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Compressor – native Android video compression using Media3

Hacker News

Compressor – native Android video compression using Media3

Hey Hacker News! Most video compression apps on Android are slow, ad-heavy, and rely on software-only encoding. I wanted something fast, native, and minimal, so I built my own: Compressor. Compressor is FAST, like really fast, that's one of the main selling points of it. Unlike many apps that rely on software-only ffmpeg-based pipelines, Compressor uses native Media3 transformers for hardware-accelerated video compression. Other apps are also filled with ads and trackers, Compressor has none. It doesn't have any manifest permissions (not even storage!), so there's no way to load ads or send data anywhere. And it's native Kotlin too, which makes it small, at just over 50MB installed and less than 10MB to download. It's got Material 3 UI, including the icon, and it works on older devices too, as long as it runs Android 7 or later. In my testing on a Pixel 8 Pro, Compressor was ~117x faster than the top Play Store result (Panda Video Compressor) when compressing a 200MB 4K video using comparable medium presets: ~11 seconds vs ~21 minutes There's also a HEAPS of quality of life features added that make it even better, size presets for compressing videos to fit in the small requirements of platforms like Discord, ability to adjust the resolution and frame rate of the end video, H.264/H.265 selector, ability to share videos to Compressor to be compressed and out of Compressor to other apps so you never have to manually open it, It's fully open source, currently available for download from both GitHub releases and the IzzyOnDroid F-Droid repository (with reproducible builds!) Since this relies on hardware encoding, I’d be interested to hear how well it works (or doesn’t) on different devices and chipsets. GitHub: https://github.com/JoshAtticus/Compressor

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
86%86% 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: apps, new, using · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, video, way · Missing: mobile apps, ios, personal
60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, hacker news, ide · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
49%49% 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
25%25% 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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