A browser-based video editor that renders videos directly with FFmpeg

Hacker News

Weave is a React app that provides a multi-track timeline editor to perform basic video edits like trimming, stitching, transitions, audio tracks etc. which maps directly to an FFmpeg command to render the video. I tried my best to have the React "video" preview closely replicate the FFmpeg lavfi filtergraph output, but naturally this is not perfect (especially replicating the `eq` filter using SVG filters is quite inaccurate). I've built this as a prototype for another project I'm working on, so I don't plan to actively maintain it, but I thought it'd be cool to share it. Try it live: https://weave.salviano.xyz/

Visit product View on Hacker NewsJul 26, 2026

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

75%75% 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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

51%51% 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

45%45% 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: active · 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: audio · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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