# The Underrated AI Tool That Lets Any LLM Watch Videos > Source: > Published: 2026-07-26 08:51:12+00:00 I spent this month testing the ways people actually get an LLM to "watch" a video. One of them got a 2,181-video stress test from a single user. Here is what held up. **1. Upload it to a hosted multimodal model (Gemini).** This is the shortest path and I want to be fair: for "tell me about this one clip," Gemini is genuinely good. You upload, you ask, you get an answer. The trade-offs are structural, not quality: your footage leaves your machine, results vary between runs, and you cannot hand the same evidence to a different model. When the answer looks wrong, there is nothing to check — you re-run and hope. **2. Run a describe-it-for-you pipeline (byjlw/video-analyzer, 1.5k stars).** A solid tool and an honest design: OpenCV pulls key frames, Whisper transcribes, a local vision model (Llama 3.2 11B) describes each frame, and you read the reconstructed description. The catch is that your LLM never sees the video — it sees another model's opinion of the video. Every frame the vision model misreads becomes a fact your LLM confidently repeats. You also pay for a second model in the loop. **3. Hand your LLM the evidence directly (claude-real-video).** This is the underrated one, and yes, it is mine — judge the argument, not the author. `crv` turns a video into what an LLM can actually read: scene-aware keyframes with true source timestamps, a timestamped transcript with optional speaker labels, and a manifest that tells the agent how to read the folder. No middleman model. Claude (or GPT, or a local model) looks at the actual frames and cites `frame_012 @ 00:03:41` . Everything runs locally. A user ran crv over his entire photo library — 2,181 videos in four days — and mailed me the failure list, worst first. Two bugs from that report shaped my view of this whole category: `frames.json` ; the same user re-verified all 60 final frames of a 22-minute lecture against the source, 60/60.Neither fix came from my test suite. They came from one user with a big real library. That is the honest state of this category: the hard bugs live where nobody's demo goes. ``` pip install "claude-real-video[fast]" npx skills add HUANGCHIHHUNGLeo/claude-real-video ``` MIT, runs on your machine, source: [https://github.com/HUANGCHIHHUNGLeo/claude-real-video](https://github.com/HUANGCHIHHUNGLeo/claude-real-video)