Rhiza – easily create shortcuts and add entries to PATH

Hacker News

Rhiza is a Windows-only tool that makes any app easily launchable from both the command line and the Windows Start Menu. It works by creating shortcuts and adding entries to the PATH. Key Features: * Crawl ~ common directories to detect apps and games automatically * Add ~ any app by searching for it across the entire file system * Path ~ search for an executable and add its directory into PATH Rhiza simplifies app launching / calling tools by finding and managing them for you.

Visit product View on Hacker NewsFeb 21, 2025

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2points

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Launch Intel predictions

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

67%67% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

37%37% 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

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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