Get – get anything from your computer

Hacker News

Hey everyone! I recently wrote a small binary tool that calls an LLM to execute commands, designed to get various information on your computer (for when you're too lazy to remember the commands :-/) e.g. get "system info" get "device ip address" get "README.md in ~/Coding" get "Latest version of get in https://github.com/Water-Run/get/releases/tag/v1.0 " ... It also implements simple caching and other mechanisms. Please check the GitHub homepage for detailed usage. Hope it helps everyone! :)

Visit product View on Hacker NewsApr 26, 2026

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

82%82% 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 · Strong signals: calls · Missing: plus, platform, intuitive

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

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

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

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

37%37% 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 · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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