I built a browser-based video editor for making social media reels

Hacker News

Hey HN! I built Reelleer - a video editor that runs entirely in your browser to create social media reels. No uploads, no server processing, everything happens client-side. Try it: https://reelleer.com What it does: Multi-track timeline editor (video, audio, images, GIFs, text) Real-time preview at 30fps Direct canvas manipulation (drag/resize elements) Animations and transitions Export to WebM Current limitations: Real-time export only (working on WebCodecs for 10x faster) WebM output only (MP4 coming soon) Chrome/Edge work best Coming soon: Undo/redo Cloud storage for projects MP4 export Filters

Visit product View on Hacker NewsOct 22, 2025

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2points

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

65%65% 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: code · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% 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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

26%26% 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.

Correct prediction on native model

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