I made the first free ad blocker for podcasts

Hacker News

Always been annoyed by podcast ads. Searched and found a few apps that can detect and skip them, but they were all paid. So I made a free one. To my knowledge this is the first podcast app with free ad block. iOS (26+) only for now. If the app becomes popular I'll def think of doing an Android version as well. The app doesn't require a user account. I may open-source it at some point; still thinking about it. If anyone's suspicious about how it's able to be free, i.e. doesn't it cost money to run, are you selling my data (I'm not) etc. I'm happy to explain in the comments. From a technical perspective, the innovation here is ad detection despite dynamic/programmatic ads, which is the norm in most popular podcasts these days. There is ofc a naive way to do it, but it's expensive and high latency. There is also an even simpler way but it's illegal lol (re-host and serve just one version of the episode). I was able to figure out a way to do it that's cheap, low latency, and legal: Shazam-style audio fingerprinting, except used for matching audio accurately, not identification. From today on, you never have to listen to a podcast ad again. tearsofjoy.jpeg Cheers.

Visit product View on Hacker NewsMay 21, 2026

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

77%77% 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: ide, io · Missing: https docs, excited, just released

67%67% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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