AOO – C++ library for real-time audio streaming and messaging

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AOO is a lightweight and flexible peer-to-peer audio streaming and messaging solution. It allows to send audio and messages in real-time and on demand between network endpoints - not only in local networks, but also over the public internet! The C/C++ library can be easily embedded in host applications or plugins. It even runs on embedded devices, such as the ESP32. In addition, the project contains a Pure Data external and a SuperCollider extension. The following article provides a high level introduction and demonstrates some use cases: https://www.soundingfuture.com/en/article/aoo-low-latency-pe... AOO has been in development since 2020 and it has been used in several art projects since. Now it has finally reached a stage where I feel comfortable presenting it to a larger public. This is still a pre-release and I want to get more feedback before making a final release, so tell me what you think!

Visit product View on Hacker NewsOct 12, 2024

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

75%75% 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 · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

28%28% 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

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: 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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