Twick – React Based Video Editor Library

Hacker News

I’m building a React-based video editing SDK for apps that generate or enhance video content — especially AI-generated ones like explainers, reels, and product showcases. This SDK solves that by embedding a full editing layer in your frontend. What’s included: Timeline editor (trim, split, sync media) Live canvas editing Styled, AI-generated captions Animations & effects Media utils (resize, extract, compress) AWS Lambda export & S3 support Ideal for AI video platforms, social automation tools, and product video builders. Would love to hear your thoughts, feedback, or feature suggestions.

Visit product View on Hacker NewsMay 26, 2025

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

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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

37%37% 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

22%22% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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