Violet – TUI front end for Vagrant

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I'm pleased to introduce Violet, a TUI program written in Go to act as a thin front end to Vagrant[1]. Vagrant is a popular tool used to bring Virtual Machines (VMs) and Docker containers up and down. Violet will give you a global view of the Vagrant environments you have installed and allow you to perform basic Vagrant commands on them, like `up` and `ssh`. This idea came about because I've seen developers (that struggle with command line and virtualization workflows in general) struggle with setting up and using Vagrant projects. A small UI in front of the Vagrant data could help users in the beginning with the most common tasks. The project seems to be in a stable place so I wanted to share it. I have a mediocre grasp of Go and even worse understanding of UI/UX, so if you have opinions, please share! [1]: https://developer.hashicorp.com/vagrant/intro

Visit product View on Hacker NewsJul 9, 2023

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

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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 · 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: users · Missing: mobile apps, ios, personal

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

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

28%28% 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

9%9% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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