QuCast – Everyone can generate AI podcasts

Hacker News

Hey! I'm building a podcast app that allows the listeners to generate their own podcasts. I listen to a lot of Huberman. While I'm driving, walking, working out, or anywhere else I want to listen to something. But I've run out of Huberman content & find myself wanting more. With life-like LLMs & TTS becoming ever cheaper, I thought why don't I just generate my own? In QuCast you can enter a topic or URL to base a podcast episode on. You can then chat to an outline to refine it in whatever way you want, then generate the speech. I love the podcast format, & find popping essays, articles, or research papers in it work fairly well. I'm not sure if something like this already exists? There are definitely podcast generators for creators but why not for the listeners themselves? Maybe it was too expensive before, but my math works out to about 2 cents for a 10 minute podcast (more if you want a better voice like ElevenLabs). Anyways, let me know what y'all think! To generate your own you'll need to download the app. And yes, this was mainly hacked together over a week using Cursor (It built that nice auto scrolling transcript view in the app all by itself?!). Mobile app + Website + Backend. Podcasts/outlines are written by Claude. Voice is from Google (free experimental).

Visit product View on Hacker NewsSep 15, 2024

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2points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, google · Missing: mac, agents, macos

98%98% 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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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