MLT2FCP, a Kdenlive – Davinci Resolve Converter

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TLDR: Quick and dirty MLT (Kdenlive / Shotcut, etc) to FCPXML (Final Cut Pro, Davinci Resolve) converter: https://github.com/ggambetta/mlt2fcp The topic of open source video editors has come up a couple of times recently on HN [0] [1]. As I've commented in these threads, I used to be a big fan of Kdenlive, but after trying Davinci Resolve [2] (free as in beer), I don't see myself going back to any of the non-professional editors. I've recently finished editing a couple of feature films [3] [4] using Resolve, so I went back to an unfinished project I had on Kdenlive. Turns out there's no way to export from Kdenlive to anything Resolve can import, so I wrote a quick and dirty importer over the weekend. I realise it's pretty niche, but I've decided to release it as open source (GPLv3) anyway: https://github.com/ggambetta/mlt2fcp [0] https://news.ycombinator.com/item?id=19725823 [1] https://news.ycombinator.com/item?id=19337689 [2] https://www.blackmagicdesign.com/products/davinciresolve/ [3] https://www.imdb.com/title/tt10204712/ [4] https://www.imdb.com/title/tt10232800/

Visit product View on Hacker NewsApr 30, 2019

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

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

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using, open · Missing: mac, agents, macos

32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

11%11% 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

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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