ReadTube – Turn YouTube subscriptions into a personal Substack

Hacker News

I built ReadTube so that I can read my YouTube subscriptions. YouTube has lots of high quality content. However, videos can be difficult to consume efficiently, especially those that are long and about series topics (e.g. general relativity, quantum physics). So I created this app to solve this need. There are already many existing YouTube AI transcription websites. However, ReadTube is one step further in that it periodically fetches the updates from the channels I subscribe to so I don't need to paste in the video URL each time. Also it can generate two different versions of summary: one short and one long, as well as a full length article. I can pick which length to consume according to the type of videos. Some other features: playlist import, translation, notes. I use the Transcript API to get transcripts for YouTube videos, and JustOneAPI for Bilibili videos. These services rely on the video's native transcripts. So this app does have this one limitation that if a video has no native transcript, it won't work. The app is built with the "standard" stack: Next.js on Vercel, Tailwind CSS, Postgres on Neon / Prisma, Clerk for auth, Gemini for summary and article generation. I will probably open source it after I clean up the codebase more.

Visit product View on Hacker NewsApr 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: gemini, notes, code · Missing: mac, agents, macos

89%89% 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 · Strong signals: created, efficiently, gemini · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

43%43% 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

25%25% 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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