I built a free video auto-captions generator app with offline AI

Hacker News

A while back, I got stuck in subtitle hell. All I wanted was a simple way to auto-generate captions for my videos—no paywalls, no upload limits, no “surprise, now you need a subscription” popups. As a broke small creator and podcaster, I wasn’t about to drop a pile of cash on tools that watermark my stuff and send my files who-knows-where. So I built my own. I made it free, offline-first and available for Mac (Apple Silicon). Just drag in your video, and it spits out clean, accurate subtitles using the local OpenAI’s Whisper model you choose. What’s the deal? - Animated subtitles that don’t look like they’re from the 90s - Whisper does the heavy lifting (so the subtitles actually make sense) - Runs fully offline—your videos never leave your computer - Free forever—no subscriptions, no "pro" upsell, no watermarks - Fast, local processing (great for long videos or bad Wi-Fi) I made this because I needed it, but I’m betting I’m not the only one tired of subtitle headaches. Hope this can help! Danilo

Visit product View on Hacker NewsJul 24, 2025

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

81%81% 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: mac, model, apple · Missing: agents, macos, agent

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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 · Strong signals: subscription · Missing: arr, mrr, revenue

25%25% 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

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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