AI Podcast Transcription with Auto-Generated Chapters and Show Notes

Hacker News

I built an AI tool that converts podcasts to text with automatic chapter detection, multi-level summaries, and highlight extraction. Unlike basic transcription services, it generates production-ready Show Notes in one click. Technical highlights: • 99%+ accuracy using Whisper with optimized chunking for long audio • Speaker diarization without training data • AI chapter detection using topic boundary analysis • Exports to Markdown/TXT/JSON with structured metadata Free tier: 5-minute preview Tech stack: Next.js, Whisper, R2 storage Try it: https://harku.io/tools/podcast-transcription Built this after spending 30-60 minutes manually creating Show Notes for each episode. Would love feedback from fellow podcasters and developers.

Visit product View on Hacker NewsOct 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, notes · Missing: mac, agents, macos

64%64% 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

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

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

40%40% 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

30%30% 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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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