BlipCut – AI Tool for Fast Video Translation and Localization

Hacker News

Hi HN, I’ve been working on BlipCut, an AI-powered video localization tool that helps creators and businesses translate and adapt video content quickly and at scale. The problem we’re tackling is that traditional video localization is slow, expensive, and manual-heavy. It often requires separate tools for subtitles, dubbing, transcription, and editing. With BlipCut, we’re trying to make the whole workflow simpler and faster in one platform. What BlipCut does: Video & Audio Translation: Translate into 130+ languages instantly Subtitles: Auto-generate and translate captions, then customize them Voice Cloning & AI Voiceover: Reproduce speakers' voices (29 languages) or use 1300+ AI voices in 90+ languages Lip Sync: Sync translated audio with speakers' lip movements for natural dubbing Batch Translation: Process multiple videos at once AI Transcription & Clip Maker: Transcribe videos, create short clips with auto titles/summaries, and translate them too Why it matters: Speed: Up to 10x faster than traditional workflows Scale: Batch process many videos at once Quality: Natural voices, accurate translations, seamless lip-sync Who it's for: Content creators, educators, businesses, and film teams who need to share video content across multiple languages without a big localization budget. I’d love feedback from the HN community—especially around what features would make this more useful in your workflows. Demo here: https://www.youtube.com/watch?v=gzPhtWFHYH8

Visit product View on Hacker NewsAug 20, 2025

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

73%73% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, para · Missing: mobile apps, ios, personal

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, 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

21%21% 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

17%17% 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.

Correct prediction on native model

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