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Developer Tests Three AI Tools by Feeding Them 200 YouTube Video Transcripts

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A developer conducted an experiment to determine whether large language models could effectively process an entire YouTube channel's worth of content, using 200 video transcripts totalling roughly 550,000 tokens. Three setups were tested: a brute-force single context window, Google's NotebookLM, and Claude Projects with retrieval. The brute-force approach proved limited, fitting only around 50–70 videos within a 200k context window, making it impractical for larger channels. NotebookLM handled all 200 videos by merging transcripts into grouped files to stay within its 50-source limit, while Claude Projects accepted the full dataset without such workarounds but occasionally missed details on broad queries. All three tools completed the task, though each showed distinct trade-offs in setup effort, channel size capacity, and citation accuracy.

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