MegaDSP an effects plugin for DAWs with 50 effects

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Hey HN, I am creating megaDSP, a huge DAW plugin for all platforms, with the goal of it being a free, open-source, and semi or fully autonomous github project that evolves around community feedback. It currently has more than 50 audio effects that can be combined together in effects chains, and also supports "snapshots" where you can seamlessly switch between different effects chains (for live use, like a fancy guitar pedalboard for example). I would really love if you have a moment to test it out, give thoughts, etc. There are many, many plugins out there in the world, but many of them are either DAW-specific or have annoying copy protections etc. Having one huge plugin with great DSP algorithms seems like it could be a benefit to the music/audio world. Thanks, -Kevin first pre-release: https://github.com/kevinlong206/megaDSP/releases/tag/v0.30.0...

Visit product View on Hacker NewsJul 29, 2026

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

89%89% 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.

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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% 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

12%12% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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