Plugins for file manager nnn

Hacker News

To extend the capabilities of file manager nnn ( https://github.com/jarun/nnn ), plugins are introduced. Plugins are scripts with which nnn can communicate and trigger. This mechanism fits perfectly with the fundamental design to keep the core file manager lean and fast, by delegating repetitive (but not necessarily filemanager-specific) tasks to the plugins. Plugins can access: - all files in the directory - the currently highlighted file - the current selection The plugins repo is here: https://github.com/jarun/nnn/tree/master/plugins Please contribute to the nnn plugins to make it a better file manager.

Visit product View on Hacker NewsApr 19, 2019

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

73%73% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · 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 · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: tasks · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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