Generate content ideas from YouTube comments

Hacker News

Hey HN! I run a niche blog and YouTube channel with ~48k subscribers. Finding new ideas is hard, especially with recent disruptions to SEO. It's getting harder to count on traditional search data (mostly because things feel fuzzy atm), so I built Comment Pilgrim to generate keywords and analyze feedback from the comments section of any YouTube video. For anyone willing to give feedback, the first 5 reports you generate are free. I always recommend going to the top creators in your niche and using their best videos to let Comment Pilgrim find where the audience still has questions, concerns, and gaps in understanding. Watch the 2-minute demo here: https://youtu.be/bw4Oh6E4cVw Built on the MERN stack and hosted entirely on GCP since I'm most familiar with it. Would love feedback from those familiar with Semrush and Ahrefs or who have built large channels or blogs!

Visit product View on Hacker NewsMay 29, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: subscribers · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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