An open source reimplementation of the classic Linode Manager

Hacker News

On January 31 Linode shuts down its classic Linode Manager interface in favor of its new Cloud Manager. Many of us who are long-time users of Linode are disappointed about this change, so we started a project to reimplement the classic Linode manager as an open source project, backed by a crowdfunding campaign: https://www.patreon.com/linodeclassic It is written from scratch in Go, using the Linode API, with modern markup and styling, and is distributed as a native binary with no external dependencies. It can be be run on either a remote or local machine, no server, runtime, or library needs to be installed. The goal is to be as faithful to the design of the original as possible. There’s an online demo with test data available at http://li1842-248.members.linode.com:8080. You can also download it for Linux (http://li1842-248.members.linode.com/demo/linux/lmc), macOS (http://li1842-248.members.linode.com/demo/macos/lmc), and Windows (http://li1842-248.members.linode.com/demo/windows/lmc.exe), and run it locally with real data: $ ./lmc [username] [email] [access_token] For now, the username and email are only for display purposes. The access token needs to be issued with at least read-only “Linodes” and “Account” access. We are very grateful to anyone who decides to back the project. Also, we asked Linode if they would post about it on their blog and social media, but while they were very encouraging in their response, they decided not to, which makes it very hard for us to reach those who would be interested in this project, so any help in sharing it, or any idea how to proceed, is appreciated. The project is at https://github.com/linodeclassic/lmc on GitHub and at https://twitter.com/linodeclassic on Twitter.

Visit product View on Hacker NewsJan 12, 2020

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

83%83% 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.

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

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

70%70% 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 · Strong signals: interface, users · 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 · Strong signals: users · Missing: mobile apps, ios, personal

41%41% 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

13%13% 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

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

Incorrect prediction on native model

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