MediaMolder – A Modern Rewrite of FFmpeg

Hacker News

MediaMolder is a ground-up rewrite of FFmpeg's interface and orchestration layer, built directly on the same libav* media libraries. Written in Go, it includes a React GUI that runs in your web browser. Import, view, validate, run and monitor your FFmpeg jobs (command lines, which are really graphs). If validation fails, MediaMolder will suggest and can implement a fix. You will see per-node performance statistics as your job runs. There is an initial implementation of a real-time mode, which monitors and dynamically optimizes your running graph, in order to achieve and maintain real-time frame rates. When your graph is perfected, hit the “-> FFmpeg” button to see the equivalent FFmpeg command line.

Visit product View on Hacker NewsMay 19, 2026

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

77%77% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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