superwhisper – AI powered offline voice to text for macOS

Hacker News

Hey HN, I built superwhisper out of frustration with the native dictation capabilities of macOS. Inaccurate, required manual punctuation, didnt activate in some contexts or would have audio capture issues. I wanted a replacement that worked offline, had cross language support, was configurable and worked in any application. Under the hood the app is using whisper.cpp, which runs really well on the Apple Silicon chips. You can use the base and standard size models for free, larger models sizes and languages other than english are paid. Let me know what you think! For context, I launched this just one month ago and have been rapidly adding features and making fixes. If you want to follow along with development, I post release info on twitter ( https://x.com/superwhisperapp ) or you can subscribe to emails via the form on the website (very bottom).

Visit product View on Hacker NewsAug 21, 2023

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Actual performance

43points

48comments

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

92%92% 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

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% 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: month · Missing: mobile apps, ios, personal

41%41% 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

41%41% 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

19%19% 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

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

Correct prediction on native model

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