Terraform Provider for Inexpensive Switches

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Hi HN, I’ve been building this provider for (web managed) network switches manufactured by HRUI. These switches often used in SMBs, home labs, and by budget-conscious enthusiasts. Many HRUI switches are also rebranded and sold under various OEM/ODM names (eg. Horaco, XikeStor, keepLiNK, Sodola, etc) making them accessible/popular but often overlooked in the world of infrastructure automation. The provider is in pre-release, and I’m looking for owners of these switches to test it and share feedback. My goal is to make it easier to automate its config using Terraform/OpenTofu :) You can use this provider to configure VLANs, port settings, trunk/link aggregation etc. I built this provider to address the lack of automation tools for budget-friendly hardware. It leverage goquery and has an internal SDK sitting between the Terraform resources and the switch Web UI. If you have one of these switches, I’d love for you to give it a try and let me know how it works for you! Terraform Registry: https://registry.terraform.io/providers/brennoo/hrui OpenTofu Provider: https://search.opentofu.org/provider/brennoo/hrui I’m happy to answer any questions about the provider or the hardware it supports. Feedback, bug reports, and ideas for improvement are more than welcome!

Visit product View on Hacker NewsJan 18, 2025

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

94%94% 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: using, open · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

11%11% 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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