Flect AI – The Podcast Search Tool I Wish Existed

Hacker News

Hey HN! Checkout Flect, a modern AI powered search engine for YouTube podcasts. Describe the content not the keywords. Flect lets you describe a moment in a podcast and it returns a list of relevant YouTube podcast clips with links to the exact timestamp in the episode. So far I have the top 40 podcasts, 16k episodes, 30k+ hours of content, all searchable by context. Lex Fridman (3+ years ago) - "one of the things that sucks with podcasts is it's hard to find stuff..." https://flectai.com/link/?id=677d98e4bb1e4ace8a95598df6cefbb... Background: I love podcasts but I can never find the clips I’m looking for. Podcasts are exploding in popularity but theres no good search tools out there to find what I'm looking for. I feel like I'm in the pre-search engine age of the internet again but for podcasts. I tried a ton of tools out there but was unable to find anything that worked well. All I could find was naive keyword title searches or AI transcript summarization apps. I want to type in the context of what I remember the hosts talking about and I want it to return a list of relevant clips for me to choose from. Sometimes I don’t know the exact keywords, episode, or even podcast itself. I wanted it to link me to the exact clips I’m looking for and allow me to save or share with friends. Why doesnt this already exist? We deserve a better way to search podcast content. I'm aware this is a common hobby project idea but nobody seems to have actually built an app that works well yet (as far as I could find, please share if it exists!). YouTube seems to be the most popular way to listen/watch podcasts these days plus it's accessible and easy to share links, so I started with that. You can try out a few suggested searches on the landing page or write your own. Flect currently has capabilities to search with arbitrary text, filter for keywords or podcasts, save clips, share, etc. I’m building this on the side so its built to be extremely lean and efficient. My tech stack: YouTube transcripts, AWS lambda, dbt and Athena for processing data, OpenAI embeddings small, MyScale for vector DB, supabase for auth and backend, nextjs and FastAPI. I’m working on the AI chat feature, taking search to the next level, allowing you to chat with a podcast, summarize, find relevant content, etc. Would love to hear your thoughts and feedback! Thoughts? Suggestions? I'm all ears!

Visit product View on Hacker NewsNov 26, 2024

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

91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% 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, ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

36%36% 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

15%15% 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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