Freestyle – Wispr Flow Alternative that respects privacy

Hacker News

Hey HN, I'm Matt. For the past couple of weeks, I've been hacking on Freestyle. It's an open source voice dictation app. I was an avid Wispr Flow user before but didn't like how it was cloud only. Every thought sent to their servers for transcription. I've been working on an alternative that lets you run models locally, like Whisper, Qwen, Sensevoice. That way thoughts never leave your device. Would really appreciate any feedback!!

Visit product View on Hacker NewsJun 23, 2026

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2points

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Launch Intel predictions

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

93%93% 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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · 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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