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Velty App –> Organize your YouTube subscriptions and videos

Hacker News

Velty App –> Organize your YouTube subscriptions and videos

hi everyone, I've been working on a web app ( https://velty.app ) that lets you organize your YouTube subscriptions and videos into folders and sub-folders. It helps you find what you want to watch more easily. The idea came up when I found it hard to organize my growing list of YouTube subscriptions and finding videos easily. The YouTube playlists system is limited — it lacks hierarchy and search functionality. I wanted an app to organizing everything into folders and sub-folders to easily find videos by their duration or the channel they're from. Also, provide the same functionality on mobile and destkop. I tried different methods like RSS readers, browser extensions, and bookmarks, but they all had drawbacks: - Browser extensions don't work on mobile. - With bookmarks, filtering and sorting are challenging. - RSS readers have limitations in organizing folders and sub-folders and lack filtering and sorting options such as duration, published date, and channel names. So, I decided to build my own solution to deal with these issues. I'm keen to hear your thoughts on the idea!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
45%45% 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
37%37% 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: subscription · Missing: arr, mrr, revenue
20%20% 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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