Ez FFmpeg – Video editing in plain English

Hacker News

I built a CLI tool that lets you do common video/audio operations without remembering ffmpeg syntax. Instead of: ffmpeg -i video.mp4 -vf "fps=15,scale=480:-1:flags=lanczos" -loop 0 output.gif You write: ff convert video.mp4 to gif More examples: ff compress video.mp4 to 10mb ff trim video.mp4 from 0:30 to 1:00 ff extract audio from video.mp4 ff resize video.mp4 to 720p ff speed up video.mp4 by 2x ff reverse video.mp4 There are similar tools that use LLMs (wtffmpeg, llmpeg, ai-ffmpeg-cli), but they require API keys, cost money, and have latency. Ez FFmpeg is different: - No AI – just regex pattern matching - Instant – no API calls - Free – no tokens - Offline – works without internet It handles ~20 common operations that cover 90% of what developers actually do with ffmpeg. For edge cases, you still need ffmpeg directly. Interactive mode (just type ff) shows media files in your current folder with typeahead search. npm install -g ezff

Visit product View on Hacker NewsDec 27, 2025

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

72%72% 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

66%66% predicted probability of success on Indie Hackers, 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

47%47% 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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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