Podyssey, a smart search engine for podcasts

Hacker News

Podyssey is an AI-powered podcast discovery platform that redefines how users find and consume podcasts. Instead of relying on metadata or broad categories like “comedy” or “society & culture” for search and recommendation, it segments episodes into bite-sized clips (30 seconds to 5 minutes), categorizes them using fine-tuned BERTopic, generates titles for each clip with a fine-tuned Mistral 7B (v0.2) model, and then runs semantic search on top of clip titles. This enables precise, content-focused search and personalized recommendations. Features include: - Granular Search: Semantic search matches queries to specific clips, not just episodes. - Tailored Recommendations: Based on individual listening patterns. - Ease of Sharing: Share specific podcast moments with ease. Future plans include speaker recognition, multilingual tools, and generating news articles from transcripts. Podyssey is leveraging AI to make podcast discovery as intuitive as Google made web search.

Visit product View on Hacker NewsDec 19, 2024

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

84%84% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, users · Missing: plus, reviews, host

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% 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, google, users · Missing: mobile apps, ios, entrepreneurs

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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 · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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