DrinkMe – A Minimalist Video Compressor

Hacker News

I recently built my first ElectronJs app, a minimalist video compressor called DrinkMe. It uses a two-pass FFmpeg encode and estimates the final size based on the original video’s duration, resolution, quality, and size. The idea behind it is to provide an alternative for non-power users who struggle/don't want to learn how to use CLI-based programs like FFmpeg, or even Handbrake (whose myriad of settings and options CAN be overwhelming). The target reduction is usually around 80–85% of the original for high-res files, but it adjusts dynamically if the video’s already compressed. All feedback is welcome!

Visit product View on Hacker NewsJul 23, 2025

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

62%62% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, users · 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: users · Missing: plus, platform, intuitive

44%44% 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

38%38% 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 · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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