Podmob – AI-Powered, Human Perfected Podcast Player

Hacker News

Podmob is a new podcast player that enriches your podcast experience with AI and social capabilities. Key Features: AI Summaries and Insights: Podmob provides AI-generated summaries, highlights, and key quotes for any episode, making it easier than ever to catch up on your favorite shows or explore new ones. You can also start playing the episode right from any highlight or quote. See an example summary the of the latest Huberman Lab episode: https://www.podmob.com/pod/1545953110/ep/46513a44-89bd-4619-... PodmobAI Chatbot: Dive deeper into any episode with our interactive AI chatbot. Ask questions, get explanations, or discuss complex topics right within the app. Personal Highlights and Bookmarks: Discover a moment worth revisiting? Save personal highlights of any portion of an episode to revisit later. Social Discovery and Sharing: Follow friends, exchange favorite moments, and discuss intriguing episodes. Podmob believes in the power of AI to transform listening and the importance of human connection in making those experiences meaningful. Download from the app store https://apps.apple.com/us/app/podmob/id6499504330 and join us in redefining the podcast experience! (Android coming soon). What features would you love to see in your ideal podcast app?

Visit product View on Hacker NewsJun 10, 2024

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

67%67% 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

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

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% 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 · Strong signals: active · Missing: arr, mrr, revenue

11%11% 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

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

Correct prediction on native model

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