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Podverse – AI Superpowers for Podcasters

Hacker News

Podverse – AI Superpowers for Podcasters

Hey HN, I’m excited to show off a project I have been building the last few months called Podverse — it automatically generates transcripts, summaries, and a chatbot from a podcast’s RSS feed, and makes it easy to embed AI features onto any website. You can check it out at https://podverse.ai . See it in action on an example podcast here: https://www.podverse.ai/podcast/omnibus I’m a former engineer at Google and Apple, former CS prof at Harvard, and was a co-founder at Fixie.ai. I truly feel that LLMs are a revolution in our field, and I wanted to bring something to life that bridged between two deep interests of mine — AI and podcasts. I’ve been listening a ton of podcasts lately, but it's frustrating to find relevant episodes, or even content that I had listened to previously. I also know that podcasters want to take advantage of the latest AI tech to connect with and expand their audience, but they're not usually equipped to bring all the pieces together. I wanted to build a complete, end-to-end solution for podcasters that is easy to use. All you have to do is provide your RSS feed URL, and Podverse does the rest — automatically ingesting new episodes as you release them, generating transcripts and summaries, identifying speakers, and populating a custom chatbot that knows about all of the content of your podcast. I built the site from scratch over the last few months (in my copious spare time!). It’s built entirely using Next.js deployed on Vercel — no backend servers beyond Vercel’s server components and API handlers. For running background tasks, like podcast ingestion and transcription, I used Inngest, which fills a big gap in the Next.js stack. Deepgram is used for ASR, which is fast, affordable, and very high quality. Supabase handles the storage, including the vector database (using pgvector). Auth is done with Clerk, which was super easy to integrate. Happy to answer any questions about the stack. I’d love to get your feedback, and especially hear from podcasters to learn more about what features you’d like to see. Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, new · Missing: mac, agents, macos
97%97% 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: including · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
64%64% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

Incorrect prediction on native model

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