OpenAI-powered semantic search for the All-In Podcast

Hacker News

My favorite podcast is the All-In Pod, but search and discovery is tough for podcasts. So I built a semantic search index that lets you search across every episode of the pod with Google-level accuracy. It uses OpenAI's latest ada-2 embedding model and Pinecone to index the embeddings + k-NN search. Wrapped up in a Next.js + Vercel webapp. The project is designed to work with any YouTube channel / playlist. I'm just using All-In as a demo. Would love to hear feedback & suggestions. Open source here: https://github.com/transitive-bullshit/yt-semantic-search Note that the source is designed to work with any YouTube playlist / channel. I'm just using the All-In Podcast as a demo because it's my favorite.

Visit product View on Hacker NewsDec 30, 2022

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

95%95% 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

39%39% 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

9%9% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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