Diffee VS Video2Any

Video2Any's answer:

Students and university learners recovering lecture slides.

Video2Any's answer:

It is built around fully local, browser-side processing. Your video file never gets uploaded to a server. Instead of using AI to invent new slides, it extracts the actual slides that appear in the video. The core detection engine is open source (video-slide-extractor), uses frame differencing to detect slide changes, and works without an account, prompts, or token usage.

Video2Any's answer:

Three main reasons:

1. Privacy by design: decoding, detection, and export happen in your browser; files never leave your device.
2. Fast processing: frames are downsampled to 160×90 and compared in blocks, so a 1-hour lecture can be scanned in roughly 40 seconds on a typical laptop.
3. Generous free tier: because processing runs locally, there is no server cost to pass on. Local file processing is free and requires no account.
4. Open-source core: the main engine is available as an MIT-licensed package, so users can inspect, self-host, or build on it.

Video2Any's answer:

It is for people who have a recording but not the original slides: students who recorded a lecture, researchers, corporate trainers, course creators, and anyone who needs to recover shared slides from Zoom, Teams, webinars, or screen recordings. It is especially useful for lecture recordings and screen shares where slides stay mostly still and only small elements move.

Video2Any's answer:

The project started because manually scrubbing through videos and taking screenshots of slides is painful. The creator also wanted to avoid the trend of adding an "AI agent" to everything. Recorded slides have a natural advantage: most of the frame is static, and slide changes create large block-level differences. That led to a zero-dependency frame-differencing detector, open-sourced as video-slide-extractor, with Video2Any built on top as a browser-based product.

Video2Any's answer:

The stack is intentionally lean:

1. Open-source detection engine: video-slide-extractor, a zero-dependency JavaScript package that runs in the browser or Node.
2. Browser-native video decoding: reads from a File or Blob, seeks by timestamp, and draws frames to canvas.
3. Block-level frame differencing: downsamples to 160×90, calculates mean absolute difference in 8×8 blocks, and flags a new slide when enough blocks change.
4. CLI tooling: an ffmpeg-based wrapper for generating slide images from files, with no network requests.