Pw-whip, a bridge between PipeWire and WHIP

Hacker News

WHIP (RFC 9725) is a standardised media ingress protocol: it serves to stream audio and video to a network server, and is supported by a number of media distribution systems. PipeWire is the currently fashionable multimedia framework for Linux, and almost all Linux applications are able to send audio to PipeWire, either natively or through PipeWire's emulation of the ALSA and PulseAudio APIs. Pw-whip is a bridge between PipeWire and WHIP: it enables every application that can speak to PipeWire to stream its audio to any compliant WHIP server.

Visit product View on Hacker NewsJun 25, 2026

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Actual performance

3points

Did not reach leaderboard

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

77%77% 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.

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

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

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: apis · Missing: mac, agents, macos

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

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

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

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

34%34% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

30%30% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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