A new open-source tool, claude-real-video, enables Large Language Models (LLMs) to analyze video content locally. Unlike existing tools that rely on fixed frame sampling or cloud processing, this project extracts scene changes and deduplicates frames before sending them to models like Claude or ChatGPT. It operates entirely on the user's machine, ensuring privacy.

Traditional methods often miss crucial moments in fast-paced videos or oversample static scenes. claude-real-video addresses this by detecting actual scene transitions and using a sliding window for deduplication, presenting LLMs with more meaningful data. The tool also integrates Whisper for audio transcription, creating a comprehensive data package for AI analysis.

The project, released under the MIT license, provides Python installation and command-line usage. It generates a folder containing JPEG frames, a transcript, and a manifest file. This allows any LLM to 'watch' a video by processing these structured outputs, offering a more efficient and accurate understanding of visual content.

This approach bypasses the limitations of current LLM video integrations, which often upload data or sample inefficiently. The tool's focus on local processing and intelligent frame selection makes advanced video analysis accessible without privacy concerns.