101 Interest Score
29 Discussions
0.29 Engagement
Jan 2024 Launched

Meet Very Disco — the “IMDb for podcasts”. Discover, listen to, and save every book, movie, TV show, and person ever mentioned in a podcast.

What the Community Said

Hi PH community, I'm Desi, and I’m happy to introduce you to Very Disco — “Everything ever mentioned in your favorite podcasts” Very Disco is the IMDb of podcast content. We identify every topic in every podcast episode: every book, person, TV show, movie, and more. So next time you’re listening to a podcast episode, and an interesting book is mentioned, you can: 1) see which book it is, 2) see every other time the podcast has mentioned it, and 3) find every *other* podcast talking about it too!

— [REDACTED]

Podcast enthusiasts unite! Definitely going to use this to skip to the good parts of my favorite pods

— [REDACTED]

Awesome tool! I'm always scrambling to take notes of topics, frameworks, people, products etc etc mentioned. This is fantastic!

— [REDACTED]

Congrats on the launch, super cool. Just finding book recommendations from a particular guest is super valuable

— [REDACTED]

This is awesome, it's like a Mention / search engine for podcast content. Interested to see where you take this! I'd happily pay for advanced search features, I'm guessing a ton of companies would also be interested in knowing when they or their domain get mentioned on a podcast. Congrats on the launch!

— [REDACTED]

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Frequently Asked Questions

A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.

Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.

Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.

The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.

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