendation 4. Supports batch processing for efficient analysis of multiple commits

## Key Features

- Support for multiple LLM providers (Claude, OpenAI, NVIDIA) - Self-contained embeddings using Candle - Optional CUDA acceleration for faster analysis - Detailed explanations of backporting decisions - Extensive test coverage and validation

## Getting Started

``` git clone https://git.sr.ht/~sashal/autosel cd autosel cargo build --release ```

To analyze a specific commit: ``` ./target/release/autosel --kernel-repo ~/linux --models claude --commit <SHA> ```

For more information, see the README.md file in the repository.

[1] https://lwn.net/Articles/764647/