`rmx` release v0.1.5 – added GNU shred feature

Hacker News

New release of your favorite (and fastest) GNU rm port https://github.com/demfabris/rmx - Added shred feature - More alternatives for bench comparison This feature performs a non deterministit set of operations including writing random bytes to the file, renaming it, creating new files (hopefully close to it disk wise speaking). For those interested in the code I appreciate if you can review and audit the effectiveness. https://github.com/demfabris/rmx/blob/master/src/shred.rs

Visit product View on Hacker NewsOct 15, 2022

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

66%66% 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.

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

49%49% 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

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

Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released

43%43% 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 HackersIH features products with proven revenue · Strong signals: including · Missing: supports, reddit linkedin, podcasting

37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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