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Digest.tube – skim YouTube videos like articles

Hacker News

Digest.tube – skim YouTube videos like articles

hi folks, I'd like to share yet another timestamped summary tool for youtube. I tried several existing extensions but couldn't find the UX that I wanted, so I built my own. it's a chrome extension that reads the transcript and maps out topics and key takeaways with timestamps. my usual flow is skimming the topics, hovering on the ones that sound interesting to see the takeaways, and if those look good too, clicking to jump straight there. demo (30s): https://www.youtube.com/watch?v=I2K94zZfD9E chrome web store: https://chromewebstore.google.com/detail/digesttube/idogajoe... - if a video has already been processed, anyone can view it for free, no signup needed either - supports videos up to ~10 hours (tested on this 8.5-hour podcast: https://www.youtube.com/watch?v=Kbk9BiPhm7o ) - the extension only activates on youtube.com. no background tracking, no browsing data some implementation notes for the technically curious: I use client-side transcript fetch since youtube throttles bots fetching transcripts. this means my api accepts user-submitted transcript text. to prevent abuse, I cache digests by transcript hash. another user only gets the cached result if their own client-fetched transcript produces the same hash. the transcript gets split into slightly overlapping chunks with some pre/post context window. each chunk goes to the LLM in parallel, so processing scales well even for very long videos. one challenge I'm still iterating on: topics that land right at a chunk boundary, starting just before one chunk ends and finishing just after the next begins. this sometimes leads to duplicate or fragmented topics across adjacent chunks. the overlap window helps but doesn't fully solve it. curious if anyone has tackled similar chunking problems and what approaches worked.

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, context · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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