Material Files – Open Source Material Design File Manager for Android

Hacker News

Features: - Open source: Lightweight, clean and secure. - Material Design: Follows Material Design guidelines, with attention into details. - Breadcrumbs: Navigate in the filesystem with ease. - Root support: View and manage files with root access. - Archive support: View, extract and create common compressed files. - NAS support: View and manage files on FTP, SFTP and SMB servers. - Themes: Customizable UI colors, plus night mode with optional true black. - Linux-aware: Knows symbolic links, file permissions and SELinux context. - Robust: Uses Linux system calls under the hood, not yet another ls parser. - Well-implemented: Built upon the right things, including Java NIO2 File API and LiveData.

Visit product View on Hacker NewsJan 14, 2024

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Actual performance

163points

71comments

Made the leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

75%75% 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: context, open · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% 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: plus, calls · Missing: platform, intuitive, reviews

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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