Live Subtitles for Any App on Mac

Hacker News

I’m non-native English Speaker and i’m so tired of saying “Say it again” on online calls. Especially if it is Indian or Scottish Accent. And moreover if it is some really serious interview. That’s why i built Subtitly - Live Subtitles for any app on macOS. Few things about subtitly: - Subtitles are in Mac’s notch ⇒ you always look into the camera - They are really fast and reliable (2-3% Word Error Rate) - On-device AI ⇒ Offline & Private - Works in any app ⇒ Watch twitch streams with subs or calls in discord with subs. Here is the demo how it works: https://x.com/rustemphd/status/1828455907786625062

Visit product View on Hacker NewsAug 27, 2024

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Launch Intel predictions

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

77%77% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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